Comprehensive Analysis of Multi-Omics Data Using elastic net based on priorities
Laila Qadir Musib, Helena Mouriño and Eunice Carrasquinha
2025-01-10
Introduction
The integration of high-dimensional omics data has emerged as a critical area of interest in the healthcare sector, presenting substantial potential to improve predictive modeling. Nevertheless, this process faces several challenges, including managing data heterogeneity, establishing a priority order for utilizing predictive information from multiple data blocks, assessing the transfer of information between various omics layers, and tackling multicollinearity issues.
The priorityelasticnet package is specifically designed
to address these challenges by extending the elastic net method to
accommodate grouped predictors in high-dimensional settings. Building on
the foundation of the prioritylasso package,
priorityelasticnet enhances its functionality by
integrating the elastic net penalty, which combines \(L_1\) (lasso) and \(L_2\) (ridge) regularization. This
combination is well-known for effectively handling multicollinearity and
performing variable selection. By incorporating block-wise penalization,
this package allows for more nuanced regularization strategies, where
different groups of predictors can be penalized differently, depending
on their importance or prior knowledge.The penalized regression models
are computed via the R package glmnet. Moreover, the R
package survival is used when the outcome consists of
survival data.
One of the features of the priorityelasticnet package is
its integration of the adaptive-elastic net, which enhances its
flexibility and performance. By setting adaptive = TRUE,
this feature builds on the standard elastic net by incorporating
data-driven adaptive weights, enabling differential penalization of
predictors based on their importance. This approach improves the model’s
ability to identify relevant predictors, especially in high-dimensional
datasets where strong signals may coexist with a large number of noise
variables.
Users can leverage penalties for various family types, including binomial,multinomial, Gaussian, and Cox, to effectively handle correlated predictors and sparse solutions. In addition, for binary classification problems, the package offers an interactive Shiny application that allows users to explore model performance in a dynamic and user-friendly environment. Users can adjust classification thresholds in real time and evaluate key performance metrics such as sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve.
The priorityelasticnet package is an ideal tool for
statisticians, bioinformaticians, and data scientists working with
complex, high-dimensional datasets where the relationships between
predictors are structured and important. Whether working on predictive
modeling in genomics, image analysis, or finance, this package provides
the advanced tools needed to build accurate, interpretable models in
challenging data scenarios.
This vignette serves as a comprehensive guide to the
priorityelasticnet package. It walks you through its main
function, priorityelasticnet, as well as several other utility
functions that enhance its functionality. You will learn how to fit
models with grouped predictors using the elastic net method, handle
missing data in various ways, perform cross-validation to select the
best model parameters, extract and interpret model coefficients, make
predictions on new data, and use the Shiny app for interactive model
evaluation. By the end of this vignette, you will have a solid
understanding of how to apply priorityelasticnet to your
own data, leveraging its powerful features to tackle even the most
challenging high-dimensional problems.
Overview
To get started, you need to install the
priorityelasticnet package. If you have the package source,
you can install it as follows:
install.packages("priorityelasticnet")Key Features
Flexible Model Families: The
priorityelasticnet package supports a wide range of
regression models, making it highly versatile for various types of data
and analytical goals. Specifically, it accommodates Gaussian, binomial,
Cox, and multinomial regression models. This flexibility allows users to
apply the package in different contexts:
Gaussian Regression: Ideal for continuous outcomes, the Gaussian model can be used for traditional linear regression, where the goal is to predict a continuous variable based on a set of predictors. With the integration of Priority-elastic net regularization, this approach extends beyond standard elastic net by incorporating block-wise penalization and adaptive weights.
Binomial Regression: Used for binary classification problems, such as predicting whether a patient has a disease (yes/no) or whether a customer will make a purchase (yes/no). The binomial family allows the priorityelasticnet function to handle these types of outcomes effectively, applying regularization to manage high-dimensional data and improve the model’s predictive accuracy.
Cox Regression: The Cox proportional hazards model is widely used in survival analysis, where the focus is on time-to-event data. This model family is crucial for analyzing the impact of various predictors on the time until an event of interest occurs, such as time to death or time to relapse in medical studies. By incorporating elastic net regularization, priorityelasticnet function enhances the model’s ability to deal with a large number of predictors while maintaining interpretability.
Multinomial Regression: When dealing with outcomes
that have more than two categories, such as predicting the type of
cancer (e.g., lung, breast, prostate) or when the focus is on refining
the diagnosis by identifying specific subtypes within a single cancer
type, the multinomial regression model is essential. This model family
allows for the simultaneous prediction of multiple classes, making
priorityelasticnet suitable for multi-class classification
problems in high-dimensional settings.
Block-wise Penalization: One of the standout
features of priorityelasticnet is its ability to apply
penalties differently across groups or blocks of predictorss, a
functionality also present in the prioritylasso package.
This block-wise penalization enables users to tailor their modeling
strategies to the specific structure of their data.
Customized Regularization: In scenarios where certain groups of predictors are believed to be more relevant or should be preserved in the model, users can apply a lighter penalty or even no penalty at all to these blocks. Conversely, less important blocks can be heavily penalized to shrink their coefficients towards zero, effectively performing variable selection within those groups.
Advanced Missing Data Handling: Real-world datasets
are often plagued by missing values, which can significantly complicate
the modeling process. The priorityelasticnet package offers
a range of options to handle missing data, ensuring that the model
remains robust and accurate:
Ignoring Missing Data: For users who prefer a straightforward approach, the package allows for the exclusion of observations with missing data from the analysis. This method is simple but can lead to a loss of valuable information, especially in cases where missing data is extensive.
Imputing Missing Data: For a more sophisticated
approach, priorityelasticnet can impute missing values
using offset models. This involves predicting the missing values based
on the observed data and incorporating these predictions into the model.
This method helps retain as much data as possible while still addressing
the issue of missingness.
Adjusting for Missing Data: The package also provides the flexibility to adjust the model based on the presence of missing data. For instance, certain blocks of predictors may have systematic missingness, and the model can be adjusted to account for this, reducing potential bias and improving the model’s performance.
Cross-Validation: Cross-validation is a critical
component of modern statistical modeling, providing a means to evaluate
model performance and select the best model parameters.
priorityelasticnet implements robust cross-validation
techniques, allowing users to:
Model Performance Evaluation: By dividing the data into multiple folds and training the model on different subsets, cross-validation helps assess how well the model generalizes to new, unseen data. This process helps prevent overfitting and ensures that the model performs well not just on the training data but also on future datasets.
Parameter Selection: Cross-validation is also used
to select the optimal values for key model parameters, such as the
regularization strength (lambda) and the mixing parameter between \(L_1\) and \(L_2\) penalties (alpha). By systematically
testing different parameter values, priorityelasticnet
identifies the configuration that minimizes prediction error, leading to
a more accurate and reliable model.
Cross-Validated Offset: logical, whether CV should be used to estimate the offsets. Default is FALSE.
Adaptive Regularization: The adaptive argument in
priorityelasticnet introduces an advanced layer of
flexibility by enabling the adaptive elastic net, which enhances the
standard elastic net through the use of data-driven adaptive weights.
These weights allow the penalization strength to vary across predictors
based on their importance, with more influential predictors receiving
lighter penalties and less significant predictors penalized more
heavily.
Additionally, the initial_global_weight option provides further customization by allowing users to apply a global weight across all predictors before fitting the adaptive elastic net.
Interactive Threshold Optimization: For binary
classification models, priorityelasticnet includes a unique
feature that sets it apart: a Shiny application for interactive
threshold optimization. This tool provides an intuitive interface for
users to:
Adjust Thresholds: Users can interactively adjust the classification threshold, which determines the cut-off point at which observations are classified into different categories. This is particularly useful in scenarios where the cost of false positives and false negatives needs to be carefully balanced.
Model Evaluation: The Shiny app allows users to see how changes in the threshold affect key performance metrics such as sensitivity (true positive rate) and specificity (true negative rate). This real-time feedback helps users find the optimal threshold that maximizes the model’s predictive accuracy while minimizing errors.
Visualize Performance: The app also provides visual tools, such as ROC curves and confusion matrices, to help users better understand the trade-offs associated with different thresholds. By visualizing these metrics, users can make informed decisions about the most appropriate threshold for their specific application.
Priority-Elastic Net
Example 1: Gaussian Family with Simulated Data
Let’s begin by exploring the core functionality of the
priorityelasticnet package through a straightforward
example involving simulated Gaussian data. This example is particularly
useful for those new to the package, as it illustrates how to set up and
fit an priority-elastic net model with predictors that are logically
grouped into blocks. The Gaussian family is the default model used for
continuous outcomes, making it an ideal starting point for understanding
the basic mechanics of the package.
In many real-world scenarios, predictors can be naturally grouped based on some underlying relationship or structure. For example, in a study involving different types of measurements (like blood pressure, cholesterol levels, and BMI), these measurements might be grouped into blocks representing different biological systems or health indicators. Grouping predictors allows for more tailored regularization strategies, which can improve the interpretability and performance of the model.
Step 1: Data Simulation
First, we need to simulate a dataset that will serve as the basis for our model. We’ll generate a matrix \(\mathbf{X}\) of predictors and a response vector \(\mathbf{Y}\). In this example, \(\mathbf{X}\) will be a matrix with 100 rows (observations) and 50 columns (predictors). The response vector \(\mathbf{Y}\) will be generated from a linear model with some added noise, making it a continuous variable suitable for Gaussian regression.
# Simulate some data
set.seed(123)
n <- 100 # Number of observations
p <- 50 # Number of predictors# Create a matrix of predictors
X <- matrix(rnorm(n * p), n, p)# Generate a response vector based on a linear combination of some predictors
beta <- rnorm(10) # Coefficients for the first 10 predictors
Y <- X[, 1:10] %*% beta + rnorm(n) # Linear model with added noiseIn the above code:
set.seed(123) ensures reproducibility by setting the random number generator’s seed. \(\mathbf{X}\) is a matrix of normally distributed random variables, representing the predictors. \(\mathbf{Y}\) is created as a linear combination of the first 10 predictors in \(\mathbf{X}\), with some added Gaussian noise to simulate realistic data.
Step 2: Defining Predictor Blocks
Next, we’ll define how the predictors in \(\mathbf{X}\) are grouped into blocks. These blocks can represent different logical groupings of the predictors, which may correspond to different sources of data or different types of variables.
# Define predictor blocks
blocks <- list(
block1 = 1:10, # First block includes the first 10 predictors
block2 = 11:30, # Second block includes the next 20 predictors
block3 = 31:50 # Third block includes the last 20 predictors
)Here, the blocks list divides the 50 predictors into three distinct groups:
block1 contains the first 10 predictors, which directly influence the response \(\mathbf{Y}\). block2 and block3 contain the remaining predictors, which might be noise or represent other variables in a real-world scenario.
Step 3: Fitting the Priority-Elastic Net Model
With the data and blocks defined, we can now fit an priority-elastic net model using the priorityelasticnet function. The function will apply regularization to the predictors within each block, allowing for block-specific penalization.
# Fit a priorityelasticnet model
fit <- priorityelasticnet(
X = X,
Y = Y,
family = "gaussian",
blocks = blocks,
type.measure = "mse",
alpha = 0.5
)In the above code:
\(\mathbf{X}\) and \(\mathbf{Y}\) are the data we simulated earlier. family = “gaussian” specifies that we are using a Gaussian (linear regression) model, appropriate for continuous outcomes. blocks defines the grouping of predictors. \(\alpha=0.5\) sets the elastic net mixing parameter, combining both lasso (\(L_1\)) and ridge (\(L_2\)) penalties. The elastic net regularization is controlled by the \(\alpha\) parameter, which determines the balance between lasso and ridge penalties:
When \(\alpha=1\), the model is purely Priority-lasso, focusing on variable selection by shrinking some coefficients exactly to zero. When \(\alpha=0\), the model is purely ridge, shrinking coefficients towards zero but not exactly to zero, making it better for multicollinear predictors. \(\alpha=0.5\) provides a balance between these two extremes, often yielding a model that performs well in practice by combining the benefits of both regularization techniques.
Step 4: Interpreting the Results
After fitting the model, it’s essential to examine the results to understand which predictors were selected and how they contribute to the response variable.
Lambda Selection
The selected lambda indices for the models, for each bock, are 64, 8, and 1.
fit$lambda.ind
#> [[1]]
#> [1] 64
#>
#> [[2]]
#> [1] 8
#>
#> [[3]]
#> [1] 1Lambda type used is “lambda.min”.
fit$lambda.type
#> [1] "lambda.min"The lambda values corresponding to lambda.min are approximately 0.0093, 0.1980, and 0.2415.
fit$lambda.min
#> [[1]]
#> [1] 0.009298608
#>
#> [[2]]
#> [1] 0.1980352
#>
#> [[3]]
#> [1] 0.241513Cross-Validation Results
The minimum cross-validated mean squared errors (min.cvm) for the models are approximately 0.914, 0.720, and 0.710.
fit$min.cvm
#> [[1]]
#> [1] 0.9139551
#>
#> [[2]]
#> [1] 0.7197412
#>
#> [[3]]
#> [1] 0.7101246Number of Non-Zero Coefficients:
The number of non-zero coefficients in the models are 10, 2, and 0.
fit$nzero
#> [[1]]
#> [1] 10
#>
#> [[2]]
#> [1] 2
#>
#> [[3]]
#> [1] 0GLMNET Model Fits:
The glmnet models show a sequence of deviance reductions
and corresponding lambda values for each block, detailing how the model
complexity increases with more non-zero coefficients as lambda
decreases.
fit$glmnet.fit
#> [[1]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 3.2650
#> 2 1 3.85 2.9750
#> 3 1 7.37 2.7110
#> 4 1 10.57 2.4700
#> 5 2 14.93 2.2500
#> 6 2 19.50 2.0500
#> 7 4 24.99 1.8680
#> 8 4 31.93 1.7020
#> 9 4 38.09 1.5510
#> 10 5 43.82 1.4130
#> 11 5 49.51 1.2880
#> 12 5 54.50 1.1730
#> 13 6 58.90 1.0690
#> 14 6 62.92 0.9741
#> 15 6 66.40 0.8876
#> 16 7 69.41 0.8087
#> 17 7 72.19 0.7369
#> 18 8 74.73 0.6714
#> 19 8 76.92 0.6118
#> 20 8 78.79 0.5574
#> 21 9 80.58 0.5079
#> 22 9 82.12 0.4628
#> 23 10 83.45 0.4217
#> 24 10 84.63 0.3842
#> 25 10 85.63 0.3501
#> 26 10 86.47 0.3190
#> 27 10 87.17 0.2906
#> 28 10 87.77 0.2648
#> 29 10 88.27 0.2413
#> 30 10 88.69 0.2199
#> 31 10 89.04 0.2003
#> 32 10 89.33 0.1825
#> 33 10 89.58 0.1663
#> 34 10 89.79 0.1515
#> 35 10 89.96 0.1381
#> 36 10 90.10 0.1258
#> 37 10 90.22 0.1146
#> 38 10 90.32 0.1045
#> 39 10 90.41 0.0952
#> 40 10 90.48 0.0867
#> 41 10 90.54 0.0790
#> 42 10 90.58 0.0720
#> 43 10 90.62 0.0656
#> 44 10 90.66 0.0598
#> 45 10 90.69 0.0545
#> 46 10 90.71 0.0496
#> 47 10 90.73 0.0452
#> 48 10 90.75 0.0412
#> 49 10 90.76 0.0375
#> 50 10 90.77 0.0342
#> 51 10 90.78 0.0312
#> 52 10 90.79 0.0284
#> 53 10 90.79 0.0259
#> 54 10 90.80 0.0236
#> 55 10 90.80 0.0215
#> 56 10 90.81 0.0196
#> 57 10 90.81 0.0178
#> 58 10 90.81 0.0163
#> 59 10 90.81 0.0148
#> 60 10 90.82 0.0135
#> 61 10 90.82 0.0123
#> 62 10 90.82 0.0112
#> 63 10 90.82 0.0102
#> 64 10 90.82 0.0093
#>
#> [[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.37980
#> 2 2 1.04 0.34610
#> 3 2 2.17 0.31530
#> 4 2 3.14 0.28730
#> 5 2 3.98 0.26180
#> 6 2 4.70 0.23850
#> 7 2 5.32 0.21730
#> 8 2 5.85 0.19800
#> 9 4 6.65 0.18040
#> 10 4 7.41 0.16440
#> 11 4 8.06 0.14980
#> 12 5 8.67 0.13650
#> 13 7 9.28 0.12440
#> 14 7 9.88 0.11330
#> 15 7 10.39 0.10330
#> 16 7 10.82 0.09408
#> 17 9 11.36 0.08572
#> 18 9 11.83 0.07811
#> 19 10 12.27 0.07117
#> 20 13 12.80 0.06485
#> 21 16 13.30 0.05909
#> 22 16 13.76 0.05384
#> 23 16 14.15 0.04905
#> 24 16 14.48 0.04470
#> 25 17 14.76 0.04073
#> 26 18 15.03 0.03711
#> 27 18 15.26 0.03381
#> 28 18 15.45 0.03081
#> 29 18 15.61 0.02807
#> 30 18 15.75 0.02558
#> 31 19 15.86 0.02331
#> 32 19 15.95 0.02123
#> 33 19 16.03 0.01935
#> 34 19 16.10 0.01763
#> 35 19 16.15 0.01606
#> 36 19 16.20 0.01464
#> 37 19 16.24 0.01334
#> 38 19 16.27 0.01215
#> 39 19 16.29 0.01107
#> 40 19 16.32 0.01009
#> 41 19 16.34 0.00919
#> 42 19 16.35 0.00838
#> 43 19 16.36 0.00763
#> 44 19 16.37 0.00695
#> 45 19 16.38 0.00634
#> 46 19 16.39 0.00577
#> 47 19 16.40 0.00526
#> 48 19 16.40 0.00479
#> 49 19 16.41 0.00437
#> 50 19 16.41 0.00398
#> 51 20 16.41 0.00363
#> 52 20 16.41 0.00330
#> 53 20 16.42 0.00301
#> 54 20 16.42 0.00274
#> 55 20 16.42 0.00250
#> 56 20 16.42 0.00228
#> 57 20 16.42 0.00208
#> 58 20 16.42 0.00189
#> 59 20 16.42 0.00172
#> 60 20 16.42 0.00157
#> 61 20 16.42 0.00143
#> 62 20 16.42 0.00130
#> 63 20 16.42 0.00119
#> 64 20 16.43 0.00108
#> 65 20 16.43 0.00099
#> 66 20 16.43 0.00090
#> 67 20 16.43 0.00082
#>
#> [[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.241500
#> 2 3 0.54 0.220100
#> 3 4 1.56 0.200500
#> 4 7 2.77 0.182700
#> 5 8 4.27 0.166500
#> 6 8 5.59 0.151700
#> 7 8 6.72 0.138200
#> 8 8 7.67 0.125900
#> 9 9 8.55 0.114700
#> 10 11 9.38 0.104500
#> 11 11 10.20 0.095260
#> 12 12 10.91 0.086800
#> 13 13 11.60 0.079080
#> 14 14 12.25 0.072060
#> 15 14 12.81 0.065660
#> 16 14 13.29 0.059820
#> 17 14 13.69 0.054510
#> 18 14 14.02 0.049670
#> 19 15 14.34 0.045260
#> 20 16 14.63 0.041230
#> 21 16 14.89 0.037570
#> 22 16 15.10 0.034230
#> 23 16 15.28 0.031190
#> 24 17 15.44 0.028420
#> 25 17 15.58 0.025900
#> 26 18 15.69 0.023600
#> 27 18 15.79 0.021500
#> 28 18 15.88 0.019590
#> 29 18 15.95 0.017850
#> 30 18 16.01 0.016260
#> 31 19 16.06 0.014820
#> 32 19 16.11 0.013500
#> 33 19 16.15 0.012300
#> 34 19 16.18 0.011210
#> 35 19 16.21 0.010210
#> 36 19 16.23 0.009307
#> 37 19 16.25 0.008480
#> 38 19 16.26 0.007727
#> 39 19 16.28 0.007040
#> 40 19 16.29 0.006415
#> 41 19 16.30 0.005845
#> 42 20 16.30 0.005326
#> 43 20 16.31 0.004853
#> 44 20 16.32 0.004421
#> 45 20 16.32 0.004029
#> 46 20 16.32 0.003671
#> 47 20 16.33 0.003345
#> 48 20 16.33 0.003048
#> 49 20 16.33 0.002777
#> 50 20 16.33 0.002530
#> 51 20 16.34 0.002305
#> 52 20 16.34 0.002101
#> 53 20 16.34 0.001914
#> 54 20 16.34 0.001744
#> 55 20 16.34 0.001589
#> 56 20 16.34 0.001448
#> 57 20 16.34 0.001319
#> 58 20 16.34 0.001202
#> 59 20 16.34 0.001095
#> 60 20 16.34 0.000998
#> 61 20 16.34 0.000909
#> 62 20 16.34 0.000829
#> 63 20 16.34 0.000755Coefficients:
The coefficients for the variables in the model are listed, with many variables having zero coefficients, indicating that they were not selected by the model.
fit$coefficients
#> V1 V2 V3 V4 V5 V6
#> -0.38237226 1.19498601 -1.00638497 1.54134581 1.01194132 0.30777552
#> V7 V8 V9 V10 V1 V2
#> 0.66521016 0.19710735 -0.35853947 -0.33364777 0.00000000 0.00000000
#> V3 V4 V5 V6 V7 V8
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V9 V10 V11 V12 V13 V14
#> 0.00000000 -0.06897620 0.00000000 0.00000000 -0.07376982 0.00000000
#> V15 V16 V17 V18 V19 V20
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V1 V2 V3 V4 V5 V6
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V7 V8 V9 V10 V11 V12
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V13 V14 V15 V16 V17 V18
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V19 V20
#> 0.00000000 0.00000000Predictions and Actual Values:
The pred matrix contains predicted values, and the observed matrix contains the actual observed values.
head(cbind.data.frame(pred = fit$pred[,1], observed = fit$actuals))
#> pred observed
#> 1 -3.665361 -4.3201992
#> 2 -3.293562 -2.1450501
#> 3 -2.898844 -2.4177882
#> 4 -4.495482 -4.4175057
#> 5 -1.468117 -2.6590505
#> 6 -1.021279 -0.9366846This example provides a clear introduction to the
priorityelasticnet package’s capabilities in handling
high-dimensional data with grouped predictors. By simulating a simple
Gaussian dataset, defining predictor blocks, and fitting an elastic net
model, you have seen how to apply regularization techniques effectively.
This approach is particularly valuable in real-world scenarios where
predictors are naturally grouped, allowing for more meaningful and
interpretable models.
Example 2: Cox Family with Simulated Data
The priorityelasticnet package supports Cox proportional
hazards models, which are commonly used in survival analysis to assess
the association between the survival time of subjects and one or more
predictor variables. This example demonstrates how to use the package to
fit a Cox model with block-wise elastic net regularization, using
simulated survival data to illustrate its application.
Simulating Survival Data
To demonstrate the functionality, we begin by generating simulated survival data. In this example, we create a dataset with 50 observations (n = 50) and 300 predictors (p = 300). A portion of these predictors have nonzero coefficients, contributing to the simulated survival outcome.
# Set seed for reproducibility
set.seed(123)
# Number of observations and predictors
n <- 50 # Number of observations
p <- 300 # Number of predictors
# Number of non-zero coefficients
nzc <- trunc(p / 10)
# Simulate predictor matrix
x <- matrix(rnorm(n * p), n, p)
# Simulate regression coefficients for non-zero predictors
beta <- rnorm(nzc)
# Calculate linear predictor
fx <- x[, seq(nzc)] %*% beta / 3
# Calculate hazard function
hx <- exp(fx)
# Simulate survival times using exponential distribution
ty <- rexp(n, hx)
# Generate censoring indicator (30% censoring probability)
tcens <- rbinom(n = n, prob = .3, size = 1)
# Load survival library and create survival object
library(survival)
#> Warning: package 'survival' was built under R version 4.3.3
y <- Surv(ty, 1 - tcens)Explanation of the Code:
n and p define the number of observations and predictors, respectively.
x is a matrix of predictors drawn from a standard normal distribution.
beta represents a set of non-zero coefficients used to simulate a linear predictor, contributing to the hazard function hx.
ty is the vector of survival times, generated using an exponential distribution with rate parameter hx.
y is a survival object created using the Surv() function from the survival package, representing the survival time and censoring status.
Defining Predictor Blocks
To apply block-wise regularization, we group the predictors into three blocks. This allows the model to apply different levels of penalization to different sets of predictors, reflecting their varying levels of importance.
blocks <- list(
bp1 = 1:20, # First block with predictors 1 to 20
bp2 = 21:200, # Second block with predictors 21 to 200
bp3 = 201:300 # Third block with predictors 201 to 300
)Explanation of the Blocks:
bp1 may represent a core group of predictors with potentially greater influence on survival.
bp2 and bp3 may capture additional predictors, offering flexibility for different levels of regularization.
Fitting a Cox Model with Priority-Elastic Net
We proceed to fit the Cox model using the priorityelasticnet function, applying block-wise elastic net regularization to manage the high-dimensional data.
# Fit Cox model using priorityelasticnet
fit_cox <- priorityelasticnet(
x,
y,
family = "cox",
alpha = 0.5,
type.measure = "deviance",
blocks = blocks,
block1.penalization = TRUE,
lambda.type = "lambda.min",
standardize = TRUE,
nfolds = 10,
cvoffset = TRUE
)Key Parameters Explained:
family = “cox” specifies that we are fitting a Cox proportional hazards model for survival data.
type.measure = “deviance” sets the measure used for cross-validation, with deviance measuring model fit.
blocks defines the predictor groups for block-wise regularization.
block1.penalization = TRUE allows penalization within the first block, enabling the model to shrink coefficients within this block based on their relevance.
lambda.type = “lambda.min” uses the lambda value minimizing cross-validated deviance for model selection.
standardize = TRUE scales predictors to have zero mean and unit variance, which is crucial when predictors vary in scale.
nfolds = 10 specifies 10-fold cross-validation for model validation.
Evaluating the Model
After fitting, it’s important to evaluate the model’s performance and review the selected coefficients and lambda values.
Cross-Validated Deviance
fit_cox$min.cvm
#> [[1]]
#> [1] 7.835655
#>
#> [[2]]
#> [1] 7.867425
#>
#> [[3]]
#> [1] 7.947122This provides the minimum cross-validated deviance, indicating how well the model predicts the survival times.
Coefficients
fit_cox$coefficients
#> V1 V2 V3 V4 V5 V6
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.122478701
#> V7 V8 V9 V10 V11 V12
#> -0.084133069 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V13 V14 V15 V16 V17 V18
#> 0.000000000 0.000000000 -0.098698993 -0.002593289 0.000000000 0.000000000
#> V19 V20 V1 V2 V3 V4
#> 0.000000000 0.000000000 0.000000000 -0.195468134 0.000000000 0.000000000
#> V5 V6 V7 V8 V9 V10
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V11 V12 V13 V14 V15 V16
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V17 V18 V19 V20 V21 V22
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V23 V24 V25 V26 V27 V28
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V29 V30 V31 V32 V33 V34
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.019156809 0.000000000
#> V35 V36 V37 V38 V39 V40
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V41 V42 V43 V44 V45 V46
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V47 V48 V49 V50 V51 V52
#> 0.000000000 0.000000000 -0.127396492 0.000000000 0.000000000 0.000000000
#> V53 V54 V55 V56 V57 V58
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V59 V60 V61 V62 V63 V64
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V65 V66 V67 V68 V69 V70
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V71 V72 V73 V74 V75 V76
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V77 V78 V79 V80 V81 V82
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V83 V84 V85 V86 V87 V88
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V89 V90 V91 V92 V93 V94
#> -0.095934693 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V95 V96 V97 V98 V99 V100
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V101 V102 V103 V104 V105 V106
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V107 V108 V109 V110 V111 V112
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V113 V114 V115 V116 V117 V118
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V119 V120 V121 V122 V123 V124
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.012491962 0.000000000
#> V125 V126 V127 V128 V129 V130
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V131 V132 V133 V134 V135 V136
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V137 V138 V139 V140 V141 V142
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V143 V144 V145 V146 V147 V148
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V149 V150 V151 V152 V153 V154
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V155 V156 V157 V158 V159 V160
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V161 V162 V163 V164 V165 V166
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V167 V168 V169 V170 V171 V172
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V173 V174 V175 V176 V177 V178
#> 0.009633117 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V179 V180 V1 V2 V3 V4
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V5 V6 V7 V8 V9 V10
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V11 V12 V13 V14 V15 V16
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V17 V18 V19 V20 V21 V22
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V23 V24 V25 V26 V27 V28
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V29 V30 V31 V32 V33 V34
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V35 V36 V37 V38 V39 V40
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V41 V42 V43 V44 V45 V46
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V47 V48 V49 V50 V51 V52
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V53 V54 V55 V56 V57 V58
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V59 V60 V61 V62 V63 V64
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V65 V66 V67 V68 V69 V70
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V71 V72 V73 V74 V75 V76
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V77 V78 V79 V80 V81 V82
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V83 V84 V85 V86 V87 V88
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V89 V90 V91 V92 V93 V94
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V95 V96 V97 V98 V99 V100
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000This outputs the estimated coefficients for each predictor within each block, helping to understand their contribution to the survival outcome.
Lambda Selection
fit_cox$lambda.min
#> [[1]]
#> [1] 0.3714762
#>
#> [[2]]
#> [1] 0.4934428
#>
#> [[3]]
#> [1] 0.5620108The selected lambda value, balancing model complexity and predictive performance.
This example demonstrates the flexibility of the
priorityelasticnet package in handling survival data with
Cox proportional hazards models. By using block-wise elastic net
regularization, we can apply differential penalization to different
groups of predictors, making it a powerful approach for modeling
complex, high-dimensional survival data.
Kaplan-Meier Curve
The Kaplan-Meier curve in this example is used to visualize and
compare survival probabilities between two risk groups (High Risk and
Low Risk) identified using the priorityelasticnet Cox
proportional hazards model and using function
separate2GroupsCox from glmSparseNet package.
library(glmSparseNet)
#> Loading required package: glmnet
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# Extract coefficients from the fitted Cox model
chosen.btas <- fit_cox$coefficients
y <- data.frame(
time = ty, # Survival times
status = 1 - tcens # Event indicator
)
# Group patients and plot Kaplan-Meier survival curves
separate2GroupsCox(
chosen.btas = chosen.btas, # Coefficients from the model
xdata = x, # Predictor matrix (xdata)
ydata = y, # Survival data (ydata as Surv object)
probs = c(0.4, 0.6), # Median split (adjust if necessary)
no.plot = FALSE, # Plot the Kaplan-Meier curve
plot.title = "Survival Curves", # Plot title
xlim = NULL, # Automatic x-axis limits
ylim = NULL, # Automatic y-axis limits
expand.yzero = FALSE, # Don't force y-axis to start at zero
legend.outside = FALSE # Keep legend inside the plot
)
#> $pvalue
#> [1] 5.707024e-06
#>
#> $plot
#>
#> $km
#> Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
#>
#> n events median 0.95LCL 0.95UCL
#> Low risk 20 11 4.860 1.211 NA
#> High risk 20 18 0.255 0.188 0.811The p-value = 5.707024e-06 strongly indicates a statistically significant difference in survival between the two groups.
This plot demonstrates that the risk stratification (High Risk vs. Low Risk) is meaningful and robust.
In our model, individuals are classified into the low-risk group if their calculated relative risk is less than or equal to the median.
Conversely, the high-risk group includes individuals whose relative risk exceeds the median.
Example 3: Binomial Family with Simulated Glioma Data
In this example, we will explore how to apply the
priorityelasticnet package for binary classification.
Binary classification is a common task in many fields, such as medical
diagnosis, fraud detection, and marketing, where the goal is to classify
observations into one of two categories based on a set of
predictors.
We will use the Pen_Data dataset, which comes with the
priorityelasticnet package. Please note that
Pen_Data is not real data, but rather simulated within the
priorityelasticnet package. This dataset includes a binary
response variable along with a large number of predictors, making it an
excellent candidate for applying elastic net regularization. The
predictors are grouped into blocks, which might represent different
categories of features, such as demographic information, behavioral
data, or genetic markers.
Load the Data
First, we will load the Pen_Data dataset, which is included in the
priorityelasticnet package. This dataset has 325 columns,
where the first 324 columns are predictors and the last column is the
binary response variable.
# Check if 'priorityelasticnet' is available
if (!requireNamespace("priorityelasticnet", quietly = TRUE)) {
message("The 'priorityelasticnet' package is not installed. Please install it to fully reproduce this vignette.")
} else {
library(priorityelasticnet)
# Load the dataset only if the package is available
data("Pen_Data", package = "priorityelasticnet")
}
dim(Pen_Data)
#> [1] 406 325The Pen_Data dataset is structured as follows:
- The first 324 columns (Pen_Data[, 1:324]) represent the predictors.
- The 325th column (Pen_Data[, 325]) is the binary outcome variable, with values typically coded as 0 and 1, representing the two classes.
Define Predictor Blocks
Similar to the previous examples, we need to define how the predictors are grouped into blocks. The predictors in this dataset are divided into four blocks. These blocks could represent different types of data or features that are logically grouped together. In a real-world scenario, these blocks might correspond to different sources of data, such as clinical measurements, genetic data, or questionnaire responses.
blocks <- list(
block1 = 1:5, # Block 1: First 5 predictors
block2 = 6:179, # Block 2: Next 174 predictors
block3 = 180:324 # Block 3: Next 145 predictors
)Fit the Elastic Net Model
set.seed(123)
fit_bin <- priorityelasticnet(
X = as.matrix(Pen_Data[, 1:324]),
Y = Pen_Data[, 325],
family = "binomial",
alpha = 0.5,
type.measure = "auc",
blocks = blocks,
standardize = FALSE
)Here’s what each parameter does:
- X is the matrix of predictors from the Pen_Data dataset.
- Y is the binary response variable.
- family = “binomial” indicates that we are fitting a logistic regression model suitable for binary classification.
- alpha = 0.5 specifies a mix of lasso and ridge penalties, which helps in both variable selection and multicollinearity management.
- type.measure = “auc” selects the AUC as the metric for cross-validation, which is a robust measure of the model’s ability to discriminate between the two classes.
- blocks defines the structure of the predictors into meaningful groups, which will be penalized differently during model fitting.
- standardize = FALSE indicates that the predictors should not be standardized, which might be appropriate if the data is already on a comparable scale or if the original scale is meaningful.
Making Predictions
With the model fitted, you can now use it to make predictions on new data. This is particularly useful when you want to classify new observations or assess the model’s performance on a test set.
predictions <- predict(fit_bin, type = "response")
head(predictions)
#> [,1]
#> [1,] 0.5327832
#> [2,] 0.7639971
#> [3,] 0.3106758
#> [4,] 0.4322164
#> [5,] 0.6860752
#> [6,] 0.3602870In this step:
- type = “response” returns the predicted probabilities of each observation belonging to the positive class (e.g., the probability of having a disease).
- The predict function can be applied to new datasets, enabling you to extend the model’s application beyond the original training data.
You can also make predictions for new data using the fitted binomal model.
predictions <- predict(fit_bin, newdata = as.matrix(Pen_Data[, 1:324]), type = "response")
head(predictions)
#> [,1]
#> [1,] 0.5327832
#> [2,] 0.7639971
#> [3,] 0.3106758
#> [4,] 0.4322164
#> [5,] 0.6860752
#> [6,] 0.3602870In this example, type = “response” gives the predicted class probabilities for each observation in the new dataset X_new.
Further Analysis and Visualization
To gain deeper insights into the model, you might want to explore additional aspects, such as the importance of different blocks, the distribution of predicted probabilities, or the performance across different subsets of the data.
For example, you can visualize the ROC curve to assess the model’s discrimination ability:
library(pROC)
#> Type 'citation("pROC")' for a citation.
#>
#> Attaching package: 'pROC'
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#>
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roc_curve <- roc(Pen_Data[, 325], predictions[,1])
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
plot(roc_curve, col = "red", main = "ROC Curve for Binomial Model")
text(0.1, 0.1, labels = paste("AUC =", round(roc_curve$auc, 2)), col = "black", cex = 1.2)
This ROC curve will help you visually inspect the trade-off between sensitivity and specificity across different thresholds, providing a comprehensive view of the model’s classification performance.
This example demonstrates the power and flexibility of the
priorityelasticnet package when applied to binary
classification tasks using real data. By fitting an elastic net model
with block-wise penalization, you can efficiently handle
high-dimensional datasets with structured predictors. The ability to
customize the penalization strategy, combined with robust
cross-validation and performance metrics like AUC, ensures that you can
build accurate and interpretable models even in challenging
scenarios.
Example 4: Multinomial Family with Simulated Data
The priorityelasticnet package also supports the
multinomial family, which is particularly useful for addressing
multi-class classification problems where the response variable can take
on more than two categories. This makes it an ideal tool for
applications such as predicting categorical outcomes in fields like
image classification, text categorization, or medical diagnostics
involving multiple disease types. Below is a detailed example using
simulated data to demonstrate how to fit a multinomial model using the
priorityelasticnet package.
Simulate Some Data
To illustrate the use of the multinomial family, we first need to generate some simulated data. In this example, we create a dataset with 100 observations (n = 100), each having 50 predictors (p = 50). The response variable \(\mathbf{Y}\) will have three possible classes (k = 3), which could represent different categories or labels in a classification task.
# Set seed for reproducibility
set.seed(123)
# Number of observations and predictors
n <- 100 # Number of observations
p <- 50 # Number of predictors
k <- 3 # Number of classes
# Simulate a matrix of predictors
x <- matrix(rnorm(n * p), n, p)
# Simulate a response vector with three classes
y <- factor(sample(1:k, n, replace = TRUE))In this code:
- x is a matrix of predictors, where each element is drawn from a standard normal distribution.
- y is the response vector, a factor with three levels representing different classes. These could correspond to distinct categories such as “low”, “medium”, and “high” in a risk assessment model, or different types of diagnoses in a medical study.
Define Predictor Blocks
Next, we define how the predictors in X are grouped into blocks. This step is crucial as it allows the model to apply different levels of penalization to different groups of predictors, which might have varying levels of importance or relevance to the outcome.
blocks <- list(
block1 = 1:10, # First block with predictors 1 to 10
block2 = 11:30, # Second block with predictors 11 to 30
block3 = 31:50 # Third block with predictors 31 to 50
)In this example:
- block1 might represent a core set of features that are expected to have a significant impact on the classification outcome.
- block2 and block3 might include additional predictors, potentially capturing different dimensions or types of information related to the classes.
By grouping predictors into blocks, we can control the regularization strength applied to each group, allowing for more nuanced modeling strategies. This is particularly useful in high-dimensional settings where certain groups of predictors are expected to be more informative than others.
Fit a Model for Multinomial Classification
With the data and predictor blocks ready, we can now fit a multinomial model using the priorityelasticnet function. This function applies elastic net regularization within each block, combining the strengths of both lasso (\(L_1\)) and ridge (\(L_2\)) penalties to handle high-dimensional data effectively.
fit_multinom <- priorityelasticnet(
X = x,
Y = y,
family = "multinomial",
alpha = 0.5,
type.measure = "class",
blocks = blocks,
block1.penalization = TRUE,
lambda.type = "lambda.min",
standardize = TRUE,
nfolds = 5
)Here’s a breakdown of the key parameters:
- family = “multinomial” specifies that we are fitting a model suitable for multi-class classification, where the response variable Y has more than two levels.
- alpha = 0.5 sets the elastic net mixing parameter, providing a balance between lasso (\(L_1\)) and ridge (\(L_2\)) regularization.
- type.measure = “class” indicates that the model’s performance during cross-validation should be evaluated based on classification accuracy, which is the proportion of correctly classified observations.
- blocks defines the groups of predictors, allowing for differential penalization across these blocks.
- block1.penalization = TRUE means that the first block will be penalized, allowing the model to shrink coefficients within this block based on its relevance to the classification task.
- lambda.type = “lambda.min” specifies that the lambda value that minimizes cross-validated error should be used, which often results in a model with the best generalization performance.
- standardize = TRUE indicates that the predictors should be standardized (mean-centered and scaled to unit variance) before fitting the model. This is particularly important when the predictors have different units or scales.
- nfolds = 5 sets the number of folds for cross-validation, where the data is split into 5 parts to evaluate the model’s performance and select the optimal lambda.
Evaluate the Model
After fitting the model, it’s essential to evaluate its performance. The priorityelasticnet function will have already performed cross-validation to select the best lambda value and estimate the classification accuracy.
The summary of the fitted multinomial model will provide valuable insights, including:
- Cross-Validation Results: The mean cross-validated errors for each lambda value tested during cross-validation, helping to understand how different levels of regularization impact the model’s ability to correctly classify observations.
fit_multinom$min.cvm
#> [[1]]
#> [1] 0.59
#>
#> [[2]]
#> [1] 0.58
#>
#> [[3]]
#> [1] 0.56- Coefficients: The estimated coefficients for each predictor within each block, across the different classes. These coefficients show how each predictor contributes to the probability of each class.
fit_multinom$coefficients
#> [[1]]
#> [,1] [,2] [,3]
#> V1 0 0 0
#> V2 0 0 0
#> V3 0 0 0
#> V4 0 0 0
#> V5 0 0 0
#> V6 0 0 0
#> V7 0 0 0
#> V8 0 0 0
#> V9 0 0 0
#> V10 0 0 0
#>
#> [[2]]
#> [,1] [,2] [,3]
#> V1 0 0 0.000000000
#> V2 0 0 0.000000000
#> V3 0 0 -0.035209326
#> V4 0 0 0.014714841
#> V5 0 0 0.000000000
#> V6 0 0 0.000000000
#> V7 0 0 0.000000000
#> V8 0 0 0.000000000
#> V9 0 0 0.000000000
#> V10 0 0 0.000000000
#> V11 0 0 0.000000000
#> V12 0 0 0.006370895
#> V13 0 0 0.000000000
#> V14 0 0 0.000000000
#> V15 0 0 0.000000000
#> V16 0 0 0.000000000
#> V17 0 0 0.000000000
#> V18 0 0 0.000000000
#> V19 0 0 0.000000000
#> V20 0 0 0.000000000
#>
#> [[3]]
#> [,1] [,2] [,3]
#> V1 0.00000000 0.00000000 0.00000000
#> V2 -0.19614864 0.00000000 0.00000000
#> V3 0.00000000 0.00000000 -0.13430500
#> V4 0.00000000 0.03363490 0.00000000
#> V5 0.00000000 0.00000000 0.00000000
#> V6 -0.13707583 0.00000000 0.00000000
#> V7 0.00000000 0.02728771 0.00000000
#> V8 0.00000000 0.00000000 0.00000000
#> V9 0.03162973 0.00000000 -0.16275847
#> V10 0.00000000 0.01378721 -0.27625744
#> V11 0.00000000 0.00000000 0.00000000
#> V12 0.00000000 0.00000000 0.00000000
#> V13 0.00000000 0.00000000 0.00000000
#> V14 0.00000000 0.00000000 0.00000000
#> V15 0.00000000 0.00000000 0.24979773
#> V16 0.00000000 0.00000000 0.00000000
#> V17 0.00000000 0.00000000 0.00000000
#> V18 0.03385628 0.00000000 -0.32552979
#> V19 0.00000000 0.00000000 -0.01798884
#> V20 0.00000000 -0.25033768 0.00000000- Lambda Selection: The lambda value that was selected based on cross-validation, which balances model complexity and performance.
fit_multinom$lambda.min
#> [[1]]
#> [1] 0.1510727
#>
#> [[2]]
#> [1] 0.2485561
#>
#> [[3]]
#> [1] 0.08848742This example demonstrates the versatility of the
priorityelasticnet package in handling multi-class
classification problems using the multinomial family. By simulating a
dataset with multiple classes and fitting a multinomial elastic net
model, you can see how block-wise regularization can be applied to
complex, high-dimensional data. The model’s ability to handle multiple
classes with different levels of penalization across predictor blocks
makes it a powerful tool for a wide range of classification tasks.
Advanced Features
Block-wise Penalization
The priorityelasticnet function provides a flexible approach to block-wise penalization, enabling different regularization strategies for distinct groups of predictors. This functionality is particularly valuable when you have prior knowledge about certain predictor groups that might require unique treatment. For instance, you may have a block of predictors that are known to be highly informative or essential for the model’s predictive power and, therefore, should not be penalized. Conversely, other blocks can be regularized to manage multicollinearity, reduce model complexity, or enhance generalization.
In the example below, we demonstrate how to exclude the first block of predictors from penalization. The data used in this example, X and Y, are generated under a Gaussian model.
fit_no_penalty <-
priorityelasticnet(
X,
Y,
family = "gaussian",
type.measure = "mse",
blocks = blocks,
block1.penalization = FALSE
)Here, the block1.penalization = FALSE argument ensures that the first block of predictors is left unpenalized, while the remaining blocks undergo regularization. This approach is particularly useful in situations where the first block contains variables that are critical to the model, such as demographic information, baseline measurements, or other covariates that you want to retain in their original form without shrinkage.
After fitting the model, you can inspect the results to understand how the penalization has been applied across the different blocks:
fit_no_penalty
#> $lambda.ind
#> $lambda.ind[[1]]
#> NULL
#>
#> $lambda.ind[[2]]
#> [1] 1
#>
#> $lambda.ind[[3]]
#> [1] 1
#>
#>
#> $lambda.type
#> [1] "lambda.min"
#>
#> $lambda.min
#> $lambda.min[[1]]
#> NULL
#>
#> $lambda.min[[2]]
#> [1] 0.3801977
#>
#> $lambda.min[[3]]
#> [1] 0.2594991
#>
#>
#> $min.cvm
#> $min.cvm[[1]]
#> NULL
#>
#> $min.cvm[[2]]
#> [1] 0.7355824
#>
#> $min.cvm[[3]]
#> [1] 0.7394962
#>
#>
#> $nzero
#> $nzero[[1]]
#> NULL
#>
#> $nzero[[2]]
#> [1] 0
#>
#> $nzero[[3]]
#> [1] 0
#>
#>
#> $glmnet.fit
#> $glmnet.fit[[1]]
#> NULL
#>
#> $glmnet.fit[[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.38020
#> 2 2 1.00 0.34640
#> 3 2 2.13 0.31560
#> 4 2 3.11 0.28760
#> 5 2 3.95 0.26210
#> 6 2 4.67 0.23880
#> 7 2 5.29 0.21760
#> 8 2 5.82 0.19820
#> 9 4 6.62 0.18060
#> 10 4 7.39 0.16460
#> 11 4 8.04 0.15000
#> 12 5 8.65 0.13660
#> 13 5 9.23 0.12450
#> 14 7 9.79 0.11340
#> 15 7 10.30 0.10340
#> 16 8 10.74 0.09418
#> 17 9 11.29 0.08581
#> 18 10 11.76 0.07819
#> 19 10 12.23 0.07124
#> 20 13 12.71 0.06491
#> 21 16 13.25 0.05915
#> 22 16 13.72 0.05389
#> 23 16 14.11 0.04910
#> 24 16 14.44 0.04474
#> 25 18 14.73 0.04077
#> 26 18 15.00 0.03715
#> 27 18 15.23 0.03385
#> 28 19 15.42 0.03084
#> 29 19 15.58 0.02810
#> 30 19 15.72 0.02560
#> 31 19 15.83 0.02333
#> 32 19 15.93 0.02126
#> 33 19 16.00 0.01937
#> 34 19 16.07 0.01765
#> 35 19 16.13 0.01608
#> 36 19 16.17 0.01465
#> 37 19 16.21 0.01335
#> 38 19 16.24 0.01216
#> 39 19 16.27 0.01108
#> 40 19 16.29 0.01010
#> 41 19 16.31 0.00920
#> 42 19 16.32 0.00838
#> 43 19 16.34 0.00764
#> 44 19 16.35 0.00696
#> 45 19 16.36 0.00634
#> 46 19 16.36 0.00578
#> 47 19 16.37 0.00526
#> 48 19 16.37 0.00480
#> 49 19 16.38 0.00437
#> 50 19 16.38 0.00398
#> 51 20 16.39 0.00363
#> 52 20 16.39 0.00331
#> 53 20 16.39 0.00301
#> 54 20 16.39 0.00275
#> 55 20 16.39 0.00250
#> 56 20 16.39 0.00228
#> 57 20 16.40 0.00208
#> 58 20 16.40 0.00189
#> 59 20 16.40 0.00172
#> 60 20 16.40 0.00157
#> 61 20 16.40 0.00143
#> 62 20 16.40 0.00130
#> 63 20 16.40 0.00119
#> 64 20 16.40 0.00108
#> 65 20 16.40 0.00099
#> 66 20 16.40 0.00090
#> 67 20 16.40 0.00082
#>
#> $glmnet.fit[[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.259500
#> 2 3 0.50 0.236400
#> 3 4 1.59 0.215400
#> 4 4 2.66 0.196300
#> 5 4 3.57 0.178900
#> 6 6 4.51 0.163000
#> 7 8 5.60 0.148500
#> 8 8 6.62 0.135300
#> 9 9 7.54 0.123300
#> 10 10 8.46 0.112300
#> 11 12 9.33 0.102400
#> 12 13 10.21 0.093260
#> 13 13 10.96 0.084970
#> 14 13 11.58 0.077430
#> 15 13 12.11 0.070550
#> 16 13 12.56 0.064280
#> 17 14 12.98 0.058570
#> 18 14 13.34 0.053370
#> 19 15 13.66 0.048630
#> 20 16 13.98 0.044310
#> 21 16 14.26 0.040370
#> 22 16 14.49 0.036780
#> 23 16 14.68 0.033520
#> 24 16 14.85 0.030540
#> 25 16 14.98 0.027830
#> 26 16 15.10 0.025350
#> 27 16 15.19 0.023100
#> 28 17 15.28 0.021050
#> 29 17 15.36 0.019180
#> 30 17 15.42 0.017480
#> 31 17 15.47 0.015920
#> 32 17 15.52 0.014510
#> 33 18 15.56 0.013220
#> 34 18 15.59 0.012040
#> 35 18 15.62 0.010970
#> 36 18 15.64 0.010000
#> 37 18 15.66 0.009112
#> 38 18 15.68 0.008302
#> 39 18 15.69 0.007565
#> 40 18 15.70 0.006893
#> 41 19 15.71 0.006280
#> 42 19 15.72 0.005722
#> 43 19 15.73 0.005214
#> 44 19 15.73 0.004751
#> 45 19 15.74 0.004329
#> 46 19 15.74 0.003944
#> 47 19 15.74 0.003594
#> 48 20 15.75 0.003275
#> 49 20 15.75 0.002984
#> 50 20 15.75 0.002719
#> 51 20 15.75 0.002477
#> 52 20 15.75 0.002257
#> 53 20 15.76 0.002056
#> 54 20 15.76 0.001874
#> 55 20 15.76 0.001707
#> 56 20 15.76 0.001556
#> 57 20 15.76 0.001417
#> 58 20 15.76 0.001292
#> 59 20 15.76 0.001177
#> 60 20 15.76 0.001072
#> 61 20 15.76 0.000977
#> 62 20 15.76 0.000890
#> 63 20 15.76 0.000811
#> 64 20 15.76 0.000739
#>
#>
#> $name
#> mse
#> "Mean-Squared Error"
#>
#> $block1unpen
#>
#> Call: glm(formula = Y[current_observations] ~ X[current_observations,
#> blocks[[1]]], family = family, weights = weights[current_observations])
#>
#> Coefficients:
#> 1 2 3 4 5 6 7
#> 0.04197 -0.38770 1.20064 -1.01319 1.54866 1.01773 0.31193 0.67048
#> 8 9 10
#> 0.20170 -0.36212 -0.34006
#>
#> Degrees of Freedom: 99 Total (i.e. Null); 89 Residual
#> Null Deviance: 779.3
#> Residual Deviance: 71.5 AIC: 274.2
#>
#> $coefficients
#> 1 2 3 4 5
#> 0.04196541 -0.38769560 1.20063840 -1.01318812 1.54866069 1.01773384
#> 6 7 8 9 10 V1
#> 0.31193327 0.67048118 0.20170326 -0.36211724 -0.34005759 0.00000000
#> V2 V3 V4 V5 V6 V7
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V8 V9 V10 V11 V12 V13
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V14 V15 V16 V17 V18 V19
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V20 V1 V2 V3 V4 V5
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V6 V7 V8 V9 V10 V11
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V12 V13 V14 V15 V16 V17
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V18 V19 V20
#> 0.00000000 0.00000000 0.00000000
#>
#> $call
#> priorityelasticnet(X = X, Y = Y, family = "gaussian", type.measure = "mse",
#> blocks = blocks, block1.penalization = FALSE)
#>
#> $X
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.560475647 -0.71040656 2.19881035 -0.71524219 -0.07355602 -0.60189285
#> [2,] -0.230177489 0.25688371 1.31241298 -0.75268897 -1.16865142 -0.99369859
#> [3,] 1.558708314 -0.24669188 -0.26514506 -0.93853870 -0.63474826 1.02678506
#> [4,] 0.070508391 -0.34754260 0.54319406 -1.05251328 -0.02884155 0.75106130
#> [5,] 0.129287735 -0.95161857 -0.41433995 -0.43715953 0.67069597 -1.50916654
#> [6,] 1.715064987 -0.04502772 -0.47624689 0.33117917 -1.65054654 -0.09514745
#> [7,] 0.460916206 -0.78490447 -0.78860284 -2.01421050 -0.34975424 -0.89594782
#> [8,] -1.265061235 -1.66794194 -0.59461727 0.21198043 0.75640644 -2.07075107
#> [9,] -0.686852852 -0.38022652 1.65090747 1.23667505 -0.53880916 0.15012013
#> [10,] -0.445661970 0.91899661 -0.05402813 2.03757402 0.22729192 -0.07921171
#> [11,] 1.224081797 -0.57534696 0.11924524 1.30117599 0.49222857 -0.09736927
#> [12,] 0.359813827 0.60796432 0.24368743 0.75677476 0.26783502 0.21615254
#> [13,] 0.400771451 -1.61788271 1.23247588 -1.72673040 0.65325768 0.88246516
#> [14,] 0.110682716 -0.05556197 -0.51606383 -0.60150671 -0.12270866 0.20559750
#> [15,] -0.555841135 0.51940720 -0.99250715 -0.35204646 -0.41367651 -0.61643584
#> [16,] 1.786913137 0.30115336 1.67569693 0.70352390 -2.64314895 -0.73479925
#> [17,] 0.497850478 0.10567619 -0.44116322 -0.10567133 -0.09294102 -0.13180279
#> [18,] -1.966617157 -0.64070601 -0.72306597 -1.25864863 0.43028470 0.31001699
#> [19,] 0.701355902 -0.84970435 -1.23627312 1.68443571 0.53539884 -1.03968035
#> [20,] -0.472791408 -1.02412879 -1.28471572 0.91139129 -0.55527835 -0.18430887
#> [21,] -1.067823706 0.11764660 -0.57397348 0.23743027 1.77950291 0.96726726
#> [22,] -0.217974915 -0.94747461 0.61798582 1.21810861 0.28642442 -0.10828009
#> [23,] -1.026004448 -0.49055744 1.10984814 -1.33877429 0.12631586 -0.69842067
#> [24,] -0.728891229 -0.25609219 0.70758835 0.66082030 1.27226678 -0.27594517
#> [25,] -0.625039268 1.84386201 -0.36365730 -0.52291238 -0.71846622 1.11464855
#> [26,] -1.686693311 -0.65194990 0.05974994 0.68374552 -0.45033862 0.55004396
#> [27,] 0.837787044 0.23538657 -0.70459646 -0.06082195 2.39745248 1.23667580
#> [28,] 0.153373118 0.07796085 -0.71721816 0.63296071 0.01112919 0.13909786
#> [29,] -1.138136937 -0.96185663 0.88465050 1.33551762 1.63356842 0.41027510
#> [30,] 1.253814921 -0.07130809 -1.01559258 0.00729009 -1.43850664 -0.55845691
#> [31,] 0.426464221 1.44455086 1.95529397 1.01755864 -0.19051680 0.60537067
#> [32,] -0.295071483 0.45150405 -0.09031959 -1.18843404 0.37842390 -0.50633354
#> [33,] 0.895125661 0.04123292 0.21453883 -0.72160444 0.30003855 -1.42056550
#> [34,] 0.878133488 -0.42249683 -0.73852770 1.51921771 -1.00563626 0.12799297
#> [35,] 0.821581082 -2.05324722 -0.57438869 0.37738797 0.01925927 1.94585122
#> [36,] 0.688640254 1.13133721 -1.31701613 -2.05222282 -1.07742065 0.80091434
#> [37,] 0.553917654 -1.46064007 -0.18292539 -1.36403745 0.71270333 1.16525339
#> [38,] -0.061911711 0.73994751 0.41898240 -0.20078102 1.08477509 0.35885572
#> [39,] -0.305962664 1.90910357 0.32430434 0.86577940 -2.22498770 -0.60855718
#> [40,] -0.380471001 -1.44389316 -0.78153649 -0.10188326 1.23569346 -0.20224086
#> [41,] -0.694706979 0.70178434 -0.78862197 0.62418747 -1.24104450 -0.27324811
#> [42,] -0.207917278 -0.26219749 -0.50219872 0.95900538 0.45476927 -0.46869978
#> [43,] -1.265396352 -1.57214416 1.49606067 1.67105483 0.65990264 0.70416728
#> [44,] 2.168955965 -1.51466765 -1.13730362 0.05601673 -0.19988983 -1.19736350
#> [45,] 1.207961998 -1.60153617 -0.17905159 -0.05198191 -0.64511396 0.86636613
#> [46,] -1.123108583 -0.53090652 1.90236182 -1.75323736 0.16532102 0.86415249
#> [47,] -0.402884835 -1.46175558 -0.10097489 0.09932759 0.43881870 -1.19862236
#> [48,] -0.466655354 0.68791677 -1.35984070 -0.57185006 0.88330282 0.63949200
#> [49,] 0.779965118 2.10010894 -0.66476944 -0.97400958 -2.05233698 2.43022665
#> [50,] -0.083369066 -1.28703048 0.48545998 -0.17990623 -1.63637927 -0.55721548
#> [51,] 0.253318514 0.78773885 -0.37560287 1.01494317 1.43040234 0.84490424
#> [52,] -0.028546755 0.76904224 -0.56187636 -1.99274849 1.04662885 -0.78220185
#> [53,] -0.042870457 0.33220258 -0.34391723 -0.42727929 0.43528895 1.11071142
#> [54,] 1.368602284 -1.00837661 0.09049665 0.11663728 0.71517841 0.24982472
#> [55,] -0.225770986 -0.11945261 1.59850877 -0.89320757 0.91717492 1.65191539
#> [56,] 1.516470604 -0.28039534 -0.08856511 0.33390294 -2.66092280 -1.45897073
#> [57,] -1.548752804 0.56298953 1.08079950 0.41142992 1.11027710 -0.05129789
#> [58,] 0.584613750 -0.37243876 0.63075412 -0.03303616 -0.48498760 -0.52692518
#> [59,] 0.123854244 0.97697339 -0.11363990 -2.46589819 0.23061683 -0.19726487
#> [60,] 0.215941569 -0.37458086 -1.53290200 2.57145815 -0.29515780 -0.62957874
#> [61,] 0.379639483 1.05271147 -0.52111732 -0.20529926 0.87196495 -0.83384358
#> [62,] -0.502323453 -1.04917701 -0.48987045 0.65119328 -0.34847245 0.57872237
#> [63,] -0.333207384 -1.26015524 0.04715443 0.27376649 0.51850377 -1.08758071
#> [64,] -1.018575383 3.24103993 1.30019868 1.02467323 -0.39068498 1.48403093
#> [65,] -1.071791226 -0.41685759 2.29307897 0.81765945 -1.09278721 -1.18620659
#> [66,] 0.303528641 0.29822759 1.54758106 -0.20979317 1.21001051 0.10107915
#> [67,] 0.448209779 0.63656967 -0.13315096 0.37816777 0.74090001 0.53298929
#> [68,] 0.053004227 -0.48378063 -1.75652740 -0.94540883 1.72426224 0.58673534
#> [69,] 0.922267468 0.51686204 -0.38877986 0.85692301 0.06515393 -0.30174666
#> [70,] 2.050084686 0.36896453 0.08920722 -0.46103834 1.12500275 0.07950200
#> [71,] -0.491031166 -0.21538051 0.84501300 2.41677335 1.97541905 0.96126415
#> [72,] -2.309168876 0.06529303 0.96252797 -1.65104890 -0.28148212 -1.45646592
#> [73,] 1.005738524 -0.03406725 0.68430943 -0.46398724 -1.32295111 -0.78173971
#> [74,] -0.709200763 2.12845190 -1.39527435 0.82537986 -0.23935157 0.32040231
#> [75,] -0.688008616 -0.74133610 0.84964305 0.51013255 -0.21404124 -0.44478198
#> [76,] 1.025571370 -1.09599627 -0.44655722 -0.58948104 0.15168050 1.37000399
#> [77,] -0.284773007 0.03778840 0.17480270 -0.99678074 1.71230498 0.67325386
#> [78,] -1.220717712 0.31048075 0.07455118 0.14447570 -0.32614389 0.07216675
#> [79,] 0.181303480 0.43652348 0.42816676 -0.01430741 0.37300466 -1.50775732
#> [80,] -0.138891362 -0.45836533 0.02467498 -1.79028124 -0.22768406 0.02610023
#> [81,] 0.005764186 -1.06332613 -1.66747510 0.03455107 0.02045071 -0.31641587
#> [82,] 0.385280401 1.26318518 0.73649596 0.19023032 0.31405766 -0.10234651
#> [83,] -0.370660032 -0.34965039 0.38602657 0.17472640 1.32821470 -1.18155923
#> [84,] 0.644376549 -0.86551286 -0.26565163 -1.05501704 0.12131838 0.49865804
#> [85,] -0.220486562 -0.23627957 0.11814451 0.47613328 0.71284232 -1.03895644
#> [86,] 0.331781964 -0.19717589 0.13403865 1.37857014 0.77886003 -0.22622198
#> [87,] 1.096839013 1.10992029 0.22101947 0.45623640 0.91477327 0.38142583
#> [88,] 0.435181491 0.08473729 1.64084617 -1.13558847 -0.57439455 -0.78351579
#> [89,] -0.325931586 0.75405379 -0.21905038 -0.43564547 1.62688121 0.58299141
#> [90,] 1.148807618 -0.49929202 0.16806538 0.34610362 -0.38095674 -1.31651040
#> [91,] 0.993503856 0.21444531 1.16838387 -0.64704563 -0.10578417 -2.80977468
#> [92,] 0.548396960 -0.32468591 1.05418102 -2.15764634 1.40405027 0.46496799
#> [93,] 0.238731735 0.09458353 1.14526311 0.88425082 1.29408391 0.84053983
#> [94,] -0.627906076 -0.89536336 -0.57746800 -0.82947761 -1.08999187 -0.28584542
#> [95,] 1.360652449 -1.31080153 2.00248273 -0.57356027 -0.87307100 0.50412625
#> [96,] -0.600259587 1.99721338 0.06670087 1.50390061 -1.35807906 -1.15591653
#> [97,] 2.187332993 0.60070882 1.86685184 -0.77414493 0.18184719 -0.12714861
#> [98,] 1.532610626 -1.25127136 -1.35090269 0.84573154 0.16484087 -1.94151838
#> [99,] -0.235700359 -0.61116592 0.02098359 -1.26068288 0.36411469 1.18118089
#> [100,] -1.026420900 -1.18548008 1.24991457 -0.35454240 0.55215771 1.85991086
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 1.07401226 -0.728219111 0.35628334 -1.014114173 -0.99579872
#> [2,] -0.02734697 -1.540442405 -0.65801021 -0.791313879 -1.03995504
#> [3,] -0.03333034 -0.693094614 0.85520221 0.299593685 -0.01798024
#> [4,] -1.51606762 0.118849433 1.15293623 1.639051909 -0.13217513
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#> [100,] 0.37291160 0.90844770 -0.5661093329 -0.1631289899 0.838219387
#> [,17] [,18] [,19] [,20] [,21]
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#> [,22] [,23] [,24] [,25] [,26]
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#> [15,] 1.356836852 0.66528801 -1.11314851 -2.012868728 0.49096141
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#> [22,] -0.776251591 0.91049037 -2.01251978 -0.630552417 1.98220971
#> [23,] 0.831441251 -0.22192200 0.50493270 -0.560485987 -0.64644183
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#> [77,] -0.160798368 -0.71318782 0.83873579 0.281819311 -0.63668638
#> [78,] 0.283600226 -0.20574614 2.07174151 -0.400603355 1.73223870
#> [79,] 1.091262650 0.39001973 0.77561884 0.173361503 0.79037160
#> [80,] 0.444400297 -0.20721565 -1.42711135 1.369387670 -0.01370798
#> [81,] 1.012070341 -0.90050722 -1.03351134 1.299196094 1.20619648
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#> [84,] 1.085168884 -0.50851168 1.29073393 -1.454089145 0.52819440
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#> [99,] 0.165903102 -0.64829930 1.38051749 1.451280944 -1.44068098
#> [100,] 0.705334553 0.07196084 0.87263457 0.641551431 0.74047345
#> [,27] [,28] [,29] [,30] [,31]
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#> [3,] -1.202197647 -1.64905677 -1.43095845 1.10405027 -1.448165290
#> [4,] 0.882678068 -0.01530787 1.89201063 0.30487211 -0.697284585
#> [5,] -1.516479036 -0.89490168 0.39787711 -0.13042189 2.598490232
#> [6,] 1.921611558 0.04631972 -0.39702813 -0.29361339 -0.037415014
#> [7,] 0.572135778 0.46100408 -0.27995785 1.58625546 0.913491890
#> [8,] -1.714895054 -0.50373877 0.78511853 1.20114550 -0.184526498
#> [9,] 0.354918896 -1.02239846 -0.21032081 -1.00373971 0.609824296
#> [10,] 0.105181866 -0.61174223 0.19211496 -1.39101698 -0.052726809
#> [11,] -2.468413533 -0.66739350 -0.26472563 1.08529588 1.363921956
#> [12,] -0.041858030 -1.49327583 -0.50139106 0.56061181 -0.503633417
#> [13,] -0.587260768 -0.78061821 0.60218981 -0.51098099 -1.709060169
#> [14,] -0.887296993 -0.19340513 0.14149456 0.39645197 0.898549683
#> [15,] 0.238716820 -0.22083331 1.30181267 -2.02323110 -0.237734026
#> [16,] 0.850890302 0.32954174 -1.12539686 -0.66514147 1.463407581
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#> [,32] [,33] [,34] [,35] [,36]
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#> [,37] [,38] [,39] [,40] [,41] [,42]
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#> [8,] -2.67816441 -0.249252612 0.21269503 0.38195897 0.37564226 -0.18892279
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#> [10,] -0.75063055 0.250899226 0.18291685 0.39057609 1.13254984 -1.25744854
#> [11,] -0.34914391 0.466728667 -0.02467293 -0.65270255 1.83947679 -0.31176654
#> [12,] 1.61863074 1.294261816 -1.09939100 0.81998237 1.52787010 0.05249805
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#> [17,] 1.10328832 -1.352901850 0.15215140 -0.85199189 -0.54388852 -0.54611202
#> [18,] 1.26449613 0.309441562 -1.97326132 -1.71937580 -0.85854930 0.45037717
#> [19,] 0.50087613 0.420861381 -0.65887876 -0.49469904 0.22978594 -0.69251547
#> [20,] -0.43940583 -1.366025419 -0.82845265 1.06648978 -0.94264315 -0.77959611
#> [21,] -0.42367998 0.047864472 1.23170354 -1.36184763 2.04350430 -0.04733266
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#> [,43] [,44] [,45] [,46] [,47] [,48]
#> [1,] 0.65099328 1.433174741 -0.03287805 0.83437149 0.91709650 1.74568499
#> [2,] -0.89516799 0.912744883 -0.77600711 -0.69840395 0.55474357 1.67538957
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#> [10,] 0.35480442 0.039242237 2.13425461 -2.13764150 0.87659208 0.30160044
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#> [14,] 0.43971623 -0.132874384 1.53836359 0.10875907 0.57448314 -0.28401258
#> [15,] 0.69821380 -0.240116874 0.46835160 1.29479690 -0.46275428 -0.96009246
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#> [17,] -1.12632173 -0.642231451 0.23043894 0.73373952 1.18298161 0.49114620
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#> [19,] -0.25327286 -1.973013711 0.50870847 0.65576257 0.92469895 -0.32550779
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#> [22,] 0.31506322 -0.226077239 1.58317836 -0.38959046 0.03407460 -0.02485664
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#> [28,] 0.01745325 -1.493520373 0.38592715 0.98037879 2.52239807 -0.42523023
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#> [42,] 0.66886073 0.025873102 -1.06846648 0.07573238 0.14124174 0.64528193
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#> [44,] -0.36416639 0.067010604 0.86088849 1.28164890 -0.25827324 -0.28622917
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#> [59,] -1.26924504 -1.920885050 -0.92584586 -1.44256268 -1.27941767 0.07882856
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#> [62,] 2.33752882 -1.110207184 1.87015839 1.11407753 -0.60814853 1.08512896
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#> [64,] -2.88762983 -0.412925485 -0.88543544 -1.09100028 0.84184980 0.50322103
#> [65,] 1.37208530 0.951889434 -0.87553390 0.23922274 0.38015694 -0.45571199
#> [66,] -0.59865238 -0.920180527 0.78839046 -0.05321768 -0.53484433 -0.78229359
#> [67,] 0.59295092 0.118175118 0.03134468 0.04031788 1.12971201 -0.54620305
#> [68,] 0.22574207 -0.202992795 0.48894782 0.21545474 1.03188963 1.03625305
#> [69,] 1.09631206 0.793099799 0.77146988 -0.39402100 -0.98938258 1.09077666
#> [70,] -0.90326602 0.038436841 0.24783461 -0.32659087 0.31316853 1.55487240
#> [71,] -1.18906159 -0.168162992 0.44783164 0.64800382 -1.15966477 -0.06199721
#> [72,] 1.06496900 -0.584189409 -1.16256527 1.62673702 1.46673354 -0.75605644
#> [73,] -0.95856747 0.891898667 -0.06178828 -1.92569377 0.27005958 1.47246617
#> [74,] -1.53369412 1.139333076 -0.61610346 -0.13568041 1.06713532 -1.55194490
#> [75,] 0.77796950 0.019442483 -1.30482930 0.97968230 0.38814380 -0.15888538
#> [76,] -0.06525828 3.271782751 -1.16898434 -1.17921193 -0.10827039 0.60325702
#> [77,] 2.27820422 -0.002993212 0.93760955 1.16681337 0.75048854 -1.16228474
#> [78,] 0.34360962 2.923823950 -1.30054699 -0.37922742 -1.10331775 -1.56009578
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#> [80,] -0.62718455 -1.570707062 0.98256505 1.47376578 -0.63115364 1.62105051
#> [81,] 1.68460867 -1.424766580 0.32925949 0.89857683 0.26361795 -0.71473653
#> [82,] -1.21492788 -0.871469943 0.65234723 1.21431502 -0.41368807 -0.68668744
#> [83,] 0.61696205 1.478407982 0.33137936 -2.20782706 -0.46511874 -0.94160377
#> [84,] 0.56168002 1.703323302 -0.14887534 -1.27336280 0.92085150 1.48472600
#> [85,] -0.57280593 0.397608593 -2.19971758 0.58146666 -0.50219271 -0.70793519
#> [86,] 1.53571788 0.308495293 -0.60883851 -0.91078080 0.97445687 -0.83744381
#> [87,] -0.74765546 -0.536955293 -1.37830797 -0.55187450 -0.77293592 -0.80402999
#> [88,] -0.01947186 -0.676675596 -0.37808429 1.38422225 -0.25648336 -0.58790399
#> [89,] 0.38762840 -0.717903102 2.05410707 0.11649412 -0.82631334 -0.59771794
#> [90,] 2.32312597 -0.870549995 0.13822540 0.04531788 -0.42619932 0.60644747
#> [91,] 0.61515224 -0.539922450 -0.71914628 -0.24558563 -1.16169687 0.30172811
#> [92,] 1.73154803 -0.622689768 0.88869244 -1.59789552 0.44698697 0.47474825
#> [93,] -0.72856262 0.528537450 0.49137293 -1.88057397 1.18231430 -0.63020029
#> [94,] -1.74544031 0.770818672 -0.08035007 -0.21776624 0.28335869 0.72451431
#> [95,] 0.88935679 1.603180754 -0.22763125 0.35473879 1.71226784 -3.04313484
#> [96,] -1.62846900 -2.448621354 -0.14548558 -1.31894478 -1.64010000 1.12770217
#> [97,] -1.34221036 0.495119682 -0.07142003 -1.80778010 -0.75155207 0.19984638
#> [98,] 0.61077020 -0.318468478 0.61953024 1.27550914 0.52464440 -0.40510219
#> [99,] -0.05577663 -0.266390603 0.12765668 0.50699835 0.63337929 0.47552750
#> [100,] 0.84701928 -1.641704110 -0.62737665 0.48209487 0.32699672 -1.22312208
#> [,49] [,50]
#> [1,] -0.6327135546 0.83666204
#> [2,] 0.1091716177 -0.98027865
#> [3,] -1.5625565841 0.34400599
#> [4,] -0.0402454328 0.18553456
#> [5,] -0.0363299297 0.14119961
#> [6,] -0.2789255815 -1.85209740
#> [7,] -1.2931294494 0.16242002
#> [8,] 1.1668008061 -0.49317896
#> [9,] -1.4853740471 -0.70378507
#> [10,] -1.4771204103 -1.18362071
#> [11,] -0.5826403563 -1.13869818
#> [12,] 1.5493037909 -0.84560347
#> [13,] 0.1068829308 1.24699041
#> [14,] 0.2595667288 0.69516501
#> [15,] -0.2159887019 0.27483248
#> [16,] 0.2708474117 1.71648527
#> [17,] 0.6331892474 1.61208120
#> [18,] 0.7074693315 0.90296077
#> [19,] 1.3706814684 -1.18344199
#> [20,] -0.7780561341 1.43308002
#> [21,] -0.1581135449 -0.20212664
#> [22,] 0.4135386632 -0.24267130
#> [23,] 0.8250757253 0.23754012
#> [24,] -0.3330222488 0.06293772
#> [25,] 0.6507739654 -0.49388005
#> [26,] -0.5484526829 0.68486948
#> [27,] -0.3414764527 -0.48204249
#> [28,] 1.0121437663 -0.56479517
#> [29,] -1.8827545019 -0.25429341
#> [30,] 0.2215467407 -0.75968287
#> [31,] 0.9259399916 0.15368201
#> [32,] -0.3447769817 -0.09725350
#> [33,] 0.6248557297 -0.29590058
#> [34,] -0.7064962937 0.46379138
#> [35,] 0.1712074144 -1.82483094
#> [36,] 0.0097787569 0.25244191
#> [37,] -0.0285917182 0.90124825
#> [38,] -1.2757872641 0.88044069
#> [39,] -0.1625880411 2.23177010
#> [40,] -0.8139526680 -0.63983483
#> [41,] -0.3596072814 -0.98010365
#> [42,] 1.0242439953 0.32609798
#> [43,] -0.5665925821 -1.68526240
#> [44,] -0.0327291611 1.21069157
#> [45,] 0.1030236218 -1.04711359
#> [46,] -0.1894660344 0.43854678
#> [47,] 0.8060904906 -0.33780519
#> [48,] -0.0424478238 -2.37947639
#> [49,] 0.1548982257 0.25934489
#> [50,] -0.8902812005 -1.10300468
#> [51,] -0.3822590762 0.92230106
#> [52,] -0.6470044320 -2.45149101
#> [53,] 0.4742782920 -0.13100382
#> [54,] 1.1515289233 -1.05339701
#> [55,] -0.4606314937 1.12716590
#> [56,] -2.2152623848 -0.72783464
#> [57,] -0.8455127725 0.93534059
#> [58,] -0.9342758947 -0.46829210
#> [59,] 1.1807547873 0.12982107
#> [60,] 0.1429936840 1.46235284
#> [61,] 1.5647374594 -0.68216938
#> [62,] 0.4009041275 1.81861839
#> [63,] -1.5475572207 0.98615837
#> [64,] 0.4949106183 1.28460132
#> [65,] -0.7478538949 -2.24640057
#> [66,] 0.0006033594 -0.16851663
#> [67,] -0.1016533711 -1.46661663
#> [68,] -0.1440581426 0.75927504
#> [69,] -0.3313690567 1.22277703
#> [70,] 1.9212081546 -0.61753539
#> [71,] 1.5098548580 -0.51177394
#> [72,] -0.8892843981 -1.62158019
#> [73,] 0.1986802070 0.79093764
#> [74,] 1.1513646800 1.46152196
#> [75,] 1.1025255707 -1.69993222
#> [76,] -0.8953830461 -1.81251475
#> [77,] 1.4098008988 1.14414110
#> [78,] -0.7045957970 1.34854186
#> [79,] 0.1266425333 0.37155646
#> [80,] 0.1687558038 0.24224903
#> [81,] -1.9199911246 -0.62125855
#> [82,] -0.1333074202 0.33903807
#> [83,] -2.1003865730 -0.45214013
#> [84,] -1.9663385042 2.04323321
#> [85,] 0.3205154324 -0.44933769
#> [86,] 0.3412434206 -3.13738453
#> [87,] 0.9743347007 0.49996221
#> [88,] 0.3795461982 -1.25714159
#> [89,] -0.6737692956 0.82276143
#> [90,] -0.8007270741 -1.54609608
#> [91,] 0.8045545068 -0.25878076
#> [92,] 1.4510356488 0.39040738
#> [93,] 0.7987937110 -0.19727020
#> [94,] 0.2169247894 -1.94694948
#> [95,] -0.0689971963 -1.42763817
#> [96,] 1.6284169621 -0.85041804
#> [97,] -2.4916869814 1.62446909
#> [98,] 0.9929091010 -0.12663816
#> [99,] -0.1676952820 1.27560203
#> [100,] -1.1271011796 0.17949618
#>
#> $missing.data
#> $missing.data[[1]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[2]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[3]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#>
#> $imputation.models
#> NULL
#>
#> $blocks.used.for.imputation
#> list()
#>
#> $missingness.pattern
#> list()
#>
#> $y.scale.param
#> NULL
#>
#> $blocks
#> $blocks$block1
#> [1] 1 2 3 4 5 6 7 8 9 10
#>
#> $blocks$block2
#> [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $blocks$block3
#> [1] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#>
#> $mcontrol
#> $handle.missingdata
#> [1] "none"
#>
#> $offset.firstblock
#> [1] "zero"
#>
#> $impute.offset.cases
#> [1] "complete.cases"
#>
#> $nfolds.imputation
#> [1] 10
#>
#> $lambda.imputation
#> [1] "lambda.min"
#>
#> $perc.comp.cases.warning
#> [1] 0.3
#>
#> $threshold.available.cases
#> [1] 30
#>
#> $select.available.cases
#> [1] "maximise.blocks"
#>
#> attr(,"class")
#> [1] "pl.missing.control" "list"
#>
#> $family
#> [1] "gaussian"
#>
#> $dim.x
#> [1] 100 50
#>
#> $pred
#> s1
#> [1,] -3.40270715
#> [2,] -3.37677490
#> [3,] -2.94278859
#> [4,] -4.34545170
#> [5,] -1.41028928
#> [6,] -1.25319036
#> [7,] -4.67032281
#> [8,] -2.31083160
#> [9,] -0.76514321
#> [10,] 4.28538855
#> [11,] 1.22164175
#> [12,] 2.00808043
#> [13,] -3.93222234
#> [14,] -0.03469549
#> [15,] 1.43881882
#> [16,] -6.00459533
#> [17,] -0.43340232
#> [18,] -0.94285893
#> [19,] 2.28031084
#> [20,] 2.10021084
#> [21,] 3.08602972
#> [22,] 0.17514250
#> [23,] -2.69690575
#> [24,] 0.93317017
#> [25,] 0.95747563
#> [26,] 1.07244604
#> [27,] 3.83916635
#> [28,] 1.76701142
#> [29,] 0.78983218
#> [30,] 0.84595480
#> [31,] 0.65051196
#> [32,] -1.21696712
#> [33,] -2.04956451
#> [34,] 1.32814534
#> [35,] -1.79460507
#> [36,] -2.66115892
#> [37,] -1.84962025
#> [38,] 2.72088026
#> [39,] 1.47458744
#> [40,] -0.41402532
#> [41,] 1.07374452
#> [42,] 1.65005920
#> [43,] 1.93378803
#> [44,] -1.13520812
#> [45,] -2.93779874
#> [46,] -4.23355957
#> [47,] -0.78378059
#> [48,] 1.25955199
#> [49,] 0.57877346
#> [50,] -2.91777168
#> [51,] 3.41623192
#> [52,] -0.29233423
#> [53,] 1.02382194
#> [54,] -1.39497510
#> [55,] -2.24469626
#> [56,] -3.24847900
#> [57,] 3.31600650
#> [58,] -1.63812940
#> [59,] -1.82882756
#> [60,] 3.67577323
#> [61,] 2.08739189
#> [62,] 0.52407119
#> [63,] 1.11536290
#> [64,] 4.60038458
#> [65,] -3.98951464
#> [66,] -1.07939973
#> [67,] 3.97086576
#> [68,] 1.70318131
#> [69,] 2.53153382
#> [70,] 0.62758126
#> [71,] 4.88083440
#> [72,] -2.87668856
#> [73,] -2.12418580
#> [74,] 4.77284122
#> [75,] -1.76380238
#> [76,] -2.81963530
#> [77,] -0.33899620
#> [78,] -0.91423990
#> [79,] -0.86903593
#> [80,] -3.99881036
#> [81,] -0.10805627
#> [82,] 2.82063934
#> [83,] 0.31232889
#> [84,] -3.50482346
#> [85,] -0.81180635
#> [86,] 2.09630389
#> [87,] 2.88634405
#> [88,] -4.39461217
#> [89,] 2.49947087
#> [90,] -3.66700816
#> [91,] -3.10858200
#> [92,] -2.62254893
#> [93,] 3.07873101
#> [94,] -3.73893662
#> [95,] -7.05327180
#> [96,] 5.28901971
#> [97,] -3.51165997
#> [98,] 0.61785980
#> [99,] -1.46312832
#> [100,] -2.61180117
#>
#> $actuals
#> [,1]
#> [1,] -4.320199229
#> [2,] -2.145050089
#> [3,] -2.417788193
#> [4,] -4.417505678
#> [5,] -2.659050504
#> [6,] -0.936684634
#> [7,] -5.387087465
#> [8,] -3.057359036
#> [9,] 1.090943326
#> [10,] 2.767568537
#> [11,] 0.296284884
#> [12,] 1.449905790
#> [13,] -4.743973252
#> [14,] -1.092945685
#> [15,] 1.288868616
#> [16,] -7.139407664
#> [17,] 0.935394039
#> [18,] -0.492059784
#> [19,] 3.251529435
#> [20,] 0.889970862
#> [21,] 3.276574344
#> [22,] 0.478397584
#> [23,] -2.655299692
#> [24,] 0.641837880
#> [25,] 0.100210428
#> [26,] 0.589233025
#> [27,] 3.185856430
#> [28,] 1.214770377
#> [29,] 1.560368758
#> [30,] 2.626714387
#> [31,] 1.453961044
#> [32,] -0.560509889
#> [33,] -2.473335868
#> [34,] 1.745593848
#> [35,] -2.322276055
#> [36,] -1.986164598
#> [37,] -1.442586898
#> [38,] 0.104824201
#> [39,] 3.267139834
#> [40,] -1.641272627
#> [41,] -1.246306746
#> [42,] 1.343464266
#> [43,] 1.919254204
#> [44,] -1.226210691
#> [45,] -2.753044533
#> [46,] -5.523246057
#> [47,] -0.007445442
#> [48,] 2.678443011
#> [49,] -0.321469775
#> [50,] -2.537900294
#> [51,] 3.016202621
#> [52,] -0.361412345
#> [53,] 1.850127170
#> [54,] -1.081278725
#> [55,] -0.775000884
#> [56,] -3.402119142
#> [57,] 4.620760220
#> [58,] -2.239158005
#> [59,] -2.117775424
#> [60,] 3.204390100
#> [61,] 2.029905859
#> [62,] 0.997897180
#> [63,] 1.173641110
#> [64,] 4.933191909
#> [65,] -4.717716401
#> [66,] -1.945906153
#> [67,] 4.563316002
#> [68,] 2.790612536
#> [69,] 2.662359340
#> [70,] 0.723182070
#> [71,] 5.435135820
#> [72,] -2.068046736
#> [73,] -2.377080276
#> [74,] 4.625818695
#> [75,] -1.330741150
#> [76,] -3.200455008
#> [77,] -0.557272238
#> [78,] -0.955805921
#> [79,] -2.700197509
#> [80,] -3.440214452
#> [81,] 0.624757094
#> [82,] 2.432717701
#> [83,] 1.990184192
#> [84,] -3.630464548
#> [85,] -0.332671471
#> [86,] 2.372909676
#> [87,] 3.399604568
#> [88,] -4.175389619
#> [89,] 2.248719212
#> [90,] -3.110653465
#> [91,] -3.094141999
#> [92,] -2.742008980
#> [93,] 3.379887452
#> [94,] -2.716828776
#> [95,] -7.173844818
#> [96,] 4.602369405
#> [97,] -2.728815268
#> [98,] 0.661930236
#> [99,] -1.808280650
#> [100,] -3.424192429
#>
#> $adaptive
#> [1] FALSE
#>
#> $adaptive_weights
#> NULL
#>
#> $initial_coeff
#> NULL
#>
#> $initial_weight_scope
#> [1] "global"
#>
#> attr(,"class")
#> [1] "priorityelasticnet" "list"The output provides detailed information about the selected lambda values, the number of non-zero coefficients in each block, and the deviance explained by the model. By analyzing these results, you can assess the impact of block-wise penalization on model performance and make informed decisions about which blocks should be penalized or left unpenalized in your specific application.
This capability allows for a more nuanced model construction, where penalization is tailored to the characteristics and importance of different predictor groups, ultimately leading to a more robust and interpretable model.
Handling Missing Data
Handling missing data is a crucial aspect of building robust models, especially when working with real-world datasets where missing values are common. The priorityelasticnet function provides several options for managing missing data, allowing you to choose the most appropriate strategy based on the nature of your dataset and the goals of your analysis.
The mcontrol argument in priorityelasticnet enables you to specify how missing data should be handled. This flexibility ensures that your model can be fitted even when dealing with incomplete data, which might otherwise lead to biased estimates or reduced predictive power.
Below, we demonstrate how to configure the mcontrol argument to handle missing data by imputing offsets. To exemplify the process of handling missing values, this example uses data generated under a Gaussian model.
mcontrol <-missing.control(handle.missingdata = "impute.offset", nfolds.imputation = 5)
fit_missing <- priorityelasticnet(
X,
Y,
family = "gaussian",
type.measure = "mse",
blocks = blocks,
mcontrol = mcontrol
)
#> Warning in priorityelasticnet(X, Y, family = "gaussian", type.measure = "mse",
#> : For handle.missingdata = impute.offset, the foldids of the observations are
#> chosen individually for every block and not set globally. foldid is set to NULLIn this example, the handle.missingdata = “impute.offset” option tells the priorityelasticnet function to impute missing values using an offset approach. This method is particularly useful when missing data is sporadic and you want to ensure that the model can incorporate all available information without discarding incomplete observations.
After fitting the model with the specified missing data handling strategy, you can examine the results:
fit_missing
#> $lambda.ind
#> $lambda.ind[[1]]
#> [1] 64
#>
#> $lambda.ind[[2]]
#> [1] 5
#>
#> $lambda.ind[[3]]
#> [1] 1
#>
#>
#> $lambda.type
#> [1] "lambda.min"
#>
#> $lambda.min
#> $lambda.min[[1]]
#> [1] 0.009298608
#>
#> $lambda.min[[2]]
#> [1] 0.2617908
#>
#> $lambda.min[[3]]
#> [1] 0.2478425
#>
#>
#> $min.cvm
#> $min.cvm[[1]]
#> [1] 0.8801211
#>
#> $min.cvm[[2]]
#> [1] 0.7257365
#>
#> $min.cvm[[3]]
#> [1] 0.72413
#>
#>
#> $nzero
#> $nzero[[1]]
#> [1] 10
#>
#> $nzero[[2]]
#> [1] 2
#>
#> $nzero[[3]]
#> [1] 0
#>
#>
#> $glmnet.fit
#> $glmnet.fit[[1]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 3.2650
#> 2 1 3.85 2.9750
#> 3 1 7.37 2.7110
#> 4 1 10.57 2.4700
#> 5 2 14.93 2.2500
#> 6 2 19.50 2.0500
#> 7 4 24.99 1.8680
#> 8 4 31.93 1.7020
#> 9 4 38.09 1.5510
#> 10 5 43.82 1.4130
#> 11 5 49.51 1.2880
#> 12 5 54.50 1.1730
#> 13 6 58.90 1.0690
#> 14 6 62.92 0.9741
#> 15 6 66.40 0.8876
#> 16 7 69.41 0.8087
#> 17 7 72.19 0.7369
#> 18 8 74.73 0.6714
#> 19 8 76.92 0.6118
#> 20 8 78.79 0.5574
#> 21 9 80.58 0.5079
#> 22 9 82.12 0.4628
#> 23 10 83.45 0.4217
#> 24 10 84.63 0.3842
#> 25 10 85.63 0.3501
#> 26 10 86.47 0.3190
#> 27 10 87.17 0.2906
#> 28 10 87.77 0.2648
#> 29 10 88.27 0.2413
#> 30 10 88.69 0.2199
#> 31 10 89.04 0.2003
#> 32 10 89.33 0.1825
#> 33 10 89.58 0.1663
#> 34 10 89.79 0.1515
#> 35 10 89.96 0.1381
#> 36 10 90.10 0.1258
#> 37 10 90.22 0.1146
#> 38 10 90.32 0.1045
#> 39 10 90.41 0.0952
#> 40 10 90.48 0.0867
#> 41 10 90.54 0.0790
#> 42 10 90.58 0.0720
#> 43 10 90.62 0.0656
#> 44 10 90.66 0.0598
#> 45 10 90.69 0.0545
#> 46 10 90.71 0.0496
#> 47 10 90.73 0.0452
#> 48 10 90.75 0.0412
#> 49 10 90.76 0.0375
#> 50 10 90.77 0.0342
#> 51 10 90.78 0.0312
#> 52 10 90.79 0.0284
#> 53 10 90.79 0.0259
#> 54 10 90.80 0.0236
#> 55 10 90.80 0.0215
#> 56 10 90.81 0.0196
#> 57 10 90.81 0.0178
#> 58 10 90.81 0.0163
#> 59 10 90.81 0.0148
#> 60 10 90.82 0.0135
#> 61 10 90.82 0.0123
#> 62 10 90.82 0.0112
#> 63 10 90.82 0.0102
#> 64 10 90.82 0.0093
#>
#> $glmnet.fit[[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.37980
#> 2 2 1.04 0.34610
#> 3 2 2.17 0.31530
#> 4 2 3.14 0.28730
#> 5 2 3.98 0.26180
#> 6 2 4.70 0.23850
#> 7 2 5.32 0.21730
#> 8 2 5.85 0.19800
#> 9 4 6.65 0.18040
#> 10 4 7.41 0.16440
#> 11 4 8.06 0.14980
#> 12 5 8.67 0.13650
#> 13 7 9.28 0.12440
#> 14 7 9.88 0.11330
#> 15 7 10.39 0.10330
#> 16 7 10.82 0.09408
#> 17 9 11.36 0.08572
#> 18 9 11.83 0.07811
#> 19 10 12.27 0.07117
#> 20 13 12.80 0.06485
#> 21 16 13.30 0.05909
#> 22 16 13.76 0.05384
#> 23 16 14.15 0.04905
#> 24 16 14.48 0.04470
#> 25 17 14.76 0.04073
#> 26 18 15.03 0.03711
#> 27 18 15.26 0.03381
#> 28 18 15.45 0.03081
#> 29 18 15.61 0.02807
#> 30 18 15.75 0.02558
#> 31 19 15.86 0.02331
#> 32 19 15.95 0.02123
#> 33 19 16.03 0.01935
#> 34 19 16.10 0.01763
#> 35 19 16.15 0.01606
#> 36 19 16.20 0.01464
#> 37 19 16.24 0.01334
#> 38 19 16.27 0.01215
#> 39 19 16.29 0.01107
#> 40 19 16.32 0.01009
#> 41 19 16.34 0.00919
#> 42 19 16.35 0.00838
#> 43 19 16.36 0.00763
#> 44 19 16.37 0.00695
#> 45 19 16.38 0.00634
#> 46 19 16.39 0.00577
#> 47 19 16.40 0.00526
#> 48 19 16.40 0.00479
#> 49 19 16.41 0.00437
#> 50 19 16.41 0.00398
#> 51 20 16.41 0.00363
#> 52 20 16.41 0.00330
#> 53 20 16.42 0.00301
#> 54 20 16.42 0.00274
#> 55 20 16.42 0.00250
#> 56 20 16.42 0.00228
#> 57 20 16.42 0.00208
#> 58 20 16.42 0.00189
#> 59 20 16.42 0.00172
#> 60 20 16.42 0.00157
#> 61 20 16.42 0.00143
#> 62 20 16.42 0.00130
#> 63 20 16.42 0.00119
#> 64 20 16.43 0.00108
#> 65 20 16.43 0.00099
#> 66 20 16.43 0.00090
#> 67 20 16.43 0.00082
#>
#> $glmnet.fit[[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.247800
#> 2 3 0.56 0.225800
#> 3 4 1.64 0.205800
#> 4 5 2.67 0.187500
#> 5 8 3.86 0.170800
#> 6 8 5.22 0.155700
#> 7 8 6.37 0.141800
#> 8 8 7.35 0.129200
#> 9 9 8.25 0.117700
#> 10 10 9.12 0.107300
#> 11 11 9.93 0.097750
#> 12 12 10.70 0.089070
#> 13 13 11.41 0.081160
#> 14 13 12.01 0.073950
#> 15 14 12.58 0.067380
#> 16 14 13.07 0.061390
#> 17 14 13.48 0.055940
#> 18 14 13.83 0.050970
#> 19 15 14.16 0.046440
#> 20 16 14.45 0.042320
#> 21 16 14.72 0.038560
#> 22 16 14.94 0.035130
#> 23 16 15.13 0.032010
#> 24 16 15.28 0.029170
#> 25 16 15.41 0.026580
#> 26 16 15.52 0.024210
#> 27 17 15.62 0.022060
#> 28 18 15.70 0.020100
#> 29 19 15.78 0.018320
#> 30 19 15.85 0.016690
#> 31 19 15.91 0.015210
#> 32 19 15.95 0.013860
#> 33 19 15.99 0.012630
#> 34 19 16.03 0.011500
#> 35 19 16.06 0.010480
#> 36 19 16.08 0.009551
#> 37 19 16.10 0.008702
#> 38 19 16.12 0.007929
#> 39 19 16.13 0.007225
#> 40 19 16.14 0.006583
#> 41 19 16.15 0.005998
#> 42 20 16.16 0.005465
#> 43 20 16.16 0.004980
#> 44 20 16.17 0.004537
#> 45 20 16.17 0.004134
#> 46 20 16.18 0.003767
#> 47 20 16.18 0.003432
#> 48 20 16.18 0.003127
#> 49 20 16.19 0.002850
#> 50 20 16.19 0.002596
#> 51 20 16.19 0.002366
#> 52 20 16.19 0.002156
#> 53 20 16.19 0.001964
#> 54 20 16.19 0.001790
#> 55 20 16.19 0.001631
#> 56 20 16.19 0.001486
#> 57 20 16.19 0.001354
#> 58 20 16.19 0.001234
#> 59 20 16.19 0.001124
#> 60 20 16.19 0.001024
#> 61 20 16.20 0.000933
#> 62 20 16.20 0.000850
#> 63 20 16.20 0.000775
#> 64 20 16.20 0.000706
#>
#>
#> $name
#> mse
#> "Mean-Squared Error"
#>
#> $block1unpen
#> NULL
#>
#> $coefficients
#> V1 V2 V3 V4 V5 V6
#> -0.38237226 1.19498601 -1.00638497 1.54134581 1.01194132 0.30777552
#> V7 V8 V9 V10 V1 V2
#> 0.66521016 0.19710735 -0.35853947 -0.33364777 0.00000000 0.00000000
#> V3 V4 V5 V6 V7 V8
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V9 V10 V11 V12 V13 V14
#> 0.00000000 -0.04362540 0.00000000 0.00000000 -0.04419504 0.00000000
#> V15 V16 V17 V18 V19 V20
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V1 V2 V3 V4 V5 V6
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V7 V8 V9 V10 V11 V12
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V13 V14 V15 V16 V17 V18
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V19 V20
#> 0.00000000 0.00000000
#>
#> $call
#> priorityelasticnet(X = X, Y = Y, family = "gaussian", type.measure = "mse",
#> blocks = blocks, mcontrol = mcontrol)
#>
#> $X
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.560475647 -0.71040656 2.19881035 -0.71524219 -0.07355602 -0.60189285
#> [2,] -0.230177489 0.25688371 1.31241298 -0.75268897 -1.16865142 -0.99369859
#> [3,] 1.558708314 -0.24669188 -0.26514506 -0.93853870 -0.63474826 1.02678506
#> [4,] 0.070508391 -0.34754260 0.54319406 -1.05251328 -0.02884155 0.75106130
#> [5,] 0.129287735 -0.95161857 -0.41433995 -0.43715953 0.67069597 -1.50916654
#> [6,] 1.715064987 -0.04502772 -0.47624689 0.33117917 -1.65054654 -0.09514745
#> [7,] 0.460916206 -0.78490447 -0.78860284 -2.01421050 -0.34975424 -0.89594782
#> [8,] -1.265061235 -1.66794194 -0.59461727 0.21198043 0.75640644 -2.07075107
#> [9,] -0.686852852 -0.38022652 1.65090747 1.23667505 -0.53880916 0.15012013
#> [10,] -0.445661970 0.91899661 -0.05402813 2.03757402 0.22729192 -0.07921171
#> [11,] 1.224081797 -0.57534696 0.11924524 1.30117599 0.49222857 -0.09736927
#> [12,] 0.359813827 0.60796432 0.24368743 0.75677476 0.26783502 0.21615254
#> [13,] 0.400771451 -1.61788271 1.23247588 -1.72673040 0.65325768 0.88246516
#> [14,] 0.110682716 -0.05556197 -0.51606383 -0.60150671 -0.12270866 0.20559750
#> [15,] -0.555841135 0.51940720 -0.99250715 -0.35204646 -0.41367651 -0.61643584
#> [16,] 1.786913137 0.30115336 1.67569693 0.70352390 -2.64314895 -0.73479925
#> [17,] 0.497850478 0.10567619 -0.44116322 -0.10567133 -0.09294102 -0.13180279
#> [18,] -1.966617157 -0.64070601 -0.72306597 -1.25864863 0.43028470 0.31001699
#> [19,] 0.701355902 -0.84970435 -1.23627312 1.68443571 0.53539884 -1.03968035
#> [20,] -0.472791408 -1.02412879 -1.28471572 0.91139129 -0.55527835 -0.18430887
#> [21,] -1.067823706 0.11764660 -0.57397348 0.23743027 1.77950291 0.96726726
#> [22,] -0.217974915 -0.94747461 0.61798582 1.21810861 0.28642442 -0.10828009
#> [23,] -1.026004448 -0.49055744 1.10984814 -1.33877429 0.12631586 -0.69842067
#> [24,] -0.728891229 -0.25609219 0.70758835 0.66082030 1.27226678 -0.27594517
#> [25,] -0.625039268 1.84386201 -0.36365730 -0.52291238 -0.71846622 1.11464855
#> [26,] -1.686693311 -0.65194990 0.05974994 0.68374552 -0.45033862 0.55004396
#> [27,] 0.837787044 0.23538657 -0.70459646 -0.06082195 2.39745248 1.23667580
#> [28,] 0.153373118 0.07796085 -0.71721816 0.63296071 0.01112919 0.13909786
#> [29,] -1.138136937 -0.96185663 0.88465050 1.33551762 1.63356842 0.41027510
#> [30,] 1.253814921 -0.07130809 -1.01559258 0.00729009 -1.43850664 -0.55845691
#> [31,] 0.426464221 1.44455086 1.95529397 1.01755864 -0.19051680 0.60537067
#> [32,] -0.295071483 0.45150405 -0.09031959 -1.18843404 0.37842390 -0.50633354
#> [33,] 0.895125661 0.04123292 0.21453883 -0.72160444 0.30003855 -1.42056550
#> [34,] 0.878133488 -0.42249683 -0.73852770 1.51921771 -1.00563626 0.12799297
#> [35,] 0.821581082 -2.05324722 -0.57438869 0.37738797 0.01925927 1.94585122
#> [36,] 0.688640254 1.13133721 -1.31701613 -2.05222282 -1.07742065 0.80091434
#> [37,] 0.553917654 -1.46064007 -0.18292539 -1.36403745 0.71270333 1.16525339
#> [38,] -0.061911711 0.73994751 0.41898240 -0.20078102 1.08477509 0.35885572
#> [39,] -0.305962664 1.90910357 0.32430434 0.86577940 -2.22498770 -0.60855718
#> [40,] -0.380471001 -1.44389316 -0.78153649 -0.10188326 1.23569346 -0.20224086
#> [41,] -0.694706979 0.70178434 -0.78862197 0.62418747 -1.24104450 -0.27324811
#> [42,] -0.207917278 -0.26219749 -0.50219872 0.95900538 0.45476927 -0.46869978
#> [43,] -1.265396352 -1.57214416 1.49606067 1.67105483 0.65990264 0.70416728
#> [44,] 2.168955965 -1.51466765 -1.13730362 0.05601673 -0.19988983 -1.19736350
#> [45,] 1.207961998 -1.60153617 -0.17905159 -0.05198191 -0.64511396 0.86636613
#> [46,] -1.123108583 -0.53090652 1.90236182 -1.75323736 0.16532102 0.86415249
#> [47,] -0.402884835 -1.46175558 -0.10097489 0.09932759 0.43881870 -1.19862236
#> [48,] -0.466655354 0.68791677 -1.35984070 -0.57185006 0.88330282 0.63949200
#> [49,] 0.779965118 2.10010894 -0.66476944 -0.97400958 -2.05233698 2.43022665
#> [50,] -0.083369066 -1.28703048 0.48545998 -0.17990623 -1.63637927 -0.55721548
#> [51,] 0.253318514 0.78773885 -0.37560287 1.01494317 1.43040234 0.84490424
#> [52,] -0.028546755 0.76904224 -0.56187636 -1.99274849 1.04662885 -0.78220185
#> [53,] -0.042870457 0.33220258 -0.34391723 -0.42727929 0.43528895 1.11071142
#> [54,] 1.368602284 -1.00837661 0.09049665 0.11663728 0.71517841 0.24982472
#> [55,] -0.225770986 -0.11945261 1.59850877 -0.89320757 0.91717492 1.65191539
#> [56,] 1.516470604 -0.28039534 -0.08856511 0.33390294 -2.66092280 -1.45897073
#> [57,] -1.548752804 0.56298953 1.08079950 0.41142992 1.11027710 -0.05129789
#> [58,] 0.584613750 -0.37243876 0.63075412 -0.03303616 -0.48498760 -0.52692518
#> [59,] 0.123854244 0.97697339 -0.11363990 -2.46589819 0.23061683 -0.19726487
#> [60,] 0.215941569 -0.37458086 -1.53290200 2.57145815 -0.29515780 -0.62957874
#> [61,] 0.379639483 1.05271147 -0.52111732 -0.20529926 0.87196495 -0.83384358
#> [62,] -0.502323453 -1.04917701 -0.48987045 0.65119328 -0.34847245 0.57872237
#> [63,] -0.333207384 -1.26015524 0.04715443 0.27376649 0.51850377 -1.08758071
#> [64,] -1.018575383 3.24103993 1.30019868 1.02467323 -0.39068498 1.48403093
#> [65,] -1.071791226 -0.41685759 2.29307897 0.81765945 -1.09278721 -1.18620659
#> [66,] 0.303528641 0.29822759 1.54758106 -0.20979317 1.21001051 0.10107915
#> [67,] 0.448209779 0.63656967 -0.13315096 0.37816777 0.74090001 0.53298929
#> [68,] 0.053004227 -0.48378063 -1.75652740 -0.94540883 1.72426224 0.58673534
#> [69,] 0.922267468 0.51686204 -0.38877986 0.85692301 0.06515393 -0.30174666
#> [70,] 2.050084686 0.36896453 0.08920722 -0.46103834 1.12500275 0.07950200
#> [71,] -0.491031166 -0.21538051 0.84501300 2.41677335 1.97541905 0.96126415
#> [72,] -2.309168876 0.06529303 0.96252797 -1.65104890 -0.28148212 -1.45646592
#> [73,] 1.005738524 -0.03406725 0.68430943 -0.46398724 -1.32295111 -0.78173971
#> [74,] -0.709200763 2.12845190 -1.39527435 0.82537986 -0.23935157 0.32040231
#> [75,] -0.688008616 -0.74133610 0.84964305 0.51013255 -0.21404124 -0.44478198
#> [76,] 1.025571370 -1.09599627 -0.44655722 -0.58948104 0.15168050 1.37000399
#> [77,] -0.284773007 0.03778840 0.17480270 -0.99678074 1.71230498 0.67325386
#> [78,] -1.220717712 0.31048075 0.07455118 0.14447570 -0.32614389 0.07216675
#> [79,] 0.181303480 0.43652348 0.42816676 -0.01430741 0.37300466 -1.50775732
#> [80,] -0.138891362 -0.45836533 0.02467498 -1.79028124 -0.22768406 0.02610023
#> [81,] 0.005764186 -1.06332613 -1.66747510 0.03455107 0.02045071 -0.31641587
#> [82,] 0.385280401 1.26318518 0.73649596 0.19023032 0.31405766 -0.10234651
#> [83,] -0.370660032 -0.34965039 0.38602657 0.17472640 1.32821470 -1.18155923
#> [84,] 0.644376549 -0.86551286 -0.26565163 -1.05501704 0.12131838 0.49865804
#> [85,] -0.220486562 -0.23627957 0.11814451 0.47613328 0.71284232 -1.03895644
#> [86,] 0.331781964 -0.19717589 0.13403865 1.37857014 0.77886003 -0.22622198
#> [87,] 1.096839013 1.10992029 0.22101947 0.45623640 0.91477327 0.38142583
#> [88,] 0.435181491 0.08473729 1.64084617 -1.13558847 -0.57439455 -0.78351579
#> [89,] -0.325931586 0.75405379 -0.21905038 -0.43564547 1.62688121 0.58299141
#> [90,] 1.148807618 -0.49929202 0.16806538 0.34610362 -0.38095674 -1.31651040
#> [91,] 0.993503856 0.21444531 1.16838387 -0.64704563 -0.10578417 -2.80977468
#> [92,] 0.548396960 -0.32468591 1.05418102 -2.15764634 1.40405027 0.46496799
#> [93,] 0.238731735 0.09458353 1.14526311 0.88425082 1.29408391 0.84053983
#> [94,] -0.627906076 -0.89536336 -0.57746800 -0.82947761 -1.08999187 -0.28584542
#> [95,] 1.360652449 -1.31080153 2.00248273 -0.57356027 -0.87307100 0.50412625
#> [96,] -0.600259587 1.99721338 0.06670087 1.50390061 -1.35807906 -1.15591653
#> [97,] 2.187332993 0.60070882 1.86685184 -0.77414493 0.18184719 -0.12714861
#> [98,] 1.532610626 -1.25127136 -1.35090269 0.84573154 0.16484087 -1.94151838
#> [99,] -0.235700359 -0.61116592 0.02098359 -1.26068288 0.36411469 1.18118089
#> [100,] -1.026420900 -1.18548008 1.24991457 -0.35454240 0.55215771 1.85991086
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 1.07401226 -0.728219111 0.35628334 -1.014114173 -0.99579872
#> [2,] -0.02734697 -1.540442405 -0.65801021 -0.791313879 -1.03995504
#> [3,] -0.03333034 -0.693094614 0.85520221 0.299593685 -0.01798024
#> [4,] -1.51606762 0.118849433 1.15293623 1.639051909 -0.13217513
#> [5,] 0.79038534 -1.364709458 0.27627456 1.084617009 -2.54934277
#> [6,] -0.21073418 0.589982679 0.14410466 -0.624567474 1.04057346
#> [7,] -0.65674293 0.289344029 -0.07562508 0.825922902 0.24972574
#> [8,] -1.41202579 -0.904215026 2.16141585 -0.048568353 2.41620737
#> [9,] -0.29976250 0.226324942 0.27631553 0.301313652 0.68519824
#> [10,] -0.84906114 0.748081162 -0.15829403 0.260361491 -0.44695931
#> [11,] -0.39703052 1.061095253 -2.50791780 2.575449764 2.79739115
#> [12,] -1.21759999 -0.212848279 -1.56528177 -1.185288811 2.83222602
#> [13,] 1.68758948 -0.093636794 -0.07767320 0.100919859 -1.21871182
#> [14,] -0.01600253 -0.086714135 0.20629404 -1.779977288 0.46903196
#> [15,] 1.07494508 1.441461756 0.27687246 0.589835923 -0.21124692
#> [16,] -2.60169967 1.125071892 0.82150678 1.096608472 0.18705115
#> [17,] -0.45319783 0.834401568 -0.19415241 1.445662241 0.22754273
#> [18,] -0.67548229 -0.287340800 1.21458879 -1.925145252 -1.26190046
#> [19,] -1.22292618 0.373241434 -0.92151604 0.412769497 0.28558958
#> [20,] 1.54660915 0.403290331 -1.20844272 1.593369951 1.74924736
#> [21,] -1.41528192 -1.041673294 -1.22898618 -0.414015863 -0.16409000
#> [22,] 0.31839026 -1.728304515 0.74229702 -0.212150532 -0.16292671
#> [23,] 0.84643629 0.641830028 -0.08291994 -0.036537222 1.39857201
#> [24,] 0.17819019 -1.529310531 0.78981792 0.365018751 0.89839624
#> [25,] -0.87525548 0.001683688 -0.26770642 0.665159876 -1.64849482
#> [26,] 0.94116581 0.250247821 -0.59189210 1.317820884 0.22855697
#> [27,] 0.17058808 0.563867390 -0.36835258 -0.095487590 1.65354723
#> [28,] -1.06349791 0.189426238 -1.85261682 0.196278045 1.41527635
#> [29,] -1.38804905 -0.732853806 -1.16961526 2.487997877 0.41995160
#> [30,] 2.08671743 0.986365860 -1.44203465 0.431098928 0.72122081
#> [31,] -0.67850315 1.738633767 1.05432227 0.188753109 -1.19693521
#> [32,] -1.85557165 0.881178809 -0.59733009 -1.342243125 0.30013157
#> [33,] 0.53325936 -1.943650901 0.78945985 0.002856048 -0.95444894
#> [34,] 0.31023026 1.399576185 1.51649060 -0.221326153 -0.45801807
#> [35,] -1.35383434 -0.056055946 -0.19177481 -0.011045830 0.93560368
#> [36,] -1.94295641 0.524914279 0.28387891 -0.575417641 -1.13689311
#> [37,] -0.11630252 0.622033236 -1.75106752 -0.686815652 0.26691825
#> [38,] 1.13939629 -0.096686073 -0.81866978 -0.720773632 0.42833204
#> [39,] 0.63612404 -0.075263198 0.05621485 -0.214504515 0.05491197
#> [40,] -0.49293742 1.019157069 0.29908690 1.368132648 1.82218882
#> [41,] -0.83418823 0.711601922 -0.75939812 1.049086627 -1.02234733
#> [42,] 0.27106676 0.990262246 2.68485900 -0.359975118 0.60613026
#> [43,] 0.15735335 2.382926695 -0.45839014 -1.685916455 -0.08893057
#> [44,] 0.62971175 0.664415864 0.06424356 -0.844583429 -0.26083224
#> [45,] -0.39579795 0.207381157 0.64979187 -0.457760533 0.46409123
#> [46,] 0.89935405 -2.210633111 -0.02601863 0.103638004 -1.02040059
#> [47,] -0.83081153 2.691714003 -0.64356739 -0.662607276 -1.31345092
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#> [22,] 3.290517443 0.20963283 1.496821710 1.09524438 1.10970808
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#> [12,] 1.61863074 1.294261816 -1.09939100 0.81998237 1.52787010 0.05249805
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#> [18,] 1.26449613 0.309441562 -1.97326132 -1.71937580 -0.85854930 0.45037717
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#> [30,] -0.87844186 -0.364461374 -0.83452283 -1.15841660 0.05810584 2.50176510
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#> [37,] 0.89850083 0.782087200 0.55294765 1.25495610 1.08664197 0.56189912
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#> [53,] 0.59313010 0.687815079 0.43559544 0.49782615 -0.58690024 -0.96838658
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#> [93,] 0.7987937110 -0.19727020
#> [94,] 0.2169247894 -1.94694948
#> [95,] -0.0689971963 -1.42763817
#> [96,] 1.6284169621 -0.85041804
#> [97,] -2.4916869814 1.62446909
#> [98,] 0.9929091010 -0.12663816
#> [99,] -0.1676952820 1.27560203
#> [100,] -1.1271011796 0.17949618
#>
#> $missing.data
#> $missing.data[[1]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[2]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[3]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#>
#> $imputation.models
#> $imputation.models[[1]]
#>
#> Call: cv.glmnet(x = x_values, y = y_values, nfolds = mcontrol$nfolds.imputation)
#>
#> Measure: Mean-Squared Error
#>
#> Lambda Index Measure SE Nonzero
#> min 0.5182 1 7.377 1.137 0
#> 1se 0.5182 1 7.377 1.137 0
#>
#> $imputation.models[[2]]
#>
#> Call: cv.glmnet(x = x_values, y = y_values, nfolds = mcontrol$nfolds.imputation)
#>
#> Measure: Mean-Squared Error
#>
#> Lambda Index Measure SE Nonzero
#> min 0.01696 50 0.009137 0.001223 10
#> 1se 0.01861 49 0.009683 0.001397 10
#>
#> $imputation.models[[3]]
#>
#> Call: cv.glmnet(x = x_values, y = y_values, nfolds = mcontrol$nfolds.imputation)
#>
#> Measure: Mean-Squared Error
#>
#> Lambda Index Measure SE Nonzero
#> min 0.0246 46 0.01031 0.003293 12
#> 1se 0.0270 45 0.01221 0.003990 12
#>
#>
#> $blocks.used.for.imputation
#> $blocks.used.for.imputation[[1]]
#> [1] 2 3
#>
#> $blocks.used.for.imputation[[2]]
#> [1] 1 3
#>
#> $blocks.used.for.imputation[[3]]
#> [1] 1 2
#>
#>
#> $missingness.pattern
#> $missingness.pattern[[1]]
#> [1] TRUE FALSE FALSE
#>
#> $missingness.pattern[[2]]
#> [1] FALSE TRUE FALSE
#>
#> $missingness.pattern[[3]]
#> [1] FALSE FALSE TRUE
#>
#>
#> $y.scale.param
#> NULL
#>
#> $blocks
#> $blocks$block1
#> [1] 1 2 3 4 5 6 7 8 9 10
#>
#> $blocks$block2
#> [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $blocks$block3
#> [1] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#>
#> $mcontrol
#> $handle.missingdata
#> [1] "impute.offset"
#>
#> $offset.firstblock
#> [1] "zero"
#>
#> $impute.offset.cases
#> [1] "complete.cases"
#>
#> $nfolds.imputation
#> [1] 5
#>
#> $lambda.imputation
#> [1] "lambda.min"
#>
#> $perc.comp.cases.warning
#> [1] 0.3
#>
#> $threshold.available.cases
#> [1] 30
#>
#> $select.available.cases
#> [1] "maximise.blocks"
#>
#> attr(,"class")
#> [1] "pl.missing.control" "list"
#>
#> $family
#> [1] "gaussian"
#>
#> $dim.x
#> [1] 100 50
#>
#> $pred
#> s1
#> [1,] -3.55767537
#> [2,] -3.31760508
#> [3,] -2.90809983
#> [4,] -4.42666459
#> [5,] -1.43734017
#> [6,] -1.10554932
#> [7,] -4.69661232
#> [8,] -2.23049730
#> [9,] -0.67608465
#> [10,] 4.17050747
#> [11,] 1.10984544
#> [12,] 2.01892292
#> [13,] -3.93846244
#> [14,] -0.12211884
#> [15,] 1.38693641
#> [16,] -5.99408992
#> [17,] -0.46940666
#> [18,] -0.86515803
#> [19,] 2.29636441
#> [20,] 1.99232693
#> [21,] 3.17149689
#> [22,] 0.08930242
#> [23,] -2.61350734
#> [24,] 1.03269329
#> [25,] 0.99606006
#> [26,] 0.99318133
#> [27,] 3.73766694
#> [28,] 1.77471693
#> [29,] 0.75042405
#> [30,] 0.91385357
#> [31,] 0.69943732
#> [32,] -1.23878814
#> [33,] -2.11914529
#> [34,] 1.33072888
#> [35,] -1.84458867
#> [36,] -2.64263012
#> [37,] -1.81037076
#> [38,] 2.60974341
#> [39,] 1.55455527
#> [40,] -0.29071008
#> [41,] 1.04590210
#> [42,] 1.67030800
#> [43,] 1.83590837
#> [44,] -1.11078692
#> [45,] -2.90077855
#> [46,] -4.15606455
#> [47,] -0.78522139
#> [48,] 1.28357487
#> [49,] 0.55483414
#> [50,] -2.96754736
#> [51,] 3.44183003
#> [52,] -0.31511013
#> [53,] 1.08808374
#> [54,] -1.28886018
#> [55,] -2.32953173
#> [56,] -3.25671480
#> [57,] 3.32984705
#> [58,] -1.63264023
#> [59,] -1.75492566
#> [60,] 3.56321468
#> [61,] 2.14105575
#> [62,] 0.47100470
#> [63,] 1.14927756
#> [64,] 4.56328557
#> [65,] -3.89751996
#> [66,] -1.14047549
#> [67,] 3.87548499
#> [68,] 1.77297154
#> [69,] 2.63038329
#> [70,] 0.58804585
#> [71,] 4.81300332
#> [72,] -2.84100278
#> [73,] -2.15691495
#> [74,] 4.71534413
#> [75,] -1.75331412
#> [76,] -2.83838207
#> [77,] -0.33303215
#> [78,] -0.85239537
#> [79,] -0.85046727
#> [80,] -4.00575555
#> [81,] -0.07641106
#> [82,] 2.74437063
#> [83,] 0.44703259
#> [84,] -3.38804118
#> [85,] -0.86047782
#> [86,] 2.11842654
#> [87,] 2.83121370
#> [88,] -4.45752020
#> [89,] 2.46289316
#> [90,] -3.72604956
#> [91,] -2.95903306
#> [92,] -2.65606059
#> [93,] 2.97189378
#> [94,] -3.67006013
#> [95,] -6.97307336
#> [96,] 5.24351414
#> [97,] -3.51818773
#> [98,] 0.61373086
#> [99,] -1.46231558
#> [100,] -2.59234610
#>
#> $actuals
#> [,1]
#> [1,] -4.320199229
#> [2,] -2.145050089
#> [3,] -2.417788193
#> [4,] -4.417505678
#> [5,] -2.659050504
#> [6,] -0.936684634
#> [7,] -5.387087465
#> [8,] -3.057359036
#> [9,] 1.090943326
#> [10,] 2.767568537
#> [11,] 0.296284884
#> [12,] 1.449905790
#> [13,] -4.743973252
#> [14,] -1.092945685
#> [15,] 1.288868616
#> [16,] -7.139407664
#> [17,] 0.935394039
#> [18,] -0.492059784
#> [19,] 3.251529435
#> [20,] 0.889970862
#> [21,] 3.276574344
#> [22,] 0.478397584
#> [23,] -2.655299692
#> [24,] 0.641837880
#> [25,] 0.100210428
#> [26,] 0.589233025
#> [27,] 3.185856430
#> [28,] 1.214770377
#> [29,] 1.560368758
#> [30,] 2.626714387
#> [31,] 1.453961044
#> [32,] -0.560509889
#> [33,] -2.473335868
#> [34,] 1.745593848
#> [35,] -2.322276055
#> [36,] -1.986164598
#> [37,] -1.442586898
#> [38,] 0.104824201
#> [39,] 3.267139834
#> [40,] -1.641272627
#> [41,] -1.246306746
#> [42,] 1.343464266
#> [43,] 1.919254204
#> [44,] -1.226210691
#> [45,] -2.753044533
#> [46,] -5.523246057
#> [47,] -0.007445442
#> [48,] 2.678443011
#> [49,] -0.321469775
#> [50,] -2.537900294
#> [51,] 3.016202621
#> [52,] -0.361412345
#> [53,] 1.850127170
#> [54,] -1.081278725
#> [55,] -0.775000884
#> [56,] -3.402119142
#> [57,] 4.620760220
#> [58,] -2.239158005
#> [59,] -2.117775424
#> [60,] 3.204390100
#> [61,] 2.029905859
#> [62,] 0.997897180
#> [63,] 1.173641110
#> [64,] 4.933191909
#> [65,] -4.717716401
#> [66,] -1.945906153
#> [67,] 4.563316002
#> [68,] 2.790612536
#> [69,] 2.662359340
#> [70,] 0.723182070
#> [71,] 5.435135820
#> [72,] -2.068046736
#> [73,] -2.377080276
#> [74,] 4.625818695
#> [75,] -1.330741150
#> [76,] -3.200455008
#> [77,] -0.557272238
#> [78,] -0.955805921
#> [79,] -2.700197509
#> [80,] -3.440214452
#> [81,] 0.624757094
#> [82,] 2.432717701
#> [83,] 1.990184192
#> [84,] -3.630464548
#> [85,] -0.332671471
#> [86,] 2.372909676
#> [87,] 3.399604568
#> [88,] -4.175389619
#> [89,] 2.248719212
#> [90,] -3.110653465
#> [91,] -3.094141999
#> [92,] -2.742008980
#> [93,] 3.379887452
#> [94,] -2.716828776
#> [95,] -7.173844818
#> [96,] 4.602369405
#> [97,] -2.728815268
#> [98,] 0.661930236
#> [99,] -1.808280650
#> [100,] -3.424192429
#>
#> $adaptive
#> [1] FALSE
#>
#> $adaptive_weights
#> NULL
#>
#> $initial_coeff
#> NULL
#>
#> $initial_weight_scope
#> [1] "global"
#>
#> attr(,"class")
#> [1] "priorityelasticnet" "list"The output will include information on how the missing data was handled, the imputation models used (if applicable), and the overall model fit. By inspecting these details, you can assess whether the chosen missing data strategy effectively maintained the integrity of your analysis.
Custom Strategies for Handling Missing Data
The priorityelasticnet function also allows for more customized strategies via the mcontrol argument. For example, you can set specific parameters for imputation, such as the number of folds used for cross-validation during imputation (nfolds.imputation) or thresholds for the percentage of complete cases required (perc.comp.cases.warning). These options enable a tailored approach to missing data, ensuring that your model is both robust and accurate.
Moreover, priorityelasticnet supports different imputation methods, including mean imputation, median imputation, and more complex model-based imputations. This versatility allows you to adapt the model to the specific characteristics of your dataset, whether you’re dealing with large gaps in the data, patterns of missingness, or particular concerns about bias.
In summary, the priorityelasticnet function’s handling of missing data is highly flexible, allowing you to choose and customize strategies that best suit your analysis. Whether you opt for simple offset imputation or more complex approaches, the key is to maintain the integrity of your data while ensuring that your model remains robust and interpretable, even in the presence of missing values.
Cross-Validation and Model Selection
The cvm_priorityelasticnet function is a powerful tool for comparing different block configurations and selecting the optimal model based on cross-validation error. This functionality is particularly valuable when dealing with complex datasets where the structure of the predictor variables can significantly impact model performance.
In the following example, we demonstrate how to use the cvm_priorityelasticnet function to evaluate and compare different block configurations. The data for this demonstration is derived from a Gaussian model.
blocks1 <- list(1:10, 11:30, 31:50)
blocks2 <- list(1:5, 6:20, 21:50)
fit_cvm <-
cvm_priorityelasticnet(
X,
Y,
blocks.list = list(blocks1, blocks2),
family = "gaussian",
type.measure = "mse",
weights = NULL,
foldid = NULL
)In this example, we define two different block configurations, blocks1 and blocks2, and pass them to the cvm_priorityelasticnet function. The function then performs cross-validation on each configuration, calculating the mean squared error (MSE) for each model. By comparing these MSE values, you can identify which block configuration yields the best predictive performance.
fit_cvm
#> $lambda.ind
#> $lambda.ind[[1]]
#> [1] 58
#>
#> $lambda.ind[[2]]
#> [1] 6
#>
#> $lambda.ind[[3]]
#> [1] 1
#>
#>
#> $lambda.type
#> [1] "lambda.min"
#>
#> $lambda.min
#> $lambda.min[[1]]
#> [1] 0.00812479
#>
#> $lambda.min[[2]]
#> [1] 0.1193552
#>
#> $lambda.min[[3]]
#> [1] 0.1215803
#>
#>
#> $min.cvm
#> $min.cvm[[1]]
#> [1] 0.8735205
#>
#> $min.cvm[[2]]
#> [1] 0.7325674
#>
#> $min.cvm[[3]]
#> [1] 0.7146441
#>
#>
#> $nzero
#> $nzero[[1]]
#> [1] 10
#>
#> $nzero[[2]]
#> [1] 2
#>
#> $nzero[[3]]
#> [1] 0
#>
#>
#> $glmnet.fit
#> $glmnet.fit[[1]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 1.63200
#> 2 1 5.81 1.48700
#> 3 1 10.63 1.35500
#> 4 1 14.63 1.23500
#> 5 2 19.78 1.12500
#> 6 2 25.06 1.02500
#> 7 4 31.53 0.93420
#> 8 4 39.46 0.85120
#> 9 4 46.05 0.77560
#> 10 5 51.94 0.70670
#> 11 5 57.56 0.64390
#> 12 5 62.21 0.58670
#> 13 5 66.08 0.53460
#> 14 5 69.29 0.48710
#> 15 6 72.18 0.44380
#> 16 6 74.60 0.40440
#> 17 7 76.65 0.36840
#> 18 8 78.57 0.33570
#> 19 8 80.32 0.30590
#> 20 9 81.91 0.27870
#> 21 9 83.35 0.25400
#> 22 9 84.54 0.23140
#> 23 9 85.52 0.21080
#> 24 10 86.40 0.19210
#> 25 10 87.15 0.17500
#> 26 10 87.78 0.15950
#> 27 10 88.29 0.14530
#> 28 10 88.72 0.13240
#> 29 10 89.08 0.12070
#> 30 10 89.38 0.10990
#> 31 10 89.62 0.10020
#> 32 10 89.83 0.09127
#> 33 10 90.00 0.08316
#> 34 10 90.14 0.07577
#> 35 10 90.25 0.06904
#> 36 10 90.35 0.06291
#> 37 10 90.43 0.05732
#> 38 10 90.50 0.05223
#> 39 10 90.55 0.04759
#> 40 10 90.60 0.04336
#> 41 10 90.64 0.03951
#> 42 10 90.67 0.03600
#> 43 10 90.70 0.03280
#> 44 10 90.72 0.02989
#> 45 10 90.74 0.02723
#> 46 10 90.75 0.02481
#> 47 10 90.76 0.02261
#> 48 10 90.77 0.02060
#> 49 10 90.78 0.01877
#> 50 10 90.79 0.01710
#> 51 10 90.80 0.01558
#> 52 10 90.80 0.01420
#> 53 10 90.81 0.01294
#> 54 10 90.81 0.01179
#> 55 10 90.81 0.01074
#> 56 10 90.81 0.00979
#> 57 10 90.82 0.00892
#> 58 10 90.82 0.00812
#> 59 10 90.82 0.00740
#> 60 10 90.82 0.00674
#> 61 10 90.82 0.00615
#> 62 10 90.82 0.00560
#>
#> $glmnet.fit[[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.190000
#> 2 2 1.24 0.173200
#> 3 2 2.52 0.157800
#> 4 2 3.58 0.143800
#> 5 2 4.46 0.131000
#> 6 2 5.20 0.119400
#> 7 2 5.80 0.108800
#> 8 2 6.31 0.099090
#> 9 4 7.14 0.090290
#> 10 4 7.90 0.082270
#> 11 4 8.53 0.074960
#> 12 5 9.11 0.068300
#> 13 7 9.70 0.062230
#> 14 7 10.27 0.056700
#> 15 7 10.75 0.051670
#> 16 9 11.17 0.047080
#> 17 9 11.71 0.042890
#> 18 9 12.16 0.039080
#> 19 11 12.62 0.035610
#> 20 13 13.15 0.032450
#> 21 14 13.62 0.029570
#> 22 16 14.05 0.026940
#> 23 16 14.42 0.024550
#> 24 16 14.72 0.022360
#> 25 17 14.99 0.020380
#> 26 18 15.25 0.018570
#> 27 18 15.46 0.016920
#> 28 18 15.63 0.015420
#> 29 18 15.77 0.014050
#> 30 18 15.89 0.012800
#> 31 18 15.99 0.011660
#> 32 18 16.08 0.010630
#> 33 19 16.15 0.009681
#> 34 19 16.20 0.008821
#> 35 19 16.25 0.008038
#> 36 19 16.29 0.007324
#> 37 19 16.32 0.006673
#> 38 19 16.35 0.006080
#> 39 19 16.38 0.005540
#> 40 19 16.39 0.005048
#> 41 19 16.41 0.004599
#> 42 19 16.42 0.004191
#> 43 19 16.43 0.003818
#> 44 19 16.44 0.003479
#> 45 19 16.45 0.003170
#> 46 19 16.46 0.002889
#> 47 19 16.46 0.002632
#> 48 19 16.47 0.002398
#> 49 19 16.47 0.002185
#> 50 19 16.47 0.001991
#> 51 19 16.47 0.001814
#> 52 19 16.48 0.001653
#> 53 20 16.48 0.001506
#> 54 20 16.48 0.001372
#> 55 20 16.48 0.001250
#> 56 20 16.48 0.001139
#> 57 20 16.48 0.001038
#> 58 20 16.48 0.000946
#> 59 20 16.48 0.000862
#> 60 20 16.48 0.000785
#> 61 20 16.49 0.000716
#> 62 20 16.49 0.000652
#> 63 20 16.49 0.000594
#> 64 20 16.49 0.000541
#> 65 20 16.49 0.000493
#> 66 20 16.49 0.000449
#>
#> $glmnet.fit[[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.121600
#> 2 3 0.66 0.110800
#> 3 4 1.85 0.100900
#> 4 5 3.06 0.091970
#> 5 8 4.62 0.083800
#> 6 8 6.01 0.076360
#> 7 8 7.16 0.069570
#> 8 9 8.12 0.063390
#> 9 9 9.00 0.057760
#> 10 10 9.83 0.052630
#> 11 11 10.62 0.047950
#> 12 12 11.35 0.043690
#> 13 12 12.00 0.039810
#> 14 14 12.63 0.036280
#> 15 14 13.17 0.033050
#> 16 14 13.61 0.030120
#> 17 14 13.98 0.027440
#> 18 15 14.30 0.025000
#> 19 15 14.61 0.022780
#> 20 16 14.87 0.020760
#> 21 16 15.11 0.018910
#> 22 16 15.30 0.017230
#> 23 16 15.46 0.015700
#> 24 17 15.60 0.014310
#> 25 17 15.72 0.013040
#> 26 17 15.82 0.011880
#> 27 18 15.91 0.010820
#> 28 18 15.98 0.009862
#> 29 19 16.05 0.008986
#> 30 19 16.11 0.008187
#> 31 19 16.16 0.007460
#> 32 19 16.20 0.006797
#> 33 19 16.23 0.006193
#> 34 19 16.26 0.005643
#> 35 19 16.28 0.005142
#> 36 19 16.30 0.004685
#> 37 19 16.32 0.004269
#> 38 19 16.33 0.003890
#> 39 19 16.34 0.003544
#> 40 20 16.35 0.003229
#> 41 20 16.36 0.002942
#> 42 20 16.37 0.002681
#> 43 20 16.37 0.002443
#> 44 20 16.38 0.002226
#> 45 20 16.38 0.002028
#> 46 20 16.38 0.001848
#> 47 20 16.39 0.001684
#> 48 20 16.39 0.001534
#> 49 20 16.39 0.001398
#> 50 20 16.39 0.001274
#> 51 20 16.39 0.001161
#> 52 20 16.40 0.001057
#> 53 20 16.40 0.000963
#> 54 20 16.40 0.000878
#> 55 20 16.40 0.000800
#> 56 20 16.40 0.000729
#> 57 20 16.40 0.000664
#> 58 20 16.40 0.000605
#> 59 20 16.40 0.000551
#> 60 20 16.40 0.000502
#> 61 20 16.40 0.000458
#> 62 20 16.40 0.000417
#> 63 20 16.40 0.000380
#>
#>
#> $name
#> mse
#> "Mean-Squared Error"
#>
#> $block1unpen
#> NULL
#>
#> $best.blocks
#> [1] "bp1 = 1:10" "bp2 = 11:30" "bp3 = 31:50"
#>
#> $best.blocks.indices
#> $best.blocks.indices[[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10
#>
#> $best.blocks.indices[[2]]
#> [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $best.blocks.indices[[3]]
#> [1] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#>
#> $best.max.coef
#> NULL
#>
#> $best.model
#> $lambda.ind
#> $lambda.ind[[1]]
#> [1] 58
#>
#> $lambda.ind[[2]]
#> [1] 6
#>
#> $lambda.ind[[3]]
#> [1] 1
#>
#>
#> $lambda.type
#> [1] "lambda.min"
#>
#> $lambda.min
#> $lambda.min[[1]]
#> [1] 0.00812479
#>
#> $lambda.min[[2]]
#> [1] 0.1193552
#>
#> $lambda.min[[3]]
#> [1] 0.1215803
#>
#>
#> $min.cvm
#> $min.cvm[[1]]
#> [1] 0.8735205
#>
#> $min.cvm[[2]]
#> [1] 0.7325674
#>
#> $min.cvm[[3]]
#> [1] 0.7146441
#>
#>
#> $nzero
#> $nzero[[1]]
#> [1] 10
#>
#> $nzero[[2]]
#> [1] 2
#>
#> $nzero[[3]]
#> [1] 0
#>
#>
#> $glmnet.fit
#> $glmnet.fit[[1]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 1.63200
#> 2 1 5.81 1.48700
#> 3 1 10.63 1.35500
#> 4 1 14.63 1.23500
#> 5 2 19.78 1.12500
#> 6 2 25.06 1.02500
#> 7 4 31.53 0.93420
#> 8 4 39.46 0.85120
#> 9 4 46.05 0.77560
#> 10 5 51.94 0.70670
#> 11 5 57.56 0.64390
#> 12 5 62.21 0.58670
#> 13 5 66.08 0.53460
#> 14 5 69.29 0.48710
#> 15 6 72.18 0.44380
#> 16 6 74.60 0.40440
#> 17 7 76.65 0.36840
#> 18 8 78.57 0.33570
#> 19 8 80.32 0.30590
#> 20 9 81.91 0.27870
#> 21 9 83.35 0.25400
#> 22 9 84.54 0.23140
#> 23 9 85.52 0.21080
#> 24 10 86.40 0.19210
#> 25 10 87.15 0.17500
#> 26 10 87.78 0.15950
#> 27 10 88.29 0.14530
#> 28 10 88.72 0.13240
#> 29 10 89.08 0.12070
#> 30 10 89.38 0.10990
#> 31 10 89.62 0.10020
#> 32 10 89.83 0.09127
#> 33 10 90.00 0.08316
#> 34 10 90.14 0.07577
#> 35 10 90.25 0.06904
#> 36 10 90.35 0.06291
#> 37 10 90.43 0.05732
#> 38 10 90.50 0.05223
#> 39 10 90.55 0.04759
#> 40 10 90.60 0.04336
#> 41 10 90.64 0.03951
#> 42 10 90.67 0.03600
#> 43 10 90.70 0.03280
#> 44 10 90.72 0.02989
#> 45 10 90.74 0.02723
#> 46 10 90.75 0.02481
#> 47 10 90.76 0.02261
#> 48 10 90.77 0.02060
#> 49 10 90.78 0.01877
#> 50 10 90.79 0.01710
#> 51 10 90.80 0.01558
#> 52 10 90.80 0.01420
#> 53 10 90.81 0.01294
#> 54 10 90.81 0.01179
#> 55 10 90.81 0.01074
#> 56 10 90.81 0.00979
#> 57 10 90.82 0.00892
#> 58 10 90.82 0.00812
#> 59 10 90.82 0.00740
#> 60 10 90.82 0.00674
#> 61 10 90.82 0.00615
#> 62 10 90.82 0.00560
#>
#> $glmnet.fit[[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.190000
#> 2 2 1.24 0.173200
#> 3 2 2.52 0.157800
#> 4 2 3.58 0.143800
#> 5 2 4.46 0.131000
#> 6 2 5.20 0.119400
#> 7 2 5.80 0.108800
#> 8 2 6.31 0.099090
#> 9 4 7.14 0.090290
#> 10 4 7.90 0.082270
#> 11 4 8.53 0.074960
#> 12 5 9.11 0.068300
#> 13 7 9.70 0.062230
#> 14 7 10.27 0.056700
#> 15 7 10.75 0.051670
#> 16 9 11.17 0.047080
#> 17 9 11.71 0.042890
#> 18 9 12.16 0.039080
#> 19 11 12.62 0.035610
#> 20 13 13.15 0.032450
#> 21 14 13.62 0.029570
#> 22 16 14.05 0.026940
#> 23 16 14.42 0.024550
#> 24 16 14.72 0.022360
#> 25 17 14.99 0.020380
#> 26 18 15.25 0.018570
#> 27 18 15.46 0.016920
#> 28 18 15.63 0.015420
#> 29 18 15.77 0.014050
#> 30 18 15.89 0.012800
#> 31 18 15.99 0.011660
#> 32 18 16.08 0.010630
#> 33 19 16.15 0.009681
#> 34 19 16.20 0.008821
#> 35 19 16.25 0.008038
#> 36 19 16.29 0.007324
#> 37 19 16.32 0.006673
#> 38 19 16.35 0.006080
#> 39 19 16.38 0.005540
#> 40 19 16.39 0.005048
#> 41 19 16.41 0.004599
#> 42 19 16.42 0.004191
#> 43 19 16.43 0.003818
#> 44 19 16.44 0.003479
#> 45 19 16.45 0.003170
#> 46 19 16.46 0.002889
#> 47 19 16.46 0.002632
#> 48 19 16.47 0.002398
#> 49 19 16.47 0.002185
#> 50 19 16.47 0.001991
#> 51 19 16.47 0.001814
#> 52 19 16.48 0.001653
#> 53 20 16.48 0.001506
#> 54 20 16.48 0.001372
#> 55 20 16.48 0.001250
#> 56 20 16.48 0.001139
#> 57 20 16.48 0.001038
#> 58 20 16.48 0.000946
#> 59 20 16.48 0.000862
#> 60 20 16.48 0.000785
#> 61 20 16.49 0.000716
#> 62 20 16.49 0.000652
#> 63 20 16.49 0.000594
#> 64 20 16.49 0.000541
#> 65 20 16.49 0.000493
#> 66 20 16.49 0.000449
#>
#> $glmnet.fit[[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.121600
#> 2 3 0.66 0.110800
#> 3 4 1.85 0.100900
#> 4 5 3.06 0.091970
#> 5 8 4.62 0.083800
#> 6 8 6.01 0.076360
#> 7 8 7.16 0.069570
#> 8 9 8.12 0.063390
#> 9 9 9.00 0.057760
#> 10 10 9.83 0.052630
#> 11 11 10.62 0.047950
#> 12 12 11.35 0.043690
#> 13 12 12.00 0.039810
#> 14 14 12.63 0.036280
#> 15 14 13.17 0.033050
#> 16 14 13.61 0.030120
#> 17 14 13.98 0.027440
#> 18 15 14.30 0.025000
#> 19 15 14.61 0.022780
#> 20 16 14.87 0.020760
#> 21 16 15.11 0.018910
#> 22 16 15.30 0.017230
#> 23 16 15.46 0.015700
#> 24 17 15.60 0.014310
#> 25 17 15.72 0.013040
#> 26 17 15.82 0.011880
#> 27 18 15.91 0.010820
#> 28 18 15.98 0.009862
#> 29 19 16.05 0.008986
#> 30 19 16.11 0.008187
#> 31 19 16.16 0.007460
#> 32 19 16.20 0.006797
#> 33 19 16.23 0.006193
#> 34 19 16.26 0.005643
#> 35 19 16.28 0.005142
#> 36 19 16.30 0.004685
#> 37 19 16.32 0.004269
#> 38 19 16.33 0.003890
#> 39 19 16.34 0.003544
#> 40 20 16.35 0.003229
#> 41 20 16.36 0.002942
#> 42 20 16.37 0.002681
#> 43 20 16.37 0.002443
#> 44 20 16.38 0.002226
#> 45 20 16.38 0.002028
#> 46 20 16.38 0.001848
#> 47 20 16.39 0.001684
#> 48 20 16.39 0.001534
#> 49 20 16.39 0.001398
#> 50 20 16.39 0.001274
#> 51 20 16.39 0.001161
#> 52 20 16.40 0.001057
#> 53 20 16.40 0.000963
#> 54 20 16.40 0.000878
#> 55 20 16.40 0.000800
#> 56 20 16.40 0.000729
#> 57 20 16.40 0.000664
#> 58 20 16.40 0.000605
#> 59 20 16.40 0.000551
#> 60 20 16.40 0.000502
#> 61 20 16.40 0.000458
#> 62 20 16.40 0.000417
#> 63 20 16.40 0.000380
#>
#>
#> $name
#> mse
#> "Mean-Squared Error"
#>
#> $block1unpen
#> NULL
#>
#> $coefficients
#> V1 V2 V3 V4 V5 V6
#> -0.37871307 1.19409872 -1.00410323 1.54075278 1.01087379 0.30506734
#> V7 V8 V9 V10 V1 V2
#> 0.66328904 0.19371876 -0.35580789 -0.33068763 0.00000000 0.00000000
#> V3 V4 V5 V6 V7 V8
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V9 V10 V11 V12 V13 V14
#> 0.00000000 -0.05948594 0.00000000 0.00000000 -0.06206454 0.00000000
#> V15 V16 V17 V18 V19 V20
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V1 V2 V3 V4 V5 V6
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V7 V8 V9 V10 V11 V12
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V13 V14 V15 V16 V17 V18
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V19 V20
#> 0.00000000 0.00000000
#>
#> $call
#> priorityelasticnet(X = X, Y = Y, weights = weights, family = family,
#> alpha = alpha, type.measure = type.measure, blocks = blocks.list[[j]],
#> max.coef = max.coef.list[[j]], block1.penalization = block1.penalization,
#> lambda.type = lambda.type, standardize = standardize, nfolds = nfolds,
#> foldid = foldid, cvoffset = cvoffset, cvoffsetnfolds = cvoffsetnfolds)
#>
#> $X
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.560475647 -0.71040656 2.19881035 -0.71524219 -0.07355602 -0.60189285
#> [2,] -0.230177489 0.25688371 1.31241298 -0.75268897 -1.16865142 -0.99369859
#> [3,] 1.558708314 -0.24669188 -0.26514506 -0.93853870 -0.63474826 1.02678506
#> [4,] 0.070508391 -0.34754260 0.54319406 -1.05251328 -0.02884155 0.75106130
#> [5,] 0.129287735 -0.95161857 -0.41433995 -0.43715953 0.67069597 -1.50916654
#> [6,] 1.715064987 -0.04502772 -0.47624689 0.33117917 -1.65054654 -0.09514745
#> [7,] 0.460916206 -0.78490447 -0.78860284 -2.01421050 -0.34975424 -0.89594782
#> [8,] -1.265061235 -1.66794194 -0.59461727 0.21198043 0.75640644 -2.07075107
#> [9,] -0.686852852 -0.38022652 1.65090747 1.23667505 -0.53880916 0.15012013
#> [10,] -0.445661970 0.91899661 -0.05402813 2.03757402 0.22729192 -0.07921171
#> [11,] 1.224081797 -0.57534696 0.11924524 1.30117599 0.49222857 -0.09736927
#> [12,] 0.359813827 0.60796432 0.24368743 0.75677476 0.26783502 0.21615254
#> [13,] 0.400771451 -1.61788271 1.23247588 -1.72673040 0.65325768 0.88246516
#> [14,] 0.110682716 -0.05556197 -0.51606383 -0.60150671 -0.12270866 0.20559750
#> [15,] -0.555841135 0.51940720 -0.99250715 -0.35204646 -0.41367651 -0.61643584
#> [16,] 1.786913137 0.30115336 1.67569693 0.70352390 -2.64314895 -0.73479925
#> [17,] 0.497850478 0.10567619 -0.44116322 -0.10567133 -0.09294102 -0.13180279
#> [18,] -1.966617157 -0.64070601 -0.72306597 -1.25864863 0.43028470 0.31001699
#> [19,] 0.701355902 -0.84970435 -1.23627312 1.68443571 0.53539884 -1.03968035
#> [20,] -0.472791408 -1.02412879 -1.28471572 0.91139129 -0.55527835 -0.18430887
#> [21,] -1.067823706 0.11764660 -0.57397348 0.23743027 1.77950291 0.96726726
#> [22,] -0.217974915 -0.94747461 0.61798582 1.21810861 0.28642442 -0.10828009
#> [23,] -1.026004448 -0.49055744 1.10984814 -1.33877429 0.12631586 -0.69842067
#> [24,] -0.728891229 -0.25609219 0.70758835 0.66082030 1.27226678 -0.27594517
#> [25,] -0.625039268 1.84386201 -0.36365730 -0.52291238 -0.71846622 1.11464855
#> [26,] -1.686693311 -0.65194990 0.05974994 0.68374552 -0.45033862 0.55004396
#> [27,] 0.837787044 0.23538657 -0.70459646 -0.06082195 2.39745248 1.23667580
#> [28,] 0.153373118 0.07796085 -0.71721816 0.63296071 0.01112919 0.13909786
#> [29,] -1.138136937 -0.96185663 0.88465050 1.33551762 1.63356842 0.41027510
#> [30,] 1.253814921 -0.07130809 -1.01559258 0.00729009 -1.43850664 -0.55845691
#> [31,] 0.426464221 1.44455086 1.95529397 1.01755864 -0.19051680 0.60537067
#> [32,] -0.295071483 0.45150405 -0.09031959 -1.18843404 0.37842390 -0.50633354
#> [33,] 0.895125661 0.04123292 0.21453883 -0.72160444 0.30003855 -1.42056550
#> [34,] 0.878133488 -0.42249683 -0.73852770 1.51921771 -1.00563626 0.12799297
#> [35,] 0.821581082 -2.05324722 -0.57438869 0.37738797 0.01925927 1.94585122
#> [36,] 0.688640254 1.13133721 -1.31701613 -2.05222282 -1.07742065 0.80091434
#> [37,] 0.553917654 -1.46064007 -0.18292539 -1.36403745 0.71270333 1.16525339
#> [38,] -0.061911711 0.73994751 0.41898240 -0.20078102 1.08477509 0.35885572
#> [39,] -0.305962664 1.90910357 0.32430434 0.86577940 -2.22498770 -0.60855718
#> [40,] -0.380471001 -1.44389316 -0.78153649 -0.10188326 1.23569346 -0.20224086
#> [41,] -0.694706979 0.70178434 -0.78862197 0.62418747 -1.24104450 -0.27324811
#> [42,] -0.207917278 -0.26219749 -0.50219872 0.95900538 0.45476927 -0.46869978
#> [43,] -1.265396352 -1.57214416 1.49606067 1.67105483 0.65990264 0.70416728
#> [44,] 2.168955965 -1.51466765 -1.13730362 0.05601673 -0.19988983 -1.19736350
#> [45,] 1.207961998 -1.60153617 -0.17905159 -0.05198191 -0.64511396 0.86636613
#> [46,] -1.123108583 -0.53090652 1.90236182 -1.75323736 0.16532102 0.86415249
#> [47,] -0.402884835 -1.46175558 -0.10097489 0.09932759 0.43881870 -1.19862236
#> [48,] -0.466655354 0.68791677 -1.35984070 -0.57185006 0.88330282 0.63949200
#> [49,] 0.779965118 2.10010894 -0.66476944 -0.97400958 -2.05233698 2.43022665
#> [50,] -0.083369066 -1.28703048 0.48545998 -0.17990623 -1.63637927 -0.55721548
#> [51,] 0.253318514 0.78773885 -0.37560287 1.01494317 1.43040234 0.84490424
#> [52,] -0.028546755 0.76904224 -0.56187636 -1.99274849 1.04662885 -0.78220185
#> [53,] -0.042870457 0.33220258 -0.34391723 -0.42727929 0.43528895 1.11071142
#> [54,] 1.368602284 -1.00837661 0.09049665 0.11663728 0.71517841 0.24982472
#> [55,] -0.225770986 -0.11945261 1.59850877 -0.89320757 0.91717492 1.65191539
#> [56,] 1.516470604 -0.28039534 -0.08856511 0.33390294 -2.66092280 -1.45897073
#> [57,] -1.548752804 0.56298953 1.08079950 0.41142992 1.11027710 -0.05129789
#> [58,] 0.584613750 -0.37243876 0.63075412 -0.03303616 -0.48498760 -0.52692518
#> [59,] 0.123854244 0.97697339 -0.11363990 -2.46589819 0.23061683 -0.19726487
#> [60,] 0.215941569 -0.37458086 -1.53290200 2.57145815 -0.29515780 -0.62957874
#> [61,] 0.379639483 1.05271147 -0.52111732 -0.20529926 0.87196495 -0.83384358
#> [62,] -0.502323453 -1.04917701 -0.48987045 0.65119328 -0.34847245 0.57872237
#> [63,] -0.333207384 -1.26015524 0.04715443 0.27376649 0.51850377 -1.08758071
#> [64,] -1.018575383 3.24103993 1.30019868 1.02467323 -0.39068498 1.48403093
#> [65,] -1.071791226 -0.41685759 2.29307897 0.81765945 -1.09278721 -1.18620659
#> [66,] 0.303528641 0.29822759 1.54758106 -0.20979317 1.21001051 0.10107915
#> [67,] 0.448209779 0.63656967 -0.13315096 0.37816777 0.74090001 0.53298929
#> [68,] 0.053004227 -0.48378063 -1.75652740 -0.94540883 1.72426224 0.58673534
#> [69,] 0.922267468 0.51686204 -0.38877986 0.85692301 0.06515393 -0.30174666
#> [70,] 2.050084686 0.36896453 0.08920722 -0.46103834 1.12500275 0.07950200
#> [71,] -0.491031166 -0.21538051 0.84501300 2.41677335 1.97541905 0.96126415
#> [72,] -2.309168876 0.06529303 0.96252797 -1.65104890 -0.28148212 -1.45646592
#> [73,] 1.005738524 -0.03406725 0.68430943 -0.46398724 -1.32295111 -0.78173971
#> [74,] -0.709200763 2.12845190 -1.39527435 0.82537986 -0.23935157 0.32040231
#> [75,] -0.688008616 -0.74133610 0.84964305 0.51013255 -0.21404124 -0.44478198
#> [76,] 1.025571370 -1.09599627 -0.44655722 -0.58948104 0.15168050 1.37000399
#> [77,] -0.284773007 0.03778840 0.17480270 -0.99678074 1.71230498 0.67325386
#> [78,] -1.220717712 0.31048075 0.07455118 0.14447570 -0.32614389 0.07216675
#> [79,] 0.181303480 0.43652348 0.42816676 -0.01430741 0.37300466 -1.50775732
#> [80,] -0.138891362 -0.45836533 0.02467498 -1.79028124 -0.22768406 0.02610023
#> [81,] 0.005764186 -1.06332613 -1.66747510 0.03455107 0.02045071 -0.31641587
#> [82,] 0.385280401 1.26318518 0.73649596 0.19023032 0.31405766 -0.10234651
#> [83,] -0.370660032 -0.34965039 0.38602657 0.17472640 1.32821470 -1.18155923
#> [84,] 0.644376549 -0.86551286 -0.26565163 -1.05501704 0.12131838 0.49865804
#> [85,] -0.220486562 -0.23627957 0.11814451 0.47613328 0.71284232 -1.03895644
#> [86,] 0.331781964 -0.19717589 0.13403865 1.37857014 0.77886003 -0.22622198
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#> [8,] -2.67816441 -0.249252612 0.21269503 0.38195897 0.37564226 -0.18892279
#> [9,] 1.63616439 -0.209374035 0.05086068 0.92853270 -1.19296852 -0.68045020
#> [10,] -0.75063055 0.250899226 0.18291685 0.39057609 1.13254984 -1.25744854
#> [11,] -0.34914391 0.466728667 -0.02467293 -0.65270255 1.83947679 -0.31176654
#> [12,] 1.61863074 1.294261816 -1.09939100 0.81998237 1.52787010 0.05249805
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#> [17,] 1.10328832 -1.352901850 0.15215140 -0.85199189 -0.54388852 -0.54611202
#> [18,] 1.26449613 0.309441562 -1.97326132 -1.71937580 -0.85854930 0.45037717
#> [19,] 0.50087613 0.420861381 -0.65887876 -0.49469904 0.22978594 -0.69251547
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#> [23,] -0.52470637 0.493842855 0.14495627 0.96193008 0.50879221 0.93448196
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#> [31,] 1.09853318 -1.497349092 0.17904754 0.83919689 -0.95297664 0.79416747
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#> [37,] 0.89850083 0.782087200 0.55294765 1.25495610 1.08664197 0.56189912
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#> [42,] 1.25185534 -0.039519537 -1.26843990 1.14035660 -0.05295022 -0.33950900
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#> [44,] 0.81975445 -1.511984611 1.34131626 1.65773610 1.30909768 0.29096208
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#> [75,] 1.48370145 0.360949903 0.54729422 -0.01205329 0.76616288 0.51868426
#> [76,] 0.32920059 -1.082702511 0.87753098 2.89485439 1.05367298 0.78704034
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#> [85,] 1.46883036 -2.333287377 0.28440959 -1.02579127 -1.75644463 0.50379348
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#> [96,] 2.37283848 -0.857718562 0.35633411 -1.42847459 0.55982377 1.42649174
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#> [,43] [,44] [,45] [,46] [,47] [,48]
#> [1,] 0.65099328 1.433174741 -0.03287805 0.83437149 0.91709650 1.74568499
#> [2,] -0.89516799 0.912744883 -0.77600711 -0.69840395 0.55474357 1.67538957
#> [3,] 1.29299294 0.382329981 0.35575943 1.30924048 -1.05550268 -1.45930436
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#> [9,] -0.06042597 -0.940116280 -0.32517300 0.69220014 0.58933550 -0.78045169
#> [10,] 0.35480442 0.039242237 2.13425461 -2.13764150 0.87659208 0.30160044
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#> [13,] 0.96101004 -1.488276766 -0.93977978 0.59982466 -1.16773309 0.11314526
#> [14,] 0.43971623 -0.132874384 1.53836359 0.10875907 0.57448314 -0.28401258
#> [15,] 0.69821380 -0.240116874 0.46835160 1.29479690 -0.46275428 -0.96009246
#> [16,] -1.48600746 0.972019278 -0.71663303 -0.17065076 0.41291213 -0.46532506
#> [17,] -1.12632173 -0.642231451 0.23043894 0.73373952 1.18298161 0.49114620
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#> [19,] -0.25327286 -1.973013711 0.50870847 0.65576257 0.92469895 -0.32550779
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#> [21,] -0.97840283 1.088671618 0.46321586 0.60656951 0.61681388 -0.43411480
#> [22,] 0.31506322 -0.226077239 1.58317836 -0.38959046 0.03407460 -0.02485664
#> [23,] 0.44095616 1.480237940 1.26276163 0.39481502 -0.85043945 -0.72910885
#> [24,] 0.23852640 -0.409756055 0.30499251 -0.87531855 0.94785037 -0.38271234
#> [25,] -0.28422261 -1.002322042 0.33367663 0.54164091 0.72260440 -1.10069412
#> [26,] -0.61814404 0.229145399 0.42150301 2.99152533 -0.86860625 0.74916476
#> [27,] -0.63676796 0.686284539 0.89837976 1.54052051 0.03770180 2.20977518
#> [28,] 0.01745325 -1.493520373 0.38592715 0.98037879 2.52239807 -0.42523023
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#> [30,] -0.79350749 0.046419881 0.63781153 0.32486047 -0.16671286 1.58196745
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#> [32,] 0.09926816 -2.344486374 0.72044942 -1.98512889 -1.11369773 -0.38916498
#> [33,] 0.79141349 -1.706187500 0.05783936 -0.24016790 2.38041364 0.64727514
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#> [37,] 0.71839966 1.200422371 0.03246411 0.65182896 1.07467642 -0.96783702
#> [38,] 0.09637101 -0.613786418 -1.07415455 1.91858058 3.23554282 0.42069596
#> [39,] -1.09564527 -0.166695813 1.19882599 1.15565715 0.48331464 -0.13881389
#> [40,] -2.33035864 1.349742741 -1.16243321 0.66018518 0.61961622 2.16952579
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#> [42,] 0.66886073 0.025873102 -1.06846648 0.07573238 0.14124174 0.64528193
#> [43,] 0.31905530 -0.899870707 -0.98208347 1.15752258 -2.35978264 -0.82132171
#> [44,] -0.36416639 0.067010604 0.86088849 1.28164890 -0.25827324 -0.28622917
#> [45,] 0.05006536 -0.644265585 -0.08174493 -0.59194686 1.46142509 -1.08880098
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#> [47,] -0.75241053 -0.970491872 1.50342038 -1.18310979 -0.05764263 1.35541777
#> [48,] 0.05455508 -0.238649091 -2.48852743 -1.19265860 0.03904464 0.48911220
#> [49,] 0.11226855 0.163631746 -0.69252602 1.59578333 -0.12372949 -0.91185652
#> [50,] -0.72283146 1.068035896 -1.52033934 0.03693927 0.10004958 1.61447747
#> [51,] 0.19819556 0.038534227 -0.56796750 0.03378210 -1.51123342 -0.16372667
#> [52,] 0.31056031 -0.127406724 0.08868113 0.97902302 -0.48087143 0.17873870
#> [53,] 0.52632360 1.106133390 -0.33354078 -0.19665659 -0.34158765 -0.04923863
#> [54,] 0.71104652 2.415056393 -0.57885415 -0.84666439 0.56977337 -0.21580072
#> [55,] 0.41031061 -0.085437750 -0.16379586 0.13835732 0.20737664 0.08097708
#> [56,] 0.30139893 1.177985591 0.26916541 -1.70863334 -0.75859247 -1.03055274
#> [57,] -0.09543010 0.486182865 -0.85575958 -0.47246610 0.84901384 1.15321130
#> [58,] 0.44876031 -0.076045978 -2.07485623 -0.15674016 1.22658542 0.64632888
#> [59,] -1.26924504 -1.920885050 -0.92584586 -1.44256268 -1.27941767 0.07882856
#> [60,] 0.65427019 -0.967552746 -1.90435779 0.18476434 0.18401111 0.94540573
#> [61,] -0.53490937 -0.556743932 0.63533873 -0.73273310 -0.74902577 -1.22214879
#> [62,] 2.33752882 -1.110207184 1.87015839 1.11407753 -0.60814853 1.08512896
#> [63,] -0.59633806 -1.161249940 -1.14546194 -0.28104204 0.41986362 0.21200187
#> [64,] -2.88762983 -0.412925485 -0.88543544 -1.09100028 0.84184980 0.50322103
#> [65,] 1.37208530 0.951889434 -0.87553390 0.23922274 0.38015694 -0.45571199
#> [66,] -0.59865238 -0.920180527 0.78839046 -0.05321768 -0.53484433 -0.78229359
#> [67,] 0.59295092 0.118175118 0.03134468 0.04031788 1.12971201 -0.54620305
#> [68,] 0.22574207 -0.202992795 0.48894782 0.21545474 1.03188963 1.03625305
#> [69,] 1.09631206 0.793099799 0.77146988 -0.39402100 -0.98938258 1.09077666
#> [70,] -0.90326602 0.038436841 0.24783461 -0.32659087 0.31316853 1.55487240
#> [71,] -1.18906159 -0.168162992 0.44783164 0.64800382 -1.15966477 -0.06199721
#> [72,] 1.06496900 -0.584189409 -1.16256527 1.62673702 1.46673354 -0.75605644
#> [73,] -0.95856747 0.891898667 -0.06178828 -1.92569377 0.27005958 1.47246617
#> [74,] -1.53369412 1.139333076 -0.61610346 -0.13568041 1.06713532 -1.55194490
#> [75,] 0.77796950 0.019442483 -1.30482930 0.97968230 0.38814380 -0.15888538
#> [76,] -0.06525828 3.271782751 -1.16898434 -1.17921193 -0.10827039 0.60325702
#> [77,] 2.27820422 -0.002993212 0.93760955 1.16681337 0.75048854 -1.16228474
#> [78,] 0.34360962 2.923823950 -1.30054699 -0.37922742 -1.10331775 -1.56009578
#> [79,] -0.35309274 -0.133879522 -0.40432803 0.70775212 -1.43268243 0.48918559
#> [80,] -0.62718455 -1.570707062 0.98256505 1.47376578 -0.63115364 1.62105051
#> [81,] 1.68460867 -1.424766580 0.32925949 0.89857683 0.26361795 -0.71473653
#> [82,] -1.21492788 -0.871469943 0.65234723 1.21431502 -0.41368807 -0.68668744
#> [83,] 0.61696205 1.478407982 0.33137936 -2.20782706 -0.46511874 -0.94160377
#> [84,] 0.56168002 1.703323302 -0.14887534 -1.27336280 0.92085150 1.48472600
#> [85,] -0.57280593 0.397608593 -2.19971758 0.58146666 -0.50219271 -0.70793519
#> [86,] 1.53571788 0.308495293 -0.60883851 -0.91078080 0.97445687 -0.83744381
#> [87,] -0.74765546 -0.536955293 -1.37830797 -0.55187450 -0.77293592 -0.80402999
#> [88,] -0.01947186 -0.676675596 -0.37808429 1.38422225 -0.25648336 -0.58790399
#> [89,] 0.38762840 -0.717903102 2.05410707 0.11649412 -0.82631334 -0.59771794
#> [90,] 2.32312597 -0.870549995 0.13822540 0.04531788 -0.42619932 0.60644747
#> [91,] 0.61515224 -0.539922450 -0.71914628 -0.24558563 -1.16169687 0.30172811
#> [92,] 1.73154803 -0.622689768 0.88869244 -1.59789552 0.44698697 0.47474825
#> [93,] -0.72856262 0.528537450 0.49137293 -1.88057397 1.18231430 -0.63020029
#> [94,] -1.74544031 0.770818672 -0.08035007 -0.21776624 0.28335869 0.72451431
#> [95,] 0.88935679 1.603180754 -0.22763125 0.35473879 1.71226784 -3.04313484
#> [96,] -1.62846900 -2.448621354 -0.14548558 -1.31894478 -1.64010000 1.12770217
#> [97,] -1.34221036 0.495119682 -0.07142003 -1.80778010 -0.75155207 0.19984638
#> [98,] 0.61077020 -0.318468478 0.61953024 1.27550914 0.52464440 -0.40510219
#> [99,] -0.05577663 -0.266390603 0.12765668 0.50699835 0.63337929 0.47552750
#> [100,] 0.84701928 -1.641704110 -0.62737665 0.48209487 0.32699672 -1.22312208
#> [,49] [,50]
#> [1,] -0.6327135546 0.83666204
#> [2,] 0.1091716177 -0.98027865
#> [3,] -1.5625565841 0.34400599
#> [4,] -0.0402454328 0.18553456
#> [5,] -0.0363299297 0.14119961
#> [6,] -0.2789255815 -1.85209740
#> [7,] -1.2931294494 0.16242002
#> [8,] 1.1668008061 -0.49317896
#> [9,] -1.4853740471 -0.70378507
#> [10,] -1.4771204103 -1.18362071
#> [11,] -0.5826403563 -1.13869818
#> [12,] 1.5493037909 -0.84560347
#> [13,] 0.1068829308 1.24699041
#> [14,] 0.2595667288 0.69516501
#> [15,] -0.2159887019 0.27483248
#> [16,] 0.2708474117 1.71648527
#> [17,] 0.6331892474 1.61208120
#> [18,] 0.7074693315 0.90296077
#> [19,] 1.3706814684 -1.18344199
#> [20,] -0.7780561341 1.43308002
#> [21,] -0.1581135449 -0.20212664
#> [22,] 0.4135386632 -0.24267130
#> [23,] 0.8250757253 0.23754012
#> [24,] -0.3330222488 0.06293772
#> [25,] 0.6507739654 -0.49388005
#> [26,] -0.5484526829 0.68486948
#> [27,] -0.3414764527 -0.48204249
#> [28,] 1.0121437663 -0.56479517
#> [29,] -1.8827545019 -0.25429341
#> [30,] 0.2215467407 -0.75968287
#> [31,] 0.9259399916 0.15368201
#> [32,] -0.3447769817 -0.09725350
#> [33,] 0.6248557297 -0.29590058
#> [34,] -0.7064962937 0.46379138
#> [35,] 0.1712074144 -1.82483094
#> [36,] 0.0097787569 0.25244191
#> [37,] -0.0285917182 0.90124825
#> [38,] -1.2757872641 0.88044069
#> [39,] -0.1625880411 2.23177010
#> [40,] -0.8139526680 -0.63983483
#> [41,] -0.3596072814 -0.98010365
#> [42,] 1.0242439953 0.32609798
#> [43,] -0.5665925821 -1.68526240
#> [44,] -0.0327291611 1.21069157
#> [45,] 0.1030236218 -1.04711359
#> [46,] -0.1894660344 0.43854678
#> [47,] 0.8060904906 -0.33780519
#> [48,] -0.0424478238 -2.37947639
#> [49,] 0.1548982257 0.25934489
#> [50,] -0.8902812005 -1.10300468
#> [51,] -0.3822590762 0.92230106
#> [52,] -0.6470044320 -2.45149101
#> [53,] 0.4742782920 -0.13100382
#> [54,] 1.1515289233 -1.05339701
#> [55,] -0.4606314937 1.12716590
#> [56,] -2.2152623848 -0.72783464
#> [57,] -0.8455127725 0.93534059
#> [58,] -0.9342758947 -0.46829210
#> [59,] 1.1807547873 0.12982107
#> [60,] 0.1429936840 1.46235284
#> [61,] 1.5647374594 -0.68216938
#> [62,] 0.4009041275 1.81861839
#> [63,] -1.5475572207 0.98615837
#> [64,] 0.4949106183 1.28460132
#> [65,] -0.7478538949 -2.24640057
#> [66,] 0.0006033594 -0.16851663
#> [67,] -0.1016533711 -1.46661663
#> [68,] -0.1440581426 0.75927504
#> [69,] -0.3313690567 1.22277703
#> [70,] 1.9212081546 -0.61753539
#> [71,] 1.5098548580 -0.51177394
#> [72,] -0.8892843981 -1.62158019
#> [73,] 0.1986802070 0.79093764
#> [74,] 1.1513646800 1.46152196
#> [75,] 1.1025255707 -1.69993222
#> [76,] -0.8953830461 -1.81251475
#> [77,] 1.4098008988 1.14414110
#> [78,] -0.7045957970 1.34854186
#> [79,] 0.1266425333 0.37155646
#> [80,] 0.1687558038 0.24224903
#> [81,] -1.9199911246 -0.62125855
#> [82,] -0.1333074202 0.33903807
#> [83,] -2.1003865730 -0.45214013
#> [84,] -1.9663385042 2.04323321
#> [85,] 0.3205154324 -0.44933769
#> [86,] 0.3412434206 -3.13738453
#> [87,] 0.9743347007 0.49996221
#> [88,] 0.3795461982 -1.25714159
#> [89,] -0.6737692956 0.82276143
#> [90,] -0.8007270741 -1.54609608
#> [91,] 0.8045545068 -0.25878076
#> [92,] 1.4510356488 0.39040738
#> [93,] 0.7987937110 -0.19727020
#> [94,] 0.2169247894 -1.94694948
#> [95,] -0.0689971963 -1.42763817
#> [96,] 1.6284169621 -0.85041804
#> [97,] -2.4916869814 1.62446909
#> [98,] 0.9929091010 -0.12663816
#> [99,] -0.1676952820 1.27560203
#> [100,] -1.1271011796 0.17949618
#>
#> $missing.data
#> $missing.data[[1]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[2]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#> $missing.data[[3]]
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
#>
#>
#> $imputation.models
#> NULL
#>
#> $blocks.used.for.imputation
#> list()
#>
#> $missingness.pattern
#> list()
#>
#> $y.scale.param
#> NULL
#>
#> $blocks
#> $blocks[[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10
#>
#> $blocks[[2]]
#> [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $blocks[[3]]
#> [1] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#>
#> $mcontrol
#> $handle.missingdata
#> [1] "none"
#>
#> $offset.firstblock
#> [1] "zero"
#>
#> $impute.offset.cases
#> [1] "complete.cases"
#>
#> $nfolds.imputation
#> [1] 10
#>
#> $lambda.imputation
#> [1] "lambda.min"
#>
#> $perc.comp.cases.warning
#> [1] 0.3
#>
#> $threshold.available.cases
#> [1] 30
#>
#> $select.available.cases
#> [1] "maximise.blocks"
#>
#> attr(,"class")
#> [1] "pl.missing.control" "list"
#>
#> $family
#> [1] "gaussian"
#>
#> $dim.x
#> [1] 100 50
#>
#> $pred
#> s1
#> [1,] -3.62028278
#> [2,] -3.29620030
#> [3,] -2.89389542
#> [4,] -4.45900828
#> [5,] -1.44549549
#> [6,] -1.05002663
#> [7,] -4.71109441
#> [8,] -2.19618435
#> [9,] -0.64212360
#> [10,] 4.13091015
#> [11,] 1.06973252
#> [12,] 2.02219465
#> [13,] -3.94518084
#> [14,] -0.15942602
#> [15,] 1.36436010
#> [16,] -5.98717149
#> [17,] -0.48387287
#> [18,] -0.84044369
#> [19,] 2.30626896
#> [20,] 1.94869186
#> [21,] 3.20205359
#> [22,] 0.06147215
#> [23,] -2.58860953
#> [24,] 1.07810785
#> [25,] 1.00947529
#> [26,] 0.95989710
#> [27,] 3.69912003
#> [28,] 1.77716532
#> [29,] 0.73724802
#> [30,] 0.93802293
#> [31,] 0.72070748
#> [32,] -1.25016732
#> [33,] -2.14059201
#> [34,] 1.33248190
#> [35,] -1.86662959
#> [36,] -2.64253022
#> [37,] -1.80375157
#> [38,] 2.56724339
#> [39,] 1.58494815
#> [40,] -0.24554389
#> [41,] 1.03551328
#> [42,] 1.68171784
#> [43,] 1.79291219
#> [44,] -1.09960512
#> [45,] -2.88702492
#> [46,] -4.12792835
#> [47,] -0.79297669
#> [48,] 1.29543991
#> [49,] 0.53985041
#> [50,] -2.98767825
#> [51,] 3.45525653
#> [52,] -0.32513382
#> [53,] 1.11306309
#> [54,] -1.24694712
#> [55,] -2.36185015
#> [56,] -3.26125089
#> [57,] 3.33266608
#> [58,] -1.62982758
#> [59,] -1.72666771
#> [60,] 3.53075156
#> [61,] 2.16472752
#> [62,] 0.45193838
#> [63,] 1.16017465
#> [64,] 4.55490316
#> [65,] -3.86667616
#> [66,] -1.16061870
#> [67,] 3.83634404
#> [68,] 1.79959366
#> [69,] 2.67353321
#> [70,] 0.57602048
#> [71,] 4.79434794
#> [72,] -2.83460089
#> [73,] -2.17403362
#> [74,] 4.70181404
#> [75,] -1.75037015
#> [76,] -2.84791696
#> [77,] -0.33071697
#> [78,] -0.82847118
#> [79,] -0.84096924
#> [80,] -4.01248816
#> [81,] -0.06320986
#> [82,] 2.71611141
#> [83,] 0.50193961
#> [84,] -3.34394023
#> [85,] -0.87293032
#> [86,] 2.13198827
#> [87,] 2.81138453
#> [88,] -4.47996632
#> [89,] 2.45014398
#> [90,] -3.74291666
#> [91,] -2.89976511
#> [92,] -2.66997386
#> [93,] 2.92952171
#> [94,] -3.64698215
#> [95,] -6.94605990
#> [96,] 5.22837817
#> [97,] -3.51857701
#> [98,] 0.61759210
#> [99,] -1.46603405
#> [100,] -2.59231429
#>
#> $actuals
#> [,1]
#> [1,] -4.320199229
#> [2,] -2.145050089
#> [3,] -2.417788193
#> [4,] -4.417505678
#> [5,] -2.659050504
#> [6,] -0.936684634
#> [7,] -5.387087465
#> [8,] -3.057359036
#> [9,] 1.090943326
#> [10,] 2.767568537
#> [11,] 0.296284884
#> [12,] 1.449905790
#> [13,] -4.743973252
#> [14,] -1.092945685
#> [15,] 1.288868616
#> [16,] -7.139407664
#> [17,] 0.935394039
#> [18,] -0.492059784
#> [19,] 3.251529435
#> [20,] 0.889970862
#> [21,] 3.276574344
#> [22,] 0.478397584
#> [23,] -2.655299692
#> [24,] 0.641837880
#> [25,] 0.100210428
#> [26,] 0.589233025
#> [27,] 3.185856430
#> [28,] 1.214770377
#> [29,] 1.560368758
#> [30,] 2.626714387
#> [31,] 1.453961044
#> [32,] -0.560509889
#> [33,] -2.473335868
#> [34,] 1.745593848
#> [35,] -2.322276055
#> [36,] -1.986164598
#> [37,] -1.442586898
#> [38,] 0.104824201
#> [39,] 3.267139834
#> [40,] -1.641272627
#> [41,] -1.246306746
#> [42,] 1.343464266
#> [43,] 1.919254204
#> [44,] -1.226210691
#> [45,] -2.753044533
#> [46,] -5.523246057
#> [47,] -0.007445442
#> [48,] 2.678443011
#> [49,] -0.321469775
#> [50,] -2.537900294
#> [51,] 3.016202621
#> [52,] -0.361412345
#> [53,] 1.850127170
#> [54,] -1.081278725
#> [55,] -0.775000884
#> [56,] -3.402119142
#> [57,] 4.620760220
#> [58,] -2.239158005
#> [59,] -2.117775424
#> [60,] 3.204390100
#> [61,] 2.029905859
#> [62,] 0.997897180
#> [63,] 1.173641110
#> [64,] 4.933191909
#> [65,] -4.717716401
#> [66,] -1.945906153
#> [67,] 4.563316002
#> [68,] 2.790612536
#> [69,] 2.662359340
#> [70,] 0.723182070
#> [71,] 5.435135820
#> [72,] -2.068046736
#> [73,] -2.377080276
#> [74,] 4.625818695
#> [75,] -1.330741150
#> [76,] -3.200455008
#> [77,] -0.557272238
#> [78,] -0.955805921
#> [79,] -2.700197509
#> [80,] -3.440214452
#> [81,] 0.624757094
#> [82,] 2.432717701
#> [83,] 1.990184192
#> [84,] -3.630464548
#> [85,] -0.332671471
#> [86,] 2.372909676
#> [87,] 3.399604568
#> [88,] -4.175389619
#> [89,] 2.248719212
#> [90,] -3.110653465
#> [91,] -3.094141999
#> [92,] -2.742008980
#> [93,] 3.379887452
#> [94,] -2.716828776
#> [95,] -7.173844818
#> [96,] 4.602369405
#> [97,] -2.728815268
#> [98,] 0.661930236
#> [99,] -1.808280650
#> [100,] -3.424192429
#>
#> $adaptive
#> [1] FALSE
#>
#> $adaptive_weights
#> NULL
#>
#> $initial_coeff
#> NULL
#>
#> $initial_weight_scope
#> [1] "global"
#>
#> attr(,"class")
#> [1] "priorityelasticnet" "list"
#>
#> $coefficients
#> V1 V2 V3 V4 V5 V6
#> -0.37871307 1.19409872 -1.00410323 1.54075278 1.01087379 0.30506734
#> V7 V8 V9 V10 V1 V2
#> 0.66328904 0.19371876 -0.35580789 -0.33068763 0.00000000 0.00000000
#> V3 V4 V5 V6 V7 V8
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V9 V10 V11 V12 V13 V14
#> 0.00000000 -0.05948594 0.00000000 0.00000000 -0.06206454 0.00000000
#> V15 V16 V17 V18 V19 V20
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V1 V2 V3 V4 V5 V6
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V7 V8 V9 V10 V11 V12
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V13 V14 V15 V16 V17 V18
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V19 V20
#> 0.00000000 0.00000000
#>
#> $call
#> cvm_priorityelasticnet(X = X, Y = Y, weights = NULL, family = "gaussian",
#> type.measure = "mse", blocks.list = list(blocks1, blocks2),
#> foldid = NULL)
#>
#> attr(,"class")
#> [1] "cvm_priorityelasticnet" "list"The output from fit_cvm provides detailed information on the performance of each block configuration. This includes cross-validated MSE values, the optimal lambda for each configuration, and the number of non-zero coefficients selected by the model. By examining these results, you can make an informed decision about which block configuration to choose for your final model.
Selecting the Optimal Model
After comparing the different block configurations, the next step is to select the optimal model. The cvm_priorityelasticnet function simplifies this process by clearly indicating the configuration with the lowest cross-validation error. This configuration is considered the best in terms of predictive accuracy and generalizability to new data.
In some cases, you may want to further explore the selected model by inspecting the coefficients, prediction accuracy, or other performance metrics. The priorityelasticnet function allows you to refit the model using the optimal block configuration and lambda values identified during cross-validation. This approach ensures that your final model is both well-calibrated and fine-tuned for the specific structure of your data.
Practical Considerations
When using cross-validation for model selection, it’s important to consider the computational cost, especially when working with large datasets or numerous block configurations. The cvm_priorityelasticnet function is designed to handle these scenarios efficiently, but it may be beneficial to parallelize the computation or reduce the number of folds in cases where computational resources are limited.
Moreover, while cross-validation is a robust method for model selection, it’s essential to validate the final model on an independent test set to ensure that the chosen configuration generalizes well to unseen data. This additional step can help guard against overfitting and provide greater confidence in the model’s predictive capabilities.
In conclusion, the cvm_priorityelasticnet function offers a systematic approach to model selection by leveraging cross-validation to compare different block configurations. By selecting the model with the lowest cross-validation error, you can optimize predictive performance while maintaining flexibility in how different groups of predictors are treated within the model.
Using the Shiny App for Threshold Optimization
For binary classification problems, the priorityelasticnet package includes a Shiny application called weightedThreshold, which is designed for interactive threshold optimization. This tool is particularly useful when you need to fine-tune the decision threshold for your model to balance performance metrics like sensitivity and specificity according to the specific requirements of your task.
Launching the Shiny App
The weightedThreshold function launches a Shiny app that provides a user-friendly interface for exploring how different threshold values impact the model’s classification performance. You can launch the app with a simple command:
weightedThreshold(object = fit_bin)Here, fit_bin is the binary classification model fitted using the priorityelasticnet function. When you run this command, the Shiny app opens in your default web browser, displaying various performance metrics and allowing you to adjust the threshold interactively.
Features of the Shiny App
The Shiny app offers several features to help you optimize the decision threshold for your binary classification model:
1. Interactive Threshold Adjustment: The app allows you to slide the threshold bar and immediately see the effects on key performance metrics such as sensitivity, specificity, accuracy, precision, and F1 score. This interactivity helps you understand how different thresholds influence the balance between false positives and false negatives.
2. Threshold Recommendations: Based on the performance metrics and the ROC curve, the app can suggest optimal thresholds, such as the one that maximizes the Youden Index (sensitivity + specificity - 1) or the one that provides the best balance between precision and recall.
3. Real-time Performance Metrics: As you adjust the threshold, the app updates the performance metrics in real-time. This dynamic feedback enables you to make data-driven decisions on the optimal threshold based on the specific needs of your application. For instance, if minimizing false negatives is crucial (e.g., in medical diagnostics), you can adjust the threshold to prioritize sensitivity.
4. ROC Curve Visualization: The app also displays the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate against the false positive rate at various threshold settings. The ROC curve helps you visualize the trade-off between sensitivity and specificity, and the area under the curve (AUC) provides an overall measure of the model’s discriminative ability.
Utility Functions
Extracting Coefficients
The coef.priorityelasticnet function is a crucial tool for interpreting the results of a fitted model. It allows you to extract the estimated coefficients, which represent the relationship between the predictors and the response variable. Understanding these coefficients is essential for gaining insights into how each predictor influences the outcome, particularly in the context of penalized regression models where some coefficients may be shrunk towards zero or set to zero due to regularization.
Here’s how you can extract the coefficients from a fitted binary classification model:
coef(fit_bin)
#> $coefficients
#> Clinical_Var1 Clinical_Var2 Clinical_Var3 Clinical_Var4
#> 0.190643859 -0.144559288 0.053386011 -0.370849102
#> Clinical_Var5 Proteomic_Var1 Proteomic_Var2 Proteomic_Var3
#> -0.052328817 0.007709307 0.000000000 0.000000000
#> Proteomic_Var4 Proteomic_Var5 Proteomic_Var6 Proteomic_Var7
#> 0.000000000 0.000000000 0.000000000 0.010560107
#> Proteomic_Var8 Proteomic_Var9 Proteomic_Var10 Proteomic_Var11
#> 0.000000000 0.000000000 0.015948350 0.000000000
#> Proteomic_Var12 Proteomic_Var13 Proteomic_Var14 Proteomic_Var15
#> 0.117435335 0.000000000 0.203824546 0.000000000
#> Proteomic_Var16 Proteomic_Var17 Proteomic_Var18 Proteomic_Var19
#> 0.089651065 0.000000000 0.098065957 0.000000000
#> Proteomic_Var20 Proteomic_Var21 Proteomic_Var22 Proteomic_Var23
#> 0.071955621 0.000000000 0.005752290 0.049993251
#> Proteomic_Var24 Proteomic_Var25 Proteomic_Var26 Proteomic_Var27
#> 0.000000000 0.000000000 0.127636107 0.068223576
#> Proteomic_Var28 Proteomic_Var29 Proteomic_Var30 Proteomic_Var31
#> 0.000000000 0.094169053 0.000000000 0.000000000
#> Proteomic_Var32 Proteomic_Var33 Proteomic_Var34 Proteomic_Var35
#> 0.000000000 0.000000000 0.000000000 0.054472381
#> Proteomic_Var36 Proteomic_Var37 Proteomic_Var38 Proteomic_Var39
#> 0.000000000 0.021103477 0.000000000 0.000000000
#> Proteomic_Var40 Proteomic_Var41 Proteomic_Var42 Proteomic_Var43
#> 0.103684591 0.000000000 0.201673804 0.086629081
#> Proteomic_Var44 Proteomic_Var45 Proteomic_Var46 Proteomic_Var47
#> 0.000000000 0.000000000 0.000000000 0.154863426
#> Proteomic_Var48 Proteomic_Var49 Proteomic_Var50 Proteomic_Var51
#> 0.119104965 0.015858700 0.000000000 0.000000000
#> Proteomic_Var52 Proteomic_Var53 Proteomic_Var54 Proteomic_Var55
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var56 Proteomic_Var57 Proteomic_Var58 Proteomic_Var59
#> 0.010548570 0.000000000 0.000000000 0.000000000
#> Proteomic_Var60 Proteomic_Var61 Proteomic_Var62 Proteomic_Var63
#> 0.000000000 0.000000000 -0.011822226 0.000000000
#> Proteomic_Var64 Proteomic_Var65 Proteomic_Var66 Proteomic_Var67
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var68 Proteomic_Var69 Proteomic_Var70 Proteomic_Var71
#> 0.017699677 0.025050808 0.000000000 0.000000000
#> Proteomic_Var72 Proteomic_Var73 Proteomic_Var74 Proteomic_Var75
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var76 Proteomic_Var77 Proteomic_Var78 Proteomic_Var79
#> 0.081769545 0.000000000 0.000000000 0.000000000
#> Proteomic_Var80 Proteomic_Var81 Proteomic_Var82 Proteomic_Var83
#> 0.016680978 0.000000000 0.000000000 0.000000000
#> Proteomic_Var84 Proteomic_Var85 Proteomic_Var86 Proteomic_Var87
#> 0.000000000 0.016551351 0.000000000 0.007366994
#> Proteomic_Var88 Proteomic_Var89 Proteomic_Var90 Proteomic_Var91
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var92 Proteomic_Var93 Proteomic_Var94 Proteomic_Var95
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var96 Proteomic_Var97 Proteomic_Var98 Proteomic_Var99
#> 0.000000000 0.028270750 0.000000000 0.000000000
#> Proteomic_Var100 Proteomic_Var101 Proteomic_Var102 Proteomic_Var103
#> 0.093835914 0.000000000 0.000000000 0.000000000
#> Proteomic_Var104 Proteomic_Var105 Proteomic_Var106 Proteomic_Var107
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var108 Proteomic_Var109 Proteomic_Var110 Proteomic_Var111
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var112 Proteomic_Var113 Proteomic_Var114 Proteomic_Var115
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var116 Proteomic_Var117 Proteomic_Var118 Proteomic_Var119
#> 0.000000000 0.000000000 0.000000000 0.048295263
#> Proteomic_Var120 Proteomic_Var121 Proteomic_Var122 Proteomic_Var123
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var124 Proteomic_Var125 Proteomic_Var126 Proteomic_Var127
#> -0.040890450 0.000000000 0.000000000 0.063112804
#> Proteomic_Var128 Proteomic_Var129 Proteomic_Var130 Proteomic_Var131
#> 0.000000000 0.016561847 0.000000000 0.000000000
#> Proteomic_Var132 Proteomic_Var133 Proteomic_Var134 Proteomic_Var135
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var136 Proteomic_Var137 Proteomic_Var138 Proteomic_Var139
#> 0.000000000 0.000000000 0.000000000 -0.053294778
#> Proteomic_Var140 Proteomic_Var141 Proteomic_Var142 Proteomic_Var143
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var144 Proteomic_Var145 Proteomic_Var146 Proteomic_Var147
#> 0.000000000 0.000000000 0.024672931 0.000000000
#> Proteomic_Var148 Proteomic_Var149 Proteomic_Var150 Proteomic_Var151
#> -0.003990008 0.000000000 0.000000000 0.000000000
#> Proteomic_Var152 Proteomic_Var153 Proteomic_Var154 Proteomic_Var155
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var156 Proteomic_Var157 Proteomic_Var158 Proteomic_Var159
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var160 Proteomic_Var161 Proteomic_Var162 Proteomic_Var163
#> -0.140207231 0.000000000 0.000000000 0.000000000
#> Proteomic_Var164 Proteomic_Var165 Proteomic_Var166 Proteomic_Var167
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var168 Proteomic_Var169 Proteomic_Var170 Proteomic_Var171
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var172 Proteomic_Var173 Proteomic_Var174 RNA_Var1
#> 0.000000000 0.041243218 0.000000000 0.000000000
#> RNA_Var2 RNA_Var3 RNA_Var4 RNA_Var5
#> 0.000000000 0.016765562 0.153411042 0.000000000
#> RNA_Var6 RNA_Var7 RNA_Var8 RNA_Var9
#> 0.000000000 0.085261161 0.000000000 0.031783886
#> RNA_Var10 RNA_Var11 RNA_Var12 RNA_Var13
#> 0.068730428 0.006540649 0.000000000 0.063928845
#> RNA_Var14 RNA_Var15 RNA_Var16 RNA_Var17
#> 0.000000000 0.045865443 0.011500765 0.093204044
#> RNA_Var18 RNA_Var19 RNA_Var20 RNA_Var21
#> 0.000000000 0.055308566 0.132617884 0.000000000
#> RNA_Var22 RNA_Var23 RNA_Var24 RNA_Var25
#> 0.002082002 0.000000000 0.020687222 0.000000000
#> RNA_Var26 RNA_Var27 RNA_Var28 RNA_Var29
#> 0.042837290 0.092848757 0.000000000 0.107760159
#> RNA_Var30 RNA_Var31 RNA_Var32 RNA_Var33
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var34 RNA_Var35 RNA_Var36 RNA_Var37
#> 0.000000000 0.000000000 0.089312669 0.107413420
#> RNA_Var38 RNA_Var39 RNA_Var40 RNA_Var41
#> 0.062248869 0.120450226 0.000000000 0.000000000
#> RNA_Var42 RNA_Var43 RNA_Var44 RNA_Var45
#> 0.026183877 0.000000000 0.000000000 0.145195502
#> RNA_Var46 RNA_Var47 RNA_Var48 RNA_Var49
#> 0.000000000 0.000000000 0.042915411 0.000000000
#> RNA_Var50 RNA_Var51 RNA_Var52 RNA_Var53
#> 0.192994643 0.000000000 0.000000000 0.000000000
#> RNA_Var54 RNA_Var55 RNA_Var56 RNA_Var57
#> 0.000000000 0.111145024 0.103870373 0.000000000
#> RNA_Var58 RNA_Var59 RNA_Var60 RNA_Var61
#> 0.000000000 0.000000000 0.027344986 0.075485805
#> RNA_Var62 RNA_Var63 RNA_Var64 RNA_Var65
#> 0.000000000 0.000000000 0.000000000 0.116484171
#> RNA_Var66 RNA_Var67 RNA_Var68 RNA_Var69
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var70 RNA_Var71 RNA_Var72 RNA_Var73
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var74 RNA_Var75 RNA_Var76 RNA_Var77
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var78 RNA_Var79 RNA_Var80 RNA_Var81
#> 0.049692654 0.000000000 0.000000000 0.066145082
#> RNA_Var82 RNA_Var83 RNA_Var84 RNA_Var85
#> 0.000000000 0.000000000 0.107636031 0.000000000
#> RNA_Var86 RNA_Var87 RNA_Var88 RNA_Var89
#> 0.033720807 0.103421196 0.000000000 0.000000000
#> RNA_Var90 RNA_Var91 RNA_Var92 RNA_Var93
#> 0.000000000 0.019601345 0.000000000 0.000000000
#> RNA_Var94 RNA_Var95 RNA_Var96 RNA_Var97
#> 0.000000000 0.000000000 0.058139383 0.000000000
#> RNA_Var98 RNA_Var99 RNA_Var100 RNA_Var101
#> 0.000000000 0.099011832 0.000000000 0.000000000
#> RNA_Var102 RNA_Var103 RNA_Var104 RNA_Var105
#> -0.078365557 0.000000000 0.000000000 0.000000000
#> RNA_Var106 RNA_Var107 RNA_Var108 RNA_Var109
#> 0.000000000 0.000000000 0.000000000 -0.091202153
#> RNA_Var110 RNA_Var111 RNA_Var112 RNA_Var113
#> 0.000000000 -0.052704524 0.000000000 0.000000000
#> RNA_Var114 RNA_Var115 RNA_Var116 RNA_Var117
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var118 RNA_Var119 RNA_Var120 RNA_Var121
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var122 RNA_Var123 RNA_Var124 RNA_Var125
#> 0.042893741 0.000000000 0.002961796 0.000000000
#> RNA_Var126 RNA_Var127 RNA_Var128 RNA_Var129
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var130 RNA_Var131 RNA_Var132 RNA_Var133
#> 0.000000000 -0.014456406 0.000000000 0.000000000
#> RNA_Var134 RNA_Var135 RNA_Var136 RNA_Var137
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var138 RNA_Var139 RNA_Var140 RNA_Var141
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var142 RNA_Var143 RNA_Var144 RNA_Var145
#> 0.000000000 0.000000000 0.000000000 0.000000000
#>
#> $intercepts
#> [1] 0.305740500 0.004197362 0.004455564In this example, fit_bin is a model fitted using the priorityelasticnet function. The extracted coefficients can help you identify which predictors are most influential in predicting the response variable. For example, non-zero coefficients indicate predictors that contribute to the model, while zero coefficients suggest that the corresponding predictors have been effectively excluded due to penalization.
In models fitted with regularization methods such as elastic net, the coefficients are often shrunken to prevent overfitting and to enhance the model’s generalizability to new data. The amount of shrinkage depends on the regularization parameters, with stronger regularization leading to more coefficients being reduced towards zero. By examining the extracted coefficients, you can assess the relative importance of each predictor and make decisions about which variables are essential for your model.
For example, in a model with several blocks of predictors, you might find that only a few predictors have non-zero coefficients, indicating that these are the most relevant features for predicting the outcome.
Making Predictions
The predict.priorityelasticnet function is used to generate predictions from a fitted model. This function can produce different types of predictions depending on the specified type parameter, including linear predictors, fitted values, or class probabilities (in the case of classification models).
Here’s how you can generate predictions from a fitted model using new data:
set.seed(123)
X_new <- matrix(rnorm(406 * 324), 406, 324)
predictions < predict(fit_bin, newdata = X_new, type = "response")
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head(predictions)
#> [,1]
#> [1,] 0.5327832
#> [2,] 0.7639971
#> [3,] 0.3106758
#> [4,] 0.4322164
#> [5,] 0.6860752
#> [6,] 0.3602870In this example, fit_bin is the fitted model, and X_new is the new data for which you want to generate predictions. The type = “response” parameter specifies that you want the predictions to be in the form of fitted values (e.g., probabilities for binary classification or actual values for regression).
Types of Predictions
Linear Predictors (type = “link”): These are the raw predictions from the linear model before applying any transformation (e.g., before applying the logistic function in logistic regression). This option is useful when you want to analyze the linear relationship between the predictors and the response.
Fitted Values (type = “response”): These are the transformed predictions that correspond to the actual scale of the response variable. For binary classification, this would typically be the predicted probabilities of the positive class.
Certainly! Here’s a more detailed and structured vignette section that first introduces the concept of the Adaptive-Elastic net in Priority-elastic net algorithm, followed by the sophisticated example.
Priority-Adaptive Elastic Net
In high-dimensional data analysis regularization techniques like Lasso and elastic net are essential for preventing overfitting and improving model interpretability. These methods work by shrinking some of the predictor coefficients to zero, effectively selecting a subset of features that contribute most to the model.
However, not all predictors are created equal. Some may have a strong relationship with the response variable, while others might have a weaker relationship, and many could be purely noise. In such cases, treating all predictors the same during regularization might not be ideal.
This is where the Adaptive-Elastic net comes into play, as
demonstrated in our previous examples. By incorporating the adaptive
argument, we showed how the priorityelasticnet package
applies different penalties to predictors based on their importance,
allowing for a more nuanced regularization approach. This method
dynamically adjusts the penalty for each predictor, shrinking less
important predictors more aggressively while preserving the influence of
key predictors.
1. Fit an Initial Model: First, fit a standard elastic net model to obtain initial estimates of the coefficients.
- You can choose to calculate these initial estimates either globally, where the weights are computed based on all predictors in the dataset, or block-wise, where the initial model is fit separately for each block of predictors.
2. Calculate Adaptive Weights: Compute weights based on these initial coefficients, where predictors with larger coefficients (indicating more importance) receive smaller penalties in the final model.
- If you selected global weighting, the adaptive weights are computed from the initial model fit to all predictors. For block-wise weighting, the adaptive weights are computed separately for each block, ensuring that the weight calculation respects the hierarchical block structure.
3. Apply Adaptive Penalties: Refit the model using these adaptive weights, allowing more important predictors to retain larger coefficients while shrinking less important ones more aggressively.
This approach allows the model to be more flexible and accurate in selecting relevant features, particularly when there is a clear distinction between strong, weak, and irrelevant predictors. The option to use global or block-wise initial weights provides additional flexibility, enabling the model to either prioritize across all predictors simultaneously (global) or account for differences within each block (block-wise), depending on the structure of the data.
Example 1: Gaussian Model
Now, let’s see how the Priority-Adaptive elastic net works in practice. We will walk through examples that demonstrate its application on a simulated dataset containing a mix of strong, weak, and noise predictors.
Step 1: Simulating the Data
We begin by simulating a dataset with 200 observations and 100 predictors. Among these:
- 10 predictors are strongly associated with the response variable.
- 20 predictors are weakly associated with the response.
- The remaining 70 predictors are noise, meaning they have no real association with the response.
The response variable is generated by combining the effects of these predictors with some added noise.
# Set the random seed for reproducibility
set.seed(1234)
# Simulate high-dimensional data
n <- 200 # Number of observations
p <- 100 # Number of predictors
n_strong <- 10 # Number of strong predictors
n_weak <- 20 # Number of weak predictors
# Design matrix (predictors)
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
# Generate coefficients: strong predictors with large effects, weak with small effects
beta <- c(rep(2, n_strong), rep(0.5, n_weak), rep(0, p - n_strong - n_weak))
# Generate response with Gaussian noise
Y <- X %*% beta + rnorm(n)Step 2: Defining Predictor Blocks
We categorize the predictors into three blocks:
- Strong Block: Contains the 10 strong predictors.
- Weak Block: Contains the 20 weak predictors.
- Noise Block: Contains the 70 noise predictors.
These blocks allow the priorityelasticnet function to apply penalties differently across these groups, which is crucial for the Priority-Adaptive elastic net.
# Define blocks of predictors for the model
blocks <- list(
strong_block = 1:n_strong, # Strong predictors
weak_block = (n_strong + 1):(n_strong + n_weak), # Weak predictors
noise_block = (n_strong + n_weak + 1):p # Noise (irrelevant predictors)
)Step 3: Running the Priority-Adaptive Elastic Net
With the data and blocks defined, we apply the Adaptive-Elastic net
using the priorityelasticnet function. The
adaptive argument is set to TRUE, which tells
the function to calculate adaptive penalties based on an initial model
fit. Moreover, initial_global_weight argument is set to FALSE in order
to calculate initial weights separately for each block.
# Run priorityelasticnet with Adaptive Elastic Net
result <- priorityelasticnet(X = X,
Y = Y,
family = "gaussian",
alpha = 0.5,
type.measure = "mse",
blocks = blocks,
adaptive = TRUE,
initial_global_weight = FALSE,
verbose = TRUE)
#> Starting priorityelasticnet with 3 blocks.
#> Checking family type and setting default type.measure if necessary...
#> Calculating adaptive weights based on an initial model using block-wise approach...
#> Adaptive weights calculated.
#> Handling missing data based on the provided mcontrol parameters...
#> Fitting model for block 1...
#> Finished processing block 1
#> Fitting model for block 2...
#> Finished processing block 2
#> Fitting model for block 3...
#> Finished processing block 3
#> priorityelasticnet completed successfully.Step 4: Analyzing the Results
After fitting the model, we can inspect the final coefficients and the adaptive weights that were applied. The adaptive weights indicate how much each predictor was penalized in the final model, based on its initial importance.
# Examine the coefficients
cat("Final model coefficients:")
#> Final model coefficients:
result$coefficients
#> V1 V2 V3 V4 V5 V6
#> 1.84781051 1.69782601 2.16791742 2.05226065 1.97174167 1.64133636
#> V7 V8 V9 V10 V1 V2
#> 2.17761446 1.93214030 2.07395409 2.22591723 0.47630172 0.50087661
#> V3 V4 V5 V6 V7 V8
#> 0.65493539 0.34392217 0.47647244 0.45794215 0.55496028 0.47227243
#> V9 V10 V11 V12 V13 V14
#> 0.57756610 0.40680911 0.47031840 0.45708712 0.46954775 0.61145873
#> V15 V16 V17 V18 V19 V20
#> 0.46018988 0.45065144 0.00000000 0.59282388 0.59462459 0.29676374
#> V1 V2 V3 V4 V5 V6
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V7 V8 V9 V10 V11 V12
#> 0.00000000 0.00000000 -0.04580515 0.00000000 0.00000000 0.00000000
#> V13 V14 V15 V16 V17 V18
#> 0.00000000 0.00000000 -0.06221073 0.00000000 0.00000000 0.00000000
#> V19 V20 V21 V22 V23 V24
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V25 V26 V27 V28 V29 V30
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V31 V32 V33 V34 V35 V36
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V37 V38 V39 V40 V41 V42
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V43 V44 V45 V46 V47 V48
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V49 V50 V51 V52 V53 V54
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V55 V56 V57 V58 V59 V60
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V61 V62 V63 V64 V65 V66
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> V67 V68 V69 V70
#> 0.00000000 0.00000000 0.00000000 0.00000000# Examine the adaptive weights
cat("Adaptive weights for each predictor:")
#> Adaptive weights for each predictor:
result$adaptive_weights
#> [1] 0.5410622 0.5882052 0.4614787 0.4874561 0.5070443 0.6082299
#> [7] 0.4594904 0.5175640 0.4823750 0.4496193 2.5754909 3.2400473
#> [13] 0.6846764 2.0464244 1.4449078 1.2709276 1.2312830 10.1110781
#> [19] 2.8377712 3.3807946 0.7520705 0.7644429 3.1867191 0.9933944
#> [25] 2.2615433 2.3554224 524.7279581 0.9804774 1.3630116 2.0384033
#> [31] 6.7764747 1.2315375 2.7988179 4.5398047 1.7701532 13.7679731
#> [37] 4.8590204 1.2385564 0.6286864 1.7146582 1.6322927 1.7038807
#> [43] 1.7322877 3.7416500 0.7961860 0.7424966 1.3719668 3.6192086
#> [49] 1.3101027 1.7014164 9.2355416 2.4621769 542.3276473 2.3362481
#> [55] 0.7847086 1.8737413 3.7184230 0.9147786 6.2041500 2.1170650
#> [61] 82.3160916 1.3467426 923.4750603 1.0764871 1.4449873 1.4106819
#> [67] 1.7736993 8.2917190 2.2140664 3.3697980 17.3414516 3.0986300
#> [73] 0.7108060 2.3415220 31.1007597 2.4455137 43.2849904 0.9066003
#> [79] 7.4245194 1.7724432 37.5822495 26.0651436 2.8313331 3.5047941
#> [85] 1.0556600 5.3698543 7.2267436 10.0125407 3.3593520 1.1360134
#> [91] 3.5905795 44.1673796 2.4701463 2.6582901 1.0998456 39.0761910
#> [97] 0.7483809 2.0926730 5.6645874 1.7072997Step 5: Visualizing the Coefficient Paths
To better understand how the Priority-Adaptive Elastic net handled different groups of predictors, we can visualize the coefficient paths for each block. These plots show how the coefficients change as the regularization parameter (lambda) varies, providing insight into how strongly each group of predictors was penalized.
plot(result$glmnet.fit[[1]], xvar = "lambda", label = TRUE, main = "Coefficient Paths for Strong Block")
plot(result$glmnet.fit[[2]], xvar = "lambda", label = TRUE, main = "Coefficient Paths for Weak Block")
plot(result$glmnet.fit[[3]], xvar = "lambda", label = TRUE, main = "Coefficient Paths for Noise Block")
This example demonstrates how this model can effectively differentiate between strong, weak, and irrelevant predictors in a high-dimensional dataset. By applying adaptive penalties for each block, the model ensures that important predictors are retained while less relevant ones are shrunk toward zero. This approach not only improves feature selection but also enhances the overall predictive performance of the model.
The coefficient paths provide a clear visual representation of how the adaptive penalties work, showing that strong predictors remain in the model even with higher levels of regularization, while noise predictors are eliminated. This example highlights the power and flexibility of the Priority-Adaptive elastic net, making it a valuable tool in high-dimensional data analysis.
Example 2: Cox Model
Step 1: Running the Priority-Adaptive Elastic Net
# Set seed for reproducibility
set.seed(123)
# Number of observations and predictors
n <- 50 # Number of observations
p <- 300 # Number of predictors
# Number of non-zero coefficients
nzc <- trunc(p / 10)
# Simulate predictor matrix
x <- matrix(rnorm(n * p), n, p)
# Simulate regression coefficients for non-zero predictors
beta <- rnorm(nzc)
# Calculate linear predictor
fx <- x[, seq(nzc)] %*% beta / 3
# Calculate hazard function
hx <- exp(fx)
# Simulate survival times using exponential distribution
ty <- rexp(n, hx)
# Generate censoring indicator (30% censoring probability)
tcens <- rbinom(n = n, prob = .3, size = 1)
# Load survival library and create survival object
library(survival)
y <- Surv(ty, 1 - tcens)blocks <- list(
bp1 = 1:20, # First block with predictors 1 to 20
bp2 = 21:200, # Second block with predictors 21 to 200
bp3 = 201:300 # Third block with predictors 201 to 300
)# Fit Cox model using priorityelasticnet
result_cox <- priorityelasticnet(
x,
y,
family = "cox",
alpha = 1,
type.measure = "deviance",
blocks = blocks,
block1.penalization = TRUE,
lambda.type = "lambda.min",
standardize = TRUE,
nfolds = 5,
adaptive = TRUE,
initial_global_weight = FALSE
)Step 2: Analyzing the Results
# Examine the coefficients
cat("Final model coefficients:")
#> Final model coefficients:
result_cox$coefficients
#> V1 V2 V3 V4 V5 V6
#> 0.000000000 -0.182813798 -0.046417767 0.000000000 0.000000000 0.556207406
#> V7 V8 V9 V10 V11 V12
#> -0.116418099 0.000000000 0.321920184 0.000000000 0.314873673 -0.167220349
#> V13 V14 V15 V16 V17 V18
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.220307668 0.000000000
#> V19 V20 V1 V2 V3 V4
#> 0.000000000 -0.231215685 0.000000000 -0.487870530 0.000000000 0.000000000
#> V5 V6 V7 V8 V9 V10
#> 0.000000000 0.000000000 0.000000000 -0.619834634 0.000000000 -0.147850826
#> V11 V12 V13 V14 V15 V16
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V17 V18 V19 V20 V21 V22
#> 0.000000000 0.000000000 -0.484895349 0.000000000 0.000000000 0.000000000
#> V23 V24 V25 V26 V27 V28
#> -0.838967210 -0.341955220 0.000000000 0.000000000 0.000000000 0.000000000
#> V29 V30 V31 V32 V33 V34
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V35 V36 V37 V38 V39 V40
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V41 V42 V43 V44 V45 V46
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V47 V48 V49 V50 V51 V52
#> 0.000000000 0.000000000 -0.255861946 0.000000000 0.000000000 0.000000000
#> V53 V54 V55 V56 V57 V58
#> 0.000000000 0.002479326 0.000000000 0.000000000 0.000000000 0.000000000
#> V59 V60 V61 V62 V63 V64
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V65 V66 V67 V68 V69 V70
#> 0.000000000 0.000000000 0.000000000 0.000000000 -0.366020370 -1.232752662
#> V71 V72 V73 V74 V75 V76
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V77 V78 V79 V80 V81 V82
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.017708148
#> V83 V84 V85 V86 V87 V88
#> 0.000000000 0.000000000 0.000000000 0.000000000 -0.001681602 0.000000000
#> V89 V90 V91 V92 V93 V94
#> -0.658501933 0.089947276 0.000000000 0.000000000 0.000000000 0.000000000
#> V95 V96 V97 V98 V99 V100
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V101 V102 V103 V104 V105 V106
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V107 V108 V109 V110 V111 V112
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V113 V114 V115 V116 V117 V118
#> 0.000000000 0.000000000 0.295375425 0.000000000 0.000000000 0.000000000
#> V119 V120 V121 V122 V123 V124
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V125 V126 V127 V128 V129 V130
#> 0.000000000 -0.305117101 0.000000000 0.000000000 0.000000000 0.000000000
#> V131 V132 V133 V134 V135 V136
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V137 V138 V139 V140 V141 V142
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.646337835
#> V143 V144 V145 V146 V147 V148
#> 0.000000000 0.165856709 0.000000000 -0.558687818 0.000000000 0.493047993
#> V149 V150 V151 V152 V153 V154
#> 0.668138535 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V155 V156 V157 V158 V159 V160
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V161 V162 V163 V164 V165 V166
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V167 V168 V169 V170 V171 V172
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V173 V174 V175 V176 V177 V178
#> 0.000000000 0.000000000 -0.234230632 0.000000000 0.000000000 0.000000000
#> V179 V180 V1 V2 V3 V4
#> 0.000000000 -0.479366808 0.000000000 0.000000000 0.000000000 0.000000000
#> V5 V6 V7 V8 V9 V10
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V11 V12 V13 V14 V15 V16
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V17 V18 V19 V20 V21 V22
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V23 V24 V25 V26 V27 V28
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V29 V30 V31 V32 V33 V34
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V35 V36 V37 V38 V39 V40
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V41 V42 V43 V44 V45 V46
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V47 V48 V49 V50 V51 V52
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V53 V54 V55 V56 V57 V58
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V59 V60 V61 V62 V63 V64
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V65 V66 V67 V68 V69 V70
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V71 V72 V73 V74 V75 V76
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V77 V78 V79 V80 V81 V82
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V83 V84 V85 V86 V87 V88
#> -0.015796910 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V89 V90 V91 V92 V93 V94
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
#> V95 V96 V97 V98 V99 V100
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000result_cox$initial_coeff
#> 100 x 1 sparse Matrix of class "dgCMatrix"
#> 1
#> V1 0.00000100
#> V2 0.00000100
#> V3 2.92496304
#> V4 1.38839382
#> V5 0.00000100
#> V6 0.61204245
#> V7 1.38789368
#> V8 1.93364496
#> V9 0.00000100
#> V10 0.00000100
#> V11 0.00000100
#> V12 2.06060165
#> V13 0.00000100
#> V14 0.00000100
#> V15 0.00000100
#> V16 0.00000100
#> V17 1.06421630
#> V18 3.14741736
#> V19 1.26813149
#> V20 2.87202300
#> V21 4.33126622
#> V22 0.00000100
#> V23 1.99419197
#> V24 0.00000100
#> V25 0.00000100
#> V26 0.00000100
#> V27 0.00000100
#> V28 1.58495644
#> V29 0.00000100
#> V30 0.00000100
#> V31 0.05356286
#> V32 3.58226407
#> V33 0.00000100
#> V34 0.00000100
#> V35 0.00000100
#> V36 3.14054571
#> V37 6.66217869
#> V38 0.28997401
#> V39 2.24700390
#> V40 0.00000100
#> V41 0.00000100
#> V42 0.00000100
#> V43 1.31424074
#> V44 0.00000100
#> V45 3.99604086
#> V46 0.03880633
#> V47 0.00000100
#> V48 0.00000100
#> V49 0.00000100
#> V50 5.88626881
#> V51 0.00000100
#> V52 0.00000100
#> V53 0.00000100
#> V54 2.35378894
#> V55 2.84374919
#> V56 1.20236503
#> V57 0.00000100
#> V58 0.00000100
#> V59 4.74863734
#> V60 0.77613460
#> V61 0.00000100
#> V62 0.00000100
#> V63 0.15926133
#> V64 0.00000100
#> V65 0.00000100
#> V66 0.41443136
#> V67 1.77903143
#> V68 6.69486629
#> V69 2.75456792
#> V70 0.00000100
#> V71 0.00000100
#> V72 0.00000100
#> V73 0.00000100
#> V74 2.93405278
#> V75 0.00000100
#> V76 0.00000100
#> V77 1.26447444
#> V78 0.00000100
#> V79 0.00000100
#> V80 0.00000100
#> V81 0.00000100
#> V82 0.00000100
#> V83 6.91858900
#> V84 1.21819825
#> V85 0.00000100
#> V86 0.92070015
#> V87 1.01374965
#> V88 0.00000100
#> V89 0.00000100
#> V90 4.07159729
#> V91 0.00000100
#> V92 1.53705300
#> V93 0.00000100
#> V94 4.87859078
#> V95 0.00000100
#> V96 2.25688823
#> V97 1.48755035
#> V98 0.80615694
#> V99 0.00000100
#> V100 0.00000100# Examine the adaptive weights
cat("Adaptive weights for each predictor:")
#> Adaptive weights for each predictor:
result_cox$adaptive_weights
#> [1] 1.000000e+06 2.648148e+00 2.499916e+00 1.895893e+01 2.937094e+00
#> [6] 1.210482e+00 2.490309e+00 4.208392e+00 2.200504e+00 4.100383e+00
#> [11] 1.630925e+00 2.502986e+00 4.062311e+00 5.433795e+00 1.611159e+01
#> [16] 3.106544e+00 2.731662e+00 6.538747e+00 4.661280e+00 2.134975e+00
#> [21] 1.072983e+00 5.271378e-01 1.000000e+06 1.000000e+06 1.000000e+06
#> [26] 4.445202e+00 1.000000e+06 2.154415e-01 1.000000e+06 7.099082e-01
#> [31] 1.000000e+06 1.000000e+06 1.000000e+06 8.638427e-01 1.000000e+06
#> [36] 6.115200e+00 1.000000e+06 1.000000e+06 3.684318e-01 1.000000e+06
#> [41] 1.000000e+06 1.000000e+06 2.499989e-01 5.114678e-01 1.000000e+06
#> [46] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.030312e+00
#> [51] 9.896202e-01 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [56] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [61] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [66] 1.000000e+06 1.000000e+06 1.251782e+01 4.423456e-01 1.000000e+06
#> [71] 1.000000e+06 1.000000e+06 1.000000e+06 6.496755e-01 1.000000e+06
#> [76] 1.000000e+06 1.000000e+06 8.047975e-01 1.000000e+06 1.000000e+06
#> [81] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 2.274304e+00
#> [86] 1.000000e+06 1.000000e+06 1.000000e+06 3.708782e-01 2.020496e-01
#> [91] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [96] 1.000000e+06 1.219072e+00 1.000000e+06 1.000000e+06 1.000000e+06
#> [101] 1.000000e+06 6.377810e-01 1.000000e+06 1.000000e+06 1.000000e+06
#> [106] 1.623767e+02 7.422910e-01 1.000000e+06 2.718743e-01 6.486840e-01
#> [111] 1.000000e+06 1.000000e+06 1.000000e+06 9.698848e-01 1.000000e+06
#> [116] 1.000000e+06 9.270856e+00 1.000000e+06 1.000000e+06 1.000000e+06
#> [121] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [126] 3.190393e+00 2.760623e+00 1.000000e+06 1.000000e+06 1.000000e+06
#> [131] 1.000000e+06 1.000000e+06 9.916027e-01 1.000000e+06 5.529787e-01
#> [136] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [141] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 9.241900e+01
#> [146] 6.597503e-01 1.000000e+06 1.000000e+06 1.000000e+06 1.796116e+00
#> [151] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [156] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [161] 1.000000e+06 4.523507e-01 1.000000e+06 4.905468e-01 5.524313e-01
#> [166] 2.684159e-01 1.000000e+06 4.155886e-01 4.313829e-01 1.000000e+06
#> [171] 1.000000e+06 1.023339e+00 1.000000e+06 1.000000e+06 1.000000e+06
#> [176] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [181] 1.000000e+06 1.000000e+06 1.017704e+00 1.000000e+06 1.000000e+06
#> [186] 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06 1.000000e+06
#> [191] 2.389859e+00 1.000000e+06 5.203715e+00 1.000000e+06 5.326313e-01
#> [196] 1.000000e+06 1.000000e+06 1.000000e+06 6.270193e+00 2.934303e-01
#> [201] 1.000000e+06 1.000000e+06 3.418847e-01 7.202567e-01 1.000000e+06
#> [206] 1.633874e+00 7.205163e-01 5.171580e-01 1.000000e+06 1.000000e+06
#> [211] 1.000000e+06 4.852952e-01 1.000000e+06 1.000000e+06 1.000000e+06
#> [216] 1.000000e+06 9.396586e-01 3.177208e-01 7.885618e-01 3.481866e-01
#> [221] 2.308794e-01 1.000000e+06 5.014562e-01 1.000000e+06 1.000000e+06
#> [226] 1.000000e+06 1.000000e+06 6.309322e-01 1.000000e+06 1.000000e+06
#> [231] 1.866965e+01 2.791531e-01 1.000000e+06 1.000000e+06 1.000000e+06
#> [236] 3.184160e-01 1.501010e-01 3.448585e+00 4.450371e-01 1.000000e+06
#> [241] 1.000000e+06 1.000000e+06 7.608956e-01 1.000000e+06 2.502477e-01
#> [246] 2.576899e+01 1.000000e+06 1.000000e+06 1.000000e+06 1.698869e-01
#> [251] 1.000000e+06 1.000000e+06 1.000000e+06 4.248469e-01 3.516485e-01
#> [256] 8.316942e-01 1.000000e+06 1.000000e+06 2.105867e-01 1.288436e+00
#> [261] 1.000000e+06 1.000000e+06 6.278988e+00 1.000000e+06 1.000000e+06
#> [266] 2.412945e+00 5.621036e-01 1.493682e-01 3.630333e-01 1.000000e+06
#> [271] 1.000000e+06 1.000000e+06 1.000000e+06 3.408255e-01 1.000000e+06
#> [276] 1.000000e+06 7.908424e-01 1.000000e+06 1.000000e+06 1.000000e+06
#> [281] 1.000000e+06 1.000000e+06 1.445381e-01 8.208844e-01 1.000000e+06
#> [286] 1.086130e+00 9.864368e-01 1.000000e+06 1.000000e+06 2.456039e-01
#> [291] 1.000000e+06 6.505957e-01 1.000000e+06 2.049772e-01 1.000000e+06
#> [296] 4.430880e-01 6.722462e-01 1.240453e+00 1.000000e+06 1.000000e+06Example 3: Binomial Model
Step 1: Running the Priority-Adaptive Elastic Net
# Run priorityelasticnet with Adaptive Elastic Net
result_bin <- priorityelasticnet(X = as.matrix(Pen_Data[, 1:324]), Y = Pen_Data[, 325],
family = "binomial", alpha = 0.5, type.measure = "auc",
blocks = list(bp1 = 1:5, bp2 = 6:179, bp3 = 180:324),
standardize = FALSE,
adaptive = TRUE,
initial_global_weight = FALSE,
verbose = TRUE)
#> Starting priorityelasticnet with 3 blocks.
#> Checking family type and setting default type.measure if necessary...
#> Calculating adaptive weights based on an initial model using block-wise approach...
#> Adaptive weights calculated.
#> Handling missing data based on the provided mcontrol parameters...
#> Fitting model for block 1...
#> Finished processing block 1
#> Fitting model for block 2...
#> Finished processing block 2
#> Fitting model for block 3...
#> Finished processing block 3
#> priorityelasticnet completed successfully.Step 2: Analyzing the Results
result_bin$nzero
#> [[1]]
#> [1] 2
#>
#> [[2]]
#> [1] 57
#>
#> [[3]]
#> [1] 58result_bin$min.cvm
#> [[1]]
#> [1] 0.5327088
#>
#> [[2]]
#> [1] 0.7489871
#>
#> [[3]]
#> [1] 0.9434801result_bin$lambda.min
#> [[1]]
#> [1] 0.0350126
#>
#> [[2]]
#> [1] 9.459303
#>
#> [[3]]
#> [1] 0.1389219result_bin$adaptive_weights
#> [1] 5.141507e+00 6.665180e+00 1.795339e+01 2.644447e+00 1.750974e+01
#> [6] 2.213490e-01 3.738723e-01 5.289715e-01 7.902716e-01 3.392049e-01
#> [11] 2.379471e-01 4.467037e-01 4.085948e-01 4.269249e-01 4.896836e-01
#> [16] 4.105847e-01 7.735520e-02 1.624573e+00 1.890792e-01 8.803842e-01
#> [21] 9.869489e-02 2.806019e+00 1.708301e-01 2.357014e-01 4.980718e-01
#> [26] 2.537087e-01 5.906280e-01 3.930062e-01 2.027173e-01 1.413512e-01
#> [31] 2.273194e-01 2.671471e-01 3.050418e-01 7.244037e-01 5.643993e-01
#> [36] 9.379058e-01 1.649622e-01 2.140122e-01 6.931411e-01 2.160211e-01
#> [41] 3.842417e-01 2.409293e-01 2.955570e-01 4.195737e-01 1.423238e-01
#> [46] 3.379331e-01 1.624112e-01 2.098216e-01 4.041567e+00 1.577413e+00
#> [51] 4.625021e-01 1.465262e-01 1.789429e-01 7.733168e-01 1.021540e+00
#> [56] 3.300157e-01 3.954499e-01 2.927617e-01 2.461243e+00 1.647153e+00
#> [61] 1.238691e+00 8.537096e-01 2.648058e+00 7.972305e-01 1.483216e-01
#> [66] 5.391537e-01 1.930324e-01 3.359328e-01 2.033212e-01 3.600713e-01
#> [71] 1.476049e+00 3.032034e-01 2.174460e-01 2.291870e-01 6.897729e-01
#> [76] 1.871608e-01 4.947961e-01 1.863169e-01 1.578115e-01 4.383793e-01
#> [81] 6.556879e-01 8.618439e-01 6.233920e-01 5.306530e-01 7.647199e-01
#> [86] 2.255225e-01 2.166415e-01 1.185602e+00 1.165359e-01 3.555029e-01
#> [91] 4.610628e-01 1.746584e-01 4.751749e-01 2.178311e-01 2.620764e+01
#> [96] 1.886756e-01 1.614814e-01 2.267271e+00 2.567402e-01 4.960166e-01
#> [101] 3.187648e-01 2.131489e-01 1.011536e+00 3.680132e+00 1.399471e-01
#> [106] 2.202482e-01 2.917678e+00 4.770147e-01 1.997948e+00 5.285475e-01
#> [111] 3.628793e-01 4.554065e-01 2.276452e-01 3.936522e-01 1.471771e-01
#> [116] 2.426163e-01 6.093088e-01 4.507815e-01 6.240798e-01 7.488445e-01
#> [121] 3.865900e-01 7.459232e-01 3.375102e-01 2.607947e-01 4.186712e-01
#> [126] 2.919347e-01 3.047830e-01 3.834317e+00 4.065684e-01 3.490737e-01
#> [131] 1.240864e+04 2.757297e-01 1.026000e+00 2.030740e-01 1.597697e+01
#> [136] 3.744005e-01 2.509052e-01 2.358557e-01 2.535494e+01 8.669322e-01
#> [141] 5.813559e-01 5.095617e-01 2.301583e+00 2.172986e-01 7.302716e-01
#> [146] 3.472889e-01 2.657546e-01 6.092070e-01 5.422842e-01 1.495025e+00
#> [151] 2.547154e+00 2.995004e-01 1.466624e-01 9.875414e-01 1.460359e-01
#> [156] 6.448320e-01 5.750954e-01 4.763194e-01 2.098901e-01 1.150849e+00
#> [161] 3.782637e-01 5.804007e-01 1.170706e+00 1.951343e-01 1.369399e-01
#> [166] 1.911389e-01 3.131722e-01 5.846901e-01 2.169872e-01 1.356158e-01
#> [171] 4.914076e-01 1.416278e-01 2.822715e-01 5.467854e+00 2.424395e-01
#> [176] 1.637018e+00 3.745029e-01 6.958811e-01 9.608760e-01 2.342764e-01
#> [181] 5.106111e-01 1.350857e-01 6.978774e-02 1.773297e-01 1.130147e-01
#> [186] 1.697304e-01 8.966378e-02 6.463426e-02 1.756512e-01 2.109448e+00
#> [191] 1.207999e+00 1.034825e-01 1.167762e-01 3.111523e-01 9.428777e-01
#> [196] 1.218542e-01 2.389807e-01 1.422507e-01 8.561647e-02 7.337985e-01
#> [201] 1.915969e-01 3.470927e+00 3.193872e+00 2.101666e-01 1.136222e-01
#> [206] 1.161560e-01 1.153582e-01 1.985720e-01 1.950585e-01 2.112748e-01
#> [211] 6.526337e-01 2.624724e-01 3.305939e-01 1.065578e-01 4.384431e-02
#> [216] 3.054924e-01 4.279473e-01 1.895131e-01 6.117286e+00 1.626549e+00
#> [221] 8.179891e-02 2.175656e-01 1.611741e-01 8.470114e-02 4.130483e-01
#> [226] 1.749884e-01 1.992047e-01 3.028433e-01 1.402757e-01 1.429396e-01
#> [231] 5.673633e-01 1.264261e-01 4.915812e-01 1.343380e-01 2.316355e-01
#> [236] 8.502451e-01 2.194347e-01 5.805817e-01 1.098710e-01 9.712815e-02
#> [241] 1.250285e+00 1.658560e-01 1.262712e+00 1.552796e-01 1.065677e-01
#> [246] 2.312222e-01 1.350026e+00 3.208817e+00 1.132996e+00 5.096236e-01
#> [251] 2.214812e-01 3.654090e-01 6.090032e+00 1.551206e-01 2.909636e-01
#> [256] 1.494848e+00 1.699816e-01 1.009074e-01 2.554721e-01 1.970378e-01
#> [261] 5.534975e-01 1.763151e-01 9.959403e-02 1.558161e-01 2.258436e-01
#> [266] 6.661475e-02 1.516210e-01 1.217920e-01 1.797447e-01 1.294210e-01
#> [271] 3.088579e-01 9.735091e-02 2.882053e-01 1.963641e-01 2.442122e-01
#> [276] 2.917160e-01 1.301358e-01 5.382903e-02 1.477405e-01 3.368754e-01
#> [281] 1.243082e-01 2.445518e-01 1.691421e-01 2.690473e+00 2.507759e-01
#> [286] 2.454915e+00 3.303527e-01 1.022899e+00 2.507523e-01 6.983933e-02
#> [291] 2.388640e-01 1.063271e-01 2.849451e-01 1.228318e-01 2.132645e+00
#> [296] 4.743187e-01 2.376556e-01 8.721999e+00 1.269434e-01 4.958467e-01
#> [301] 7.556683e-02 1.916197e-01 9.880261e-02 5.153471e-01 2.819146e-01
#> [306] 1.828617e+00 4.846432e+01 1.359844e-01 1.593255e-01 3.151634e-01
#> [311] 1.870832e-01 4.205636e-01 1.703843e-01 2.768101e-01 5.292362e-01
#> [316] 3.662286e-01 2.319133e-01 5.633696e+00 6.780486e-01 1.032382e+00
#> [321] 8.950273e-01 5.365098e-01 1.863496e-01 2.528347e-01result_bin$coefficients
#> Clinical_Var1 Clinical_Var2 Clinical_Var3 Clinical_Var4
#> 0.110757676 0.000000000 0.000000000 -0.247222984
#> Clinical_Var5 Proteomic_Var1 Proteomic_Var2 Proteomic_Var3
#> 0.000000000 0.149764717 0.000000000 0.000000000
#> Proteomic_Var4 Proteomic_Var5 Proteomic_Var6 Proteomic_Var7
#> 0.000000000 0.000000000 0.096049370 0.002508445
#> Proteomic_Var8 Proteomic_Var9 Proteomic_Var10 Proteomic_Var11
#> 0.000000000 0.000000000 0.013534646 0.000000000
#> Proteomic_Var12 Proteomic_Var13 Proteomic_Var14 Proteomic_Var15
#> 0.389373510 0.000000000 0.407888866 0.000000000
#> Proteomic_Var16 Proteomic_Var17 Proteomic_Var18 Proteomic_Var19
#> 0.358090502 0.000000000 0.318959760 0.001416932
#> Proteomic_Var20 Proteomic_Var21 Proteomic_Var22 Proteomic_Var23
#> 0.012862137 0.046407521 0.000000000 0.070906591
#> Proteomic_Var24 Proteomic_Var25 Proteomic_Var26 Proteomic_Var27
#> 0.063383938 0.170489686 0.237954590 0.177944249
#> Proteomic_Var28 Proteomic_Var29 Proteomic_Var30 Proteomic_Var31
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var32 Proteomic_Var33 Proteomic_Var34 Proteomic_Var35
#> 0.116970178 0.000000000 0.000000000 0.161971832
#> Proteomic_Var36 Proteomic_Var37 Proteomic_Var38 Proteomic_Var39
#> 0.000000000 0.112049130 0.045874154 0.000000000
#> Proteomic_Var40 Proteomic_Var41 Proteomic_Var42 Proteomic_Var43
#> 0.246711417 0.000000000 0.361395282 0.220712771
#> Proteomic_Var44 Proteomic_Var45 Proteomic_Var46 Proteomic_Var47
#> 0.000000000 0.000000000 0.000000000 0.304745414
#> Proteomic_Var48 Proteomic_Var49 Proteomic_Var50 Proteomic_Var51
#> 0.283544927 0.000000000 0.000000000 0.000000000
#> Proteomic_Var52 Proteomic_Var53 Proteomic_Var54 Proteomic_Var55
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var56 Proteomic_Var57 Proteomic_Var58 Proteomic_Var59
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var60 Proteomic_Var61 Proteomic_Var62 Proteomic_Var63
#> 0.120381156 0.000000000 -0.136989812 0.000000000
#> Proteomic_Var64 Proteomic_Var65 Proteomic_Var66 Proteomic_Var67
#> 0.131638693 0.000000000 0.000000000 0.000000000
#> Proteomic_Var68 Proteomic_Var69 Proteomic_Var70 Proteomic_Var71
#> 0.149028545 0.087780627 0.000000000 0.080253942
#> Proteomic_Var72 Proteomic_Var73 Proteomic_Var74 Proteomic_Var75
#> 0.000000000 -0.018675119 0.000000000 0.000000000
#> Proteomic_Var76 Proteomic_Var77 Proteomic_Var78 Proteomic_Var79
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var80 Proteomic_Var81 Proteomic_Var82 Proteomic_Var83
#> 0.000000000 0.048839677 0.023659403 0.000000000
#> Proteomic_Var84 Proteomic_Var85 Proteomic_Var86 Proteomic_Var87
#> 0.237133456 0.072988329 0.000000000 0.167704533
#> Proteomic_Var88 Proteomic_Var89 Proteomic_Var90 Proteomic_Var91
#> 0.000000000 0.103523587 0.000000000 0.000000000
#> Proteomic_Var92 Proteomic_Var93 Proteomic_Var94 Proteomic_Var95
#> 0.162162661 0.000000000 0.055699797 0.000000000
#> Proteomic_Var96 Proteomic_Var97 Proteomic_Var98 Proteomic_Var99
#> 0.000000000 0.123102916 0.000000000 0.000000000
#> Proteomic_Var100 Proteomic_Var101 Proteomic_Var102 Proteomic_Var103
#> 0.288957079 -0.030542891 0.000000000 0.000000000
#> Proteomic_Var104 Proteomic_Var105 Proteomic_Var106 Proteomic_Var107
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var108 Proteomic_Var109 Proteomic_Var110 Proteomic_Var111
#> -0.037597372 0.000000000 0.071064166 0.000000000
#> Proteomic_Var112 Proteomic_Var113 Proteomic_Var114 Proteomic_Var115
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var116 Proteomic_Var117 Proteomic_Var118 Proteomic_Var119
#> 0.000000000 0.000000000 0.000000000 0.120608069
#> Proteomic_Var120 Proteomic_Var121 Proteomic_Var122 Proteomic_Var123
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var124 Proteomic_Var125 Proteomic_Var126 Proteomic_Var127
#> -0.026905297 0.000000000 0.000000000 0.086135671
#> Proteomic_Var128 Proteomic_Var129 Proteomic_Var130 Proteomic_Var131
#> 0.000000000 0.126026780 0.000000000 0.000000000
#> Proteomic_Var132 Proteomic_Var133 Proteomic_Var134 Proteomic_Var135
#> 0.043210490 0.000000000 0.000000000 0.000000000
#> Proteomic_Var136 Proteomic_Var137 Proteomic_Var138 Proteomic_Var139
#> 0.000000000 0.000000000 0.000000000 -0.161198105
#> Proteomic_Var140 Proteomic_Var141 Proteomic_Var142 Proteomic_Var143
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var144 Proteomic_Var145 Proteomic_Var146 Proteomic_Var147
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var148 Proteomic_Var149 Proteomic_Var150 Proteomic_Var151
#> -0.190403902 0.000000000 0.000000000 0.000000000
#> Proteomic_Var152 Proteomic_Var153 Proteomic_Var154 Proteomic_Var155
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var156 Proteomic_Var157 Proteomic_Var158 Proteomic_Var159
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var160 Proteomic_Var161 Proteomic_Var162 Proteomic_Var163
#> -0.372156845 0.053789347 0.000000000 0.000000000
#> Proteomic_Var164 Proteomic_Var165 Proteomic_Var166 Proteomic_Var167
#> -0.001086006 0.079702701 0.000000000 -0.142427676
#> Proteomic_Var168 Proteomic_Var169 Proteomic_Var170 Proteomic_Var171
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> Proteomic_Var172 Proteomic_Var173 Proteomic_Var174 RNA_Var1
#> 0.000000000 0.000000000 0.000000000 0.065907003
#> RNA_Var2 RNA_Var3 RNA_Var4 RNA_Var5
#> 0.000000000 0.136837594 0.394546527 0.000000000
#> RNA_Var6 RNA_Var7 RNA_Var8 RNA_Var9
#> 0.112994792 0.124576310 0.143440941 0.247333718
#> RNA_Var10 RNA_Var11 RNA_Var12 RNA_Var13
#> 0.085986117 0.000000000 0.000000000 0.253351889
#> RNA_Var14 RNA_Var15 RNA_Var16 RNA_Var17
#> 0.047375454 0.033619585 0.000000000 0.187523968
#> RNA_Var18 RNA_Var19 RNA_Var20 RNA_Var21
#> 0.000000000 0.196582754 0.301462230 0.000000000
#> RNA_Var22 RNA_Var23 RNA_Var24 RNA_Var25
#> 0.077031359 0.000000000 0.000000000 0.000000000
#> RNA_Var26 RNA_Var27 RNA_Var28 RNA_Var29
#> 0.143712390 0.194968208 0.000000000 0.207773756
#> RNA_Var30 RNA_Var31 RNA_Var32 RNA_Var33
#> 0.041012364 0.000000000 0.000000000 0.000000000
#> RNA_Var34 RNA_Var35 RNA_Var36 RNA_Var37
#> 0.000000000 0.138136118 0.331798049 0.065895039
#> RNA_Var38 RNA_Var39 RNA_Var40 RNA_Var41
#> 0.000000000 0.217585783 0.000000000 0.000000000
#> RNA_Var42 RNA_Var43 RNA_Var44 RNA_Var45
#> 0.187029131 0.000000000 0.000000000 0.413866221
#> RNA_Var46 RNA_Var47 RNA_Var48 RNA_Var49
#> 0.000000000 0.028835385 0.000000000 0.000000000
#> RNA_Var50 RNA_Var51 RNA_Var52 RNA_Var53
#> 0.351990574 0.000000000 0.000000000 0.116741992
#> RNA_Var54 RNA_Var55 RNA_Var56 RNA_Var57
#> 0.000000000 0.271911885 0.102169949 0.000000000
#> RNA_Var58 RNA_Var59 RNA_Var60 RNA_Var61
#> 0.000000000 0.000000000 0.099498503 0.234555626
#> RNA_Var62 RNA_Var63 RNA_Var64 RNA_Var65
#> 0.000000000 0.000000000 0.000000000 0.221279065
#> RNA_Var66 RNA_Var67 RNA_Var68 RNA_Var69
#> 0.031175161 0.000000000 0.000000000 0.000000000
#> RNA_Var70 RNA_Var71 RNA_Var72 RNA_Var73
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var74 RNA_Var75 RNA_Var76 RNA_Var77
#> 0.000000000 0.065334685 0.000000000 0.000000000
#> RNA_Var78 RNA_Var79 RNA_Var80 RNA_Var81
#> 0.096786739 0.000000000 0.000000000 0.011085624
#> RNA_Var82 RNA_Var83 RNA_Var84 RNA_Var85
#> 0.000000000 0.034267824 0.282633267 0.087678733
#> RNA_Var86 RNA_Var87 RNA_Var88 RNA_Var89
#> 0.049113234 0.334089788 0.060512522 0.005001527
#> RNA_Var90 RNA_Var91 RNA_Var92 RNA_Var93
#> 0.000000000 0.142042097 0.000000000 0.186829238
#> RNA_Var94 RNA_Var95 RNA_Var96 RNA_Var97
#> 0.000000000 0.000000000 0.070510312 0.000000000
#> RNA_Var98 RNA_Var99 RNA_Var100 RNA_Var101
#> 0.000000000 0.397544877 0.000000000 0.000000000
#> RNA_Var102 RNA_Var103 RNA_Var104 RNA_Var105
#> -0.187227496 0.000000000 -0.037373253 0.000000000
#> RNA_Var106 RNA_Var107 RNA_Var108 RNA_Var109
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var110 RNA_Var111 RNA_Var112 RNA_Var113
#> 0.000000000 -0.298824525 0.000000000 -0.124613340
#> RNA_Var114 RNA_Var115 RNA_Var116 RNA_Var117
#> 0.000000000 -0.016945718 0.000000000 0.000000000
#> RNA_Var118 RNA_Var119 RNA_Var120 RNA_Var121
#> 0.000000000 0.000000000 0.053234129 0.000000000
#> RNA_Var122 RNA_Var123 RNA_Var124 RNA_Var125
#> 0.202222154 0.000000000 0.036071475 0.000000000
#> RNA_Var126 RNA_Var127 RNA_Var128 RNA_Var129
#> 0.000000000 0.000000000 0.000000000 0.060807603
#> RNA_Var130 RNA_Var131 RNA_Var132 RNA_Var133
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var134 RNA_Var135 RNA_Var136 RNA_Var137
#> 0.000000000 0.000000000 0.000000000 0.000000000
#> RNA_Var138 RNA_Var139 RNA_Var140 RNA_Var141
#> -0.065737009 0.000000000 0.000000000 0.000000000
#> RNA_Var142 RNA_Var143 RNA_Var144 RNA_Var145
#> 0.000000000 0.000000000 0.000000000 0.000000000predictions <- predict(result_bin, newdata = as.matrix(Pen_Data[, 1:324]), type = "response")
head(predictions)
#> [,1]
#> [1,] 0.1952048
#> [2,] 0.9894019
#> [3,] 0.3632325
#> [4,] 0.4123135
#> [5,] 0.9669972
#> [6,] 0.2112270library(pROC)
roc_curve <- roc(Pen_Data[, 325], predictions[,1])
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
plot(roc_curve, col = "red", main = "ROC Curve for Binomial Model")
text(0.1, 0.1, labels = paste("AUC =", round(roc_curve$auc, 2)), col = "black", cex = 1.2)
The Priority-Adaptive elastic net outperforms the Priority-elastic net for the binomial family, as shown by the Area Under the ROC Curve (AUC). The Priority-Adaptive elastic net achieves an AUC of 0.99, indicating near-perfect discrimination, compared to 0.96 for the Priority-elastic net. This improvement stems from its adaptive weighting scheme, which dynamically prioritizes important predictors, unlike the fixed penalties in the Priority-Elastic net. The higher AUC underscores the enhanced predictive accuracy of the Priority-Adaptive elastic net.
Example 4: Multinomial Model
# Set seed for reproducibility
set.seed(123)
# Number of observations and predictors
n <- 100 # Number of observations
p <- 50 # Number of predictors
k <- 3 # Number of classes
# Simulate a matrix of predictors
x <- matrix(rnorm(n * p), n, p)
# Simulate a response vector with three classes
y <- factor(sample(1:k, n, replace = TRUE))Step 1: Define Predictor Blocks
blocks <- list(
block1 = 1:10, # First block with predictors 1 to 10
block2 = 11:30, # Second block with predictors 11 to 30
block3 = 31:50 # Third block with predictors 31 to 50
)Step 2: Running the Priority-Adaptive Elastic Net
# Run priorityelasticnet
result_multinom <- priorityelasticnet(
X = x,
Y = y,
family = "multinomial",
alpha = 0.5,
type.measure = "class",
blocks = blocks,
block1.penalization = TRUE,
lambda.type = "lambda.min",
standardize = TRUE,
nfolds = 10,
adaptive = TRUE,
initial_global_weight = FALSE
)
Step 3: Analyzing the Results
result_multinom$coefficients
#> [[1]]
#> [,1] [,2] [,3]
#> V1 0 0 0
#> V2 0 0 0
#> V3 0 0 0
#> V4 0 0 0
#> V5 0 0 0
#> V6 0 0 0
#> V7 0 0 0
#> V8 0 0 0
#> V9 0 0 0
#> V10 0 0 0
#>
#> [[2]]
#> [,1] [,2] [,3]
#> V1 0.00000000 0 0.0000000
#> V2 0.00000000 0 0.0000000
#> V3 0.00000000 0 0.0000000
#> V4 0.00000000 0 0.1495269
#> V5 0.00000000 0 0.0000000
#> V6 -0.02600309 0 0.0000000
#> V7 0.00000000 0 0.0000000
#> V8 0.00000000 0 0.0000000
#> V9 0.00000000 0 0.0000000
#> V10 0.01075062 0 0.0000000
#> V11 0.00000000 0 0.0000000
#> V12 0.00000000 0 0.0000000
#> V13 0.00000000 0 0.0000000
#> V14 0.00000000 0 0.0000000
#> V15 0.00000000 0 0.0000000
#> V16 0.00000000 0 0.0000000
#> V17 0.09127639 0 0.0000000
#> V18 0.09546645 0 0.0000000
#> V19 0.00000000 0 0.0000000
#> V20 0.00000000 0 0.0000000
#>
#> [[3]]
#> [,1] [,2] [,3]
#> V1 0.57252568 -0.15246684 0.00000000
#> V2 -0.92682099 0.00000000 0.08171181
#> V3 0.06224417 0.00000000 -0.39942375
#> V4 0.00000000 0.00000000 0.00000000
#> V5 -0.11573636 0.01275773 0.00000000
#> V6 -0.68798760 0.09236136 0.00000000
#> V7 -0.09335892 0.20794089 0.00000000
#> V8 0.16701044 0.00000000 -0.10364456
#> V9 0.39945279 0.00000000 -0.33714098
#> V10 0.03240177 0.00000000 -0.75205685
#> V11 -0.29958401 0.00000000 0.01021199
#> V12 0.00000000 0.00000000 0.00000000
#> V13 0.00000000 0.00000000 0.00000000
#> V14 0.00000000 0.00000000 0.00000000
#> V15 -0.09725444 0.00000000 0.59774352
#> V16 0.00000000 0.00000000 0.00000000
#> V17 -0.39321351 0.00000000 0.09891149
#> V18 0.56782163 0.00000000 -0.66334345
#> V19 0.28051789 0.00000000 -0.21987625
#> V20 0.30125051 -0.49464974 0.00000000result_multinom$adaptive
#> [1] TRUEresult_multinom$adaptive_weights
#> [1] 2.578630e+00 7.020071e+00 1.000000e+06 1.149693e+01 5.201818e+00
#> [6] 4.216925e+00 1.096730e+01 1.000000e+06 1.000000e+06 6.571876e+00
#> [11] 7.793152e+00 4.891840e+00 1.000000e+06 4.512051e+00 3.734899e+00
#> [16] 1.770678e+00 4.186298e+00 2.729921e+01 1.000000e+06 3.195098e+00
#> [21] 1.000000e+06 1.000000e+06 3.100487e+00 9.139896e+01 7.803041e+00
#> [26] 1.086459e+01 2.761149e+00 2.155480e+00 5.749804e+00 6.128405e+00
#> [31] 1.621161e+00 1.003146e+00 1.455475e+01 1.000000e+06 8.071559e+00
#> [36] 1.324280e+00 9.840380e+00 5.196698e+00 2.181014e+00 2.302390e+01
#> [41] 3.328951e+00 1.000000e+06 2.217256e+01 1.000000e+06 1.023540e+01
#> [46] 1.000000e+06 2.295208e+00 1.537817e+00 3.974199e+00 2.907481e+00result_multinom$glmnet.fit
#> [[1]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 13990.0
#> 2 1 0.12 12750.0
#> 3 1 0.22 11620.0
#> 4 2 0.33 10580.0
#> 5 2 0.54 9644.0
#> 6 2 0.73 8787.0
#> 7 2 0.90 8006.0
#> 8 2 1.05 7295.0
#> 9 2 1.18 6647.0
#> 10 2 1.30 6056.0
#> 11 2 1.42 5518.0
#> 12 2 1.53 5028.0
#> 13 2 1.62 4581.0
#> 14 2 1.69 4174.0
#> 15 3 1.82 3804.0
#> 16 3 1.94 3466.0
#> 17 4 2.04 3158.0
#> 18 4 2.17 2877.0
#> 19 4 2.30 2622.0
#> 20 5 2.43 2389.0
#> 21 5 2.58 2177.0
#> 22 5 2.70 1983.0
#> 23 5 2.81 1807.0
#> 24 5 2.90 1646.0
#> 25 5 2.98 1500.0
#> 26 6 3.08 1367.0
#> 27 7 3.17 1246.0
#> 28 7 3.25 1135.0
#> 29 7 3.32 1034.0
#> 30 7 3.38 942.2
#> 31 7 3.43 858.5
#> 32 7 3.48 782.2
#> 33 7 3.52 712.7
#> 34 7 3.55 649.4
#> 35 7 3.57 591.7
#> 36 7 3.60 539.2
#> 37 7 3.62 491.3
#> 38 7 3.63 447.6
#> 39 7 3.65 407.8
#> 40 7 3.66 371.6
#> 41 7 3.67 338.6
#> 42 7 3.68 308.5
#> 43 7 3.68 281.1
#> 44 7 3.69 256.1
#> 45 7 3.69 233.4
#> 46 7 3.70 212.7
#> 47 7 3.70 193.8
#> 48 7 3.70 176.5
#> 49 7 3.70 160.9
#> 50 7 3.71 146.6
#> 51 7 3.71 133.6
#> 52 7 3.71 121.7
#> 53 7 3.71 110.9
#> 54 7 3.71 101.0
#>
#> [[2]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 11520.0
#> 2 2 0.47 10500.0
#> 3 3 1.07 9563.0
#> 4 3 1.61 8714.0
#> 5 4 2.13 7940.0
#> 6 5 2.68 7234.0
#> 7 5 3.25 6592.0
#> 8 5 3.77 6006.0
#> 9 6 4.33 5473.0
#> 10 7 4.96 4986.0
#> 11 9 5.61 4543.0
#> 12 9 6.30 4140.0
#> 13 10 6.96 3772.0
#> 14 10 7.67 3437.0
#> 15 10 8.30 3132.0
#> 16 11 8.89 2853.0
#> 17 13 9.56 2600.0
#> 18 13 10.28 2369.0
#> 19 13 10.92 2158.0
#> 20 13 11.48 1967.0
#> 21 13 11.97 1792.0
#> 22 13 12.41 1633.0
#> 23 13 12.79 1488.0
#> 24 13 13.12 1356.0
#> 25 13 13.41 1235.0
#> 26 13 13.66 1125.0
#> 27 13 13.88 1025.0
#> 28 14 14.09 934.4
#> 29 14 14.30 851.4
#> 30 14 14.49 775.7
#> 31 14 14.66 706.8
#> 32 14 14.80 644.0
#> 33 14 14.92 586.8
#> 34 14 15.03 534.7
#> 35 14 15.12 487.2
#> 36 15 15.23 443.9
#> 37 15 15.32 404.5
#> 38 15 15.41 368.5
#> 39 15 15.48 335.8
#> 40 15 15.54 306.0
#> 41 15 15.59 278.8
#> 42 15 15.64 254.0
#> 43 15 15.68 231.4
#> 44 15 15.71 210.9
#> 45 15 15.74 192.2
#> 46 15 15.76 175.1
#> 47 15 15.78 159.5
#> 48 15 15.80 145.4
#> 49 15 15.81 132.4
#> 50 15 15.82 120.7
#> 51 16 15.86 110.0
#> 52 16 15.89 100.2
#> 53 16 15.92 91.3
#> 54 16 15.95 83.2
#> 55 16 15.97 75.8
#> 56 16 15.99 69.1
#> 57 16 16.01 62.9
#> 58 16 16.02 57.3
#> 59 16 16.04 52.2
#> 60 16 16.05 47.6
#> 61 16 16.07 43.4
#> 62 16 16.08 39.5
#> 63 16 16.08 36.0
#> 64 16 16.09 32.8
#> 65 16 16.10 29.9
#> 66 16 16.10 27.2
#> 67 16 16.11 24.8
#> 68 16 16.11 22.6
#> 69 16 16.11 20.6
#> 70 16 16.11 18.8
#> 71 16 16.12 17.1
#> 72 16 16.12 15.6
#> 73 16 16.12 14.2
#> 74 16 16.12 12.9
#> 75 16 16.12 11.8
#>
#> [[3]]
#>
#> Call: glmnet(x = X[current_observations, actual_block], y = Y[current_observations], weights = weights[current_observations], offset = offset_matrix, family = family, alpha = alpha, standardize = standardize, penalty.factor = penalty.factor)
#>
#> Df %Dev Lambda
#> 1 0 0.00 35290
#> 2 1 0.40 32160
#> 3 1 0.77 29300
#> 4 2 1.21 26700
#> 5 2 1.67 24330
#> 6 3 2.27 22170
#> 7 3 2.85 20200
#> 8 3 3.37 18400
#> 9 5 3.95 16770
#> 10 5 5.02 15280
#> 11 5 5.99 13920
#> 12 5 6.86 12680
#> 13 5 7.64 11560
#> 14 5 8.34 10530
#> 15 5 8.96 9595
#> 16 6 9.55 8742
#> 17 6 10.18 7966
#> 18 6 10.76 7258
#> 19 7 11.31 6613
#> 20 7 11.86 6026
#> 21 7 12.39 5490
#> 22 7 12.86 5003
#> 23 7 13.27 4558
#> 24 7 13.63 4153
#> 25 8 14.16 3784
#> 26 8 14.70 3448
#> 27 9 15.21 3142
#> 28 10 15.69 2863
#> 29 10 16.18 2608
#> 30 10 16.63 2377
#> 31 10 17.02 2166
#> 32 12 17.74 1973
#> 33 12 18.49 1798
#> 34 13 19.20 1638
#> 35 14 19.99 1493
#> 36 14 20.71 1360
#> 37 14 21.37 1239
#> 38 14 21.96 1129
#> 39 14 22.49 1029
#> 40 14 22.97 937
#> 41 14 23.40 854
#> 42 14 23.79 778
#> 43 14 24.15 709
#> 44 14 24.46 646
#> 45 14 24.75 589
#> 46 15 25.00 536
#> 47 15 25.24 489
#> 48 15 25.45 445
#> 49 15 25.64 406
#> 50 15 25.80 370
#> 51 15 25.95 337
#> 52 15 26.08 307
#> 53 15 26.19 280
#> 54 15 26.29 255
#> 55 15 26.37 232
#> 56 15 26.45 212
#> 57 15 26.51 193
#> 58 15 26.57 176
#> 59 15 26.62 160
#> 60 15 26.66 146
#> 61 15 26.70 133
#> 62 15 26.73 121
#> 63 15 26.76 110
#> 64 15 26.78 100
#> 65 15 26.80 92
#> 66 15 26.81 83
#> 67 15 26.83 76
#> 68 15 26.84 69
#> 69 15 26.85 63
#> 70 15 26.86 58
#> 71 15 26.86 52
#> 72 15 26.87 48
#> 73 15 26.87 44
#> 74 15 26.88 40
#> 75 15 26.88 36
#> 76 15 26.88 33
#> 77 15 26.89 30
#> 78 16 26.89 27
#> 79 16 26.89 25
#> 80 16 26.89 23
#> 81 16 26.89 21
#> 82 16 26.89 19Evaluate the Model
result_multinom$min.cvm
#> [[1]]
#> [1] 0.59
#>
#> [[2]]
#> [1] 0.56
#>
#> [[3]]
#> [1] 0.51The results show that the adaptive version of Priority-elastic net improves performance compared to the non-adaptive version for a multinomial family.
result_multinom$lambda.min
#> [[1]]
#> [1] 13991.08
#>
#> [[2]]
#> [1] 7234.381
#>
#> [[3]]
#> [1] 110.3167The values of lambda.min (13991.08, 7234.381, 110.3167) are significantly larger.
This is because the adaptive approach applies data-driven weights to the penalty terms, focusing on important predictors. By adaptively reducing penalties on key variables and increasing penalties on others, the effective regularization strength (lambda) can increase.
Conclusion
The priorityelasticnet package is a powerful tool for
high-dimensional data analysis, particularly when dealing with grouped
predictors and the need for flexible penalization strategies. Its design
caters to the needs of researchers and data scientists working with
complex datasets where traditional modeling approaches may fall
short.
One of the standout features of this package is the Adaptive-Elastic net regularization, which enhances traditional regularization methods by applying penalties that are adjusted based on the importance of the predictors. This allows for more nuanced and effective feature selection, especially in scenarios where predictors vary widely in their relevance to the response variable. By retaining important predictors while shrinking less relevant ones, the Adaptive-Elastic net significantly improves model accuracy and interpretability.
The examples and explanations provided in this vignette should give
you a solid foundation to start using this package effectively in your
analyses. From block-wise penalization to handling missing data,
optimizing thresholds in binary classification, and leveraging the
Adaptive-Elastic net, priorityelasticnet offers a wide
range of functionalities that are crucial for building robust models.
The ability to handle various types of data structures and provide
tailored regularization across different groups of predictors makes it
an invaluable tool for both exploratory and confirmatory data
analysis.