Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

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Documentation for package ‘glmnetr’ version 0.5-5

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

aicreg	Identify model based upon AIC criteria from a stepreg() putput
ann_tab_cv	Fit an Artificial Neural Network model on "tabular" provided as a matrix, optionally allowing for an offset term
ann_tab_cv_best	Fit multiple Artificial Neural Network models on "tabular" provided as a matrix, and keep the best one.
best.preds	Get the best models for the steps of a stepreg() fit
boot.factor.foldid	Generate foldid's by 0/1 factor for bootstrap like samples where unique option between 0 and 1
calceloss	calculate cross-entry for multinomial outcomes
calplot	Construct calibration plots for a nested.glmnetr output object
cox.sat.dev	Calculate the CoxPH saturated log-likelihood
cv.glmnetr	Get a cross validation informed relaxed lasso model fit.
cv.stepreg	Cross validation informed stepwise regression model fit.
devrat_	Calculate deviance ratios for CV based
diff_time	Output to console the elapsed and split times
diff_time1	Get elapsed time in c(hour, minute, secs)
factor.foldid	Generate foldid's by factor levels
get.foldid	Get foldid's with branching for cox, binomial and gaussian models
get.id.foldid	Get foldid's when id variable is used to identify groups of dependent sampling units. With branching for cox, binomial and gaussian models
glmnetr	Fit relaxed part of lasso model
glmnetr.cis	A redirect to nested.cis()
glmnetr.compcv	A redirect to nested.compare
glmnetr.simdata	Generate example data
glmnetr_seed	Get seeds to store, facilitating replicable results
nested.cis	Calculate performance measure "nominal" CI's and p's
nested.compare	Compare cross validation fit performances from a nested.glmnetr output.
nested.compare_0_5_1	Compare cross validation fit performances from a nested.glmnetr output.
nested.glmnetr	Using (nested) cross validation, describe and compare some machine learning model performances
orf_tune	Fit a Random Forest model on data provided in matrix and vector formats.
plot.cv.glmnetr	Plot cross-validation deviances, or model coefficients.
plot.glmnetr	Plot the relaxed lasso coefficients.
plot.nested.glmnetr	Plot results from a nested.glmnetr() output
plot_perf_glmnetr	Plot nested cross validation performance summaries
predict.cv.glmnetr	Give predicteds based upon a cv.glmnetr() output object.
predict.cv.stepreg	Beta's or predicteds based upon a cv.stepreg() output object.
predict.glmnetr	Get predicteds or coefficients using a glmnetr output object
predict.nested.glmnetr	Give predicteds based upon the cv.glmnet output object contained in the nested.glmnetr output object.
predict_ann_tab	Get predicteds for an Artificial Neural Network model fit in nested.glmnetr()
print.nested.glmnetr	A redirect to the summary() function for nested.glmnetr() output objects
print.orf_tune	Print output from orf_tune() function
print.rf_tune	Print output from rf_tune() function
rederive_orf	Rederive Oblique Random Forest models not kept in nested.glmnetr() output
rederive_rf	Rederive Random Forest models not kept in nested.glmnetr() output
rederive_xgb	Rederive XGB models not kept in nested.glmnetr() output
rf_tune	Fit a Random Forest model on data provided in matrix and vector formats.
roundperf	round elements of a summary.glmnetr() output
stepreg	Fit the steps of a stepwise regression.
summary.cv.glmnetr	Output summary of a cv.glmnetr() output object.
summary.cv.stepreg	Summarize results from a cv.stepreg() output object.
summary.nested.glmnetr	Summarize a nested.glmnetr() output object
summary.orf_tune	Summarize output from rf_tune() function
summary.rf_tune	Summarize output from rf_tune() function
summary.stepreg	Briefly summarize steps in a stepreg() output object, i.e. a stepwise regression fit
xgb.simple	Get a simple XGBoost model fit (no tuning)
xgb.tuned	Get a tuned XGBoost model fit