| Title: | Maximum Diversity Weighting |
| Version: | 2024.8-1 |
| Description: | Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <doi:10.1002/sim.8212>. |
| Depends: | R (≥ 3.5.0) |
| Suggests: | R.rsp, RUnit, Rmosek, mvtnorm, gtools |
| Imports: | kyotil, MASS, Matrix |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| VignetteBuilder: | R.rsp |
| NeedsCompilation: | no |
| Packaged: | 2024-07-31 22:41:45 UTC; Youyi |
| Author: | Zonglin He [aut], Youyi Fong [cre] |
| Maintainer: | Youyi Fong <youyifong@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-07-31 23:00:02 UTC |
Asymptotic variance for maximum entropy weights
Description
asym.v.e produces estimated asymptotic covariance matrix of the first p-1 maximum entropy weights (because the p weights sum to 1).
Usage
asym.v.e(X, w, h)
Arguments
X |
n by p maxtrix containing observations of p biomarkers of n subjects. |
w |
maximum entropy weights for dateset X with bandwidth h used |
h |
bandwidth for kernel density estimation. |
Examples
library(MASS)
# a three biomarkers dataset generated from independent normal(0,1)
X = mvrnorm(n = 100, mu=rep(0,3), Sigma=diag(3), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
h = 1
w <- entropy.weight(X,h)
asym.v.e(X,w,h)
Asymptotic variance for maximum variance weights
Description
asym.v.v produces estimated asymptotic covariance matrix of the first p-1 maximum variance weights (because the p weights sum to 1).
Usage
asym.v.v(X, w)
Arguments
X |
n by p maxtrix containing observations of p biomarkers of n subjects. |
w |
maximum variance weights for dateset X |
Examples
library(MASS)
# a three biomarkers dataset generated from independent normal(0,1)
X = mvrnorm(n = 100, mu=rep(0,3), Sigma=diag(3), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
w <- var.weight(X)
asym.v.v(X,w)
Maximum entropy weights
Description
entropy.weight produces a set of weights that maximizes the total weighted entropy of the distribution of different biomarkers within each subject, values of biomarkers can be either continuous or categorical.
Usage
entropy.weight(X, h)
Arguments
X |
n by p maxtrix containing observations of p biomarkers of n subjects. |
h |
bandwidth for kernel density estimation. if data is categorical, set to 'na'. |
Examples
library(MASS)
# a three biomarkers dataset generated from independent normal(0,1)
set.seed(1)
X = mvrnorm(n = 100, mu=rep(0,3), Sigma=diag(3), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
entropy.weight(X, h=1)
###
# a three categorical biomarkers dataset
set.seed(1)
tmp=mvrnorm(n=10,mu=c(0,0,0),Sigma = diag(3))
dat=t(apply(tmp, 1, function(x) cut(x,c(-Inf,-0.5,0.5,Inf),labels=1:3)))
entropy.weight(dat,h='na')
Bandwidth Selection
Description
get.bw applies a specified bandwidth selection method to the dataset subject-wisely and return the median of the n selected bandwidths as the choice of bandwidth for entropy.weight.
Usage
get.bw(x, bw = c("nrd", "ucv", "bcv", "SJ"), nb)
Arguments
x |
n by p maxtrix containing observations of p biomarkers of n subjects. |
bw |
bandwidth selectors of nrd, ucv, bcv, and SJ corresponding to R functions bw.nrd, bw.ucv, bw.bcv, and bw.SJ. |
nb |
number of bins to use, 'na' if bw='nrd' |
Examples
library(MASS)
# a ten biomarkers dataset generated from independent normal(0,1)
x = mvrnorm(n = 100, mu=rep(0,10), Sigma=diag(10), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
get.bw(x,bw='ucv',nb=100)
get.bw(x,bw='nrd',nb='na')
mdw Package
Description
Please see the Index link below for a list of available functions.
Weights based on PCA
Description
pca.weight produce the coefficients of the first principal compoment
Usage
pca.weight(emp.cor)
Arguments
emp.cor |
empirical correlation matrix of the dataset |
Examples
library(MASS)
# a three biomarkers dataset generated from independent normal(0,1)
X = mvrnorm(n = 100, mu=rep(0,3), Sigma=diag(3), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
emp.cor <- cor(X)
pca.weight(emp.cor)
Weights based on GSC Tree Method
Description
tree.weight Produce a set of weights for different end points based on a correlation matrix using the GSC tree method
Usage
tree.weight (cor.mat, method="GSC", clustering.method="average", plot=TRUE,
orientation=c("vertical","horizontal"), ...)
Arguments
cor.mat |
a matrix, correlation matrix |
method |
a string. GSC, implementation of Gerstein et al., is the only implemented currently |
clustering.method |
a string, how the bottom-up hierarchical clustering tree is built, is passed to hclust as the method parameter |
plot |
a Boolean, whether to plot the tree |
orientation |
vertical or horizontal |
... |
additional args |
Value
A vector of weights that sum to 1.
Author(s)
Youyi Fong yfong@fhcrc.org
References
Gerstein, M., Sonnhammer, E., and Chothia, C. (1994), Volume changes in protein evolution. J Mol Biol, 236, 1067-78.
Examples
cor.mat=diag(rep(1,3))
cor.mat[1,2]<-cor.mat[2,1]<-0.9
cor.mat[1,3]<-cor.mat[3,1]<-0.1
cor.mat[2,3]<-cor.mat[3,2]<-0.1
tree.weight(cor.mat)
Maximum variance weights
Description
var.weight produces a set of weights that maximizes the total weighted variance of the distribution of different biomarkers within each subject.
Usage
var.weight(X, method = c("optim", "mosek"))
Arguments
X |
n by p maxtrix containing observations of p biomarkers of n subjects. |
method |
optim (default) using R constrOptim function from stats package for optimization, mosek using mosek function from Rmosek package for optimization |
Examples
library(MASS)
# a three biomarkers dataset generated from independent normal(0,1)
X = mvrnorm(n = 100, mu=rep(0,3), Sigma=diag(3), tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
# compute maximum variance weights using constrOptim for optimization
var.weight(X)
## Not run:
# need mosek installed
# compute maximum variance weights using mosek for optimization
library(Rmosek)
var.weight(X,'mosek')
## End(Not run)