plsgenomics (version 1.5-3)

stability.selection.heatmap: Heatmap visualization of estimated probabilities of selection for each covariate

Description

The function stability.selection.heatmap allows to visualize estimated probabilities to be selected for each covariate depending on the value of hyper-parameters in the spls, logit-spls or multinomial-spls models. These estimated probabilities are used in the stability selection procedure described in Durif et al. (2018).

Usage

stability.selection.heatmap(stab.out, ...)

Value

No return, just plot the heatmap in the current graphic window.

Arguments

stab.out

the output of the functions spls.stab, logit.spls.stab or multinom.spls.stab.

...

any argument that could be pass to the functions image.plot or image.

Author

Ghislain Durif (https://gdurif.perso.math.cnrs.fr/).

Details

The procedure is described in Durif et al. (2018). The stability selection procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).

(i) For each candidate values of hyper-parameters, a model is trained on nresamp resamplings of the data. Then, for each candidate value of the hyper-parameters, the probability that a covariate (i.e. a column in X) is selected is computed among the resamplings.

The estimated probabilities can be visualized as a heatmap with the function stability.selection.heatmap.

(ii) Eventually, the set of "stable selected" variables corresponds to the set of covariates that were selected by most of the training among the grid of hyper-parameters candidate values, based on a threshold probability piThreshold and a restriction of the grid of hyper-parameters based on rhoError (c.f. Durif et al., 2018, for details).

This function allows to visualize probabalities estimated at the first step (i) of the stability selection by the functions spls.stab, logit.spls.stab or multinom.spls.stab.

This function use the function matrix.heatmap.

See Also

logit.spls, stability.selection, stability.selection.heatmap

Examples

if (FALSE) {
### load plsgenomics library
library(plsgenomics)

### generating data
n <- 100
p <- 100
sample1 <- sample.cont(n=n, p=p, kstar=10, lstar=2, 
                       beta.min=0.25, beta.max=0.75, mean.H=0.2, 
                       sigma.H=10, sigma.F=5, sigma.E=5)
                       
X <- sample1$X
Y <- sample1$Y

### hyper-parameters values to test
lambda.l1.range <- seq(0.05,0.95,by=0.1) # between 0 and 1
ncomp.range <- 1:10

### tuning the hyper-parameters
stab1 <- spls.stab(X=X, Y=Y, lambda.l1.range=lambda.l1.range, 
                   ncomp.range=ncomp.range, weight.mat=NULL, 
                   adapt=FALSE, center.X=TRUE, center.Y=TRUE, 
                   scale.X=TRUE, scale.Y=TRUE, weighted.center=FALSE, 
                   ncores=1, nresamp=100)
                       
str(stab1)

### heatmap of estimated probabilities
stability.selection.heatmap(stab1)
}