plsgenomics (version 1.5-3)

logit.spls.stab: Stability selection procedure to estimate probabilities of selection of covariates for the LOGIT-SPLS method

Description

The function logit.spls.stab train a logit-spls model for each candidate values (ncomp, lambda.l1, lambda.ridge) of hyper-parameters on multiple sub-samplings in the data. The stability selection procedure selects the covariates that are selected by most of the models among the grid of hyper-parameters, following the procedure described in Durif et al. (2018). Candidates values for ncomp, lambda.l1 and lambda.l2 are respectively given by the input arguments ncomp.range, lambda.l1.range and lambda.l2.range.

Usage

logit.spls.stab(
  X,
  Y,
  lambda.ridge.range,
  lambda.l1.range,
  ncomp.range,
  adapt = TRUE,
  maxIter = 100,
  svd.decompose = TRUE,
  ncores = 1,
  nresamp = 100,
  center.X = TRUE,
  scale.X = FALSE,
  weighted.center = TRUE,
  seed = NULL,
  verbose = TRUE
)

Value

An object with the following attributes

q.Lambda

A table with values of q.Lambda (c.f. Durif et al. (2018) for the notation), being the averaged number of covariates selected among the entire grid of hyper-parameters candidates values, for increasing size of hyper-parameter grid.

probs.lambda

A table with estimated probability of selection for each covariates depending on the candidates values for hyper-parameters.

p

An integer values indicating the number of covariates in the model.

Arguments

X

a (n x p) data matrix of predictors. X must be a matrix. Each row corresponds to an observation and each column to a predictor variable.

Y

a (n) vector of (continuous) responses. Y must be a vector or a one column matrix. It contains the response variable for each observation. Y should take values in {0,1}.

lambda.ridge.range

a vector of positive real values. lambda.ridge is the Ridge regularization parameter for the RIRLS algorithm (see details), the optimal value will be chosen among lambda.ridge.range.

lambda.l1.range

a vecor of positive real values, in [0,1]. lambda.l1 is the sparse penalty parameter for the dimension reduction step by sparse PLS (see details), the optimal value will be chosen among lambda.l1.range.

ncomp.range

a vector of positive integers. ncomp is the number of PLS components. The optimal value will be chosen among ncomp.range.

adapt

a boolean value, indicating whether the sparse PLS selection step sould be adaptive or not (see details).

maxIter

a positive integer, the maximal number of iterations in the RIRLS algorithm (see details).

svd.decompose

a boolean parameter. svd.decompose indicates wether or not the predictor matrix Xtrain should be decomposed by SVD (singular values decomposition) for the RIRLS step (see details).

ncores

a positve integer, indicating the number of cores that the cross-validation is allowed to use for parallel computation (see details).

nresamp

number of resamplings of the data to estimate the probility of selection for each covariate, default is 100.

center.X

a boolean value indicating whether the data matrices Xtrain and Xtest (if provided) should be centered or not.

scale.X

a boolean value indicating whether the data matrices Xtrain and Xtest (if provided) should be scaled or not (scale.X=TRUE implies center.X=TRUE) in the spls step.

weighted.center

a boolean value indicating whether the centering should take into account the weighted l2 metric or not in the SPLS step.

seed

a positive integer value (default is NULL). If non NULL, the seed for pseudo-random number generation is set accordingly.

verbose

a boolean parameter indicating the verbosity.

Author

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

Details

The columns of the data matrices X may not be standardized, since standardizing is performed by the function logit.spls.stab as a preliminary step.

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 (ncomp, lambda.l1, lambda.ridge) of hyper-parameters, a logit-SPLS is trained on nresamp resamplings of the data. Then, for each triplet (ncomp, lambda.l1, lambda.ridge), the probability that a covariate (i.e. a column in X) is selected is computed among the resamplings.

(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.

This function achieves the first step (i) of the stability selection procedure. The second step (ii) is achieved by the function stability.selection.

This procedures uses mclapply from the parallel package, available on GNU/Linux and MacOS. Users of Microsoft Windows can refer to the README file in the source to be able to use a mclapply type function.

References

Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S., Picard, F., 2018. High dimensional classification with combined adaptive sparse PLS and logistic regression. Bioinformatics 34, 485--493. tools:::Rd_expr_doi("10.1093/bioinformatics/btx571"). Available at http://arxiv.org/abs/1502.05933.

Meinshausen, N., Buhlmann P. (2010). Stability Selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 4, 417-473.

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.bin(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)

X <- sample1$X
Y <- sample1$Y

### pertinent covariates id
sample1$sel

### hyper-parameters values to test
lambda.l1.range <- seq(0.05,0.95,by=0.1) # between 0 and 1
ncomp.range <- 1:10
# log-linear range between 0.01 a,d 1000 for lambda.ridge.range
logspace <- function( d1, d2, n) exp(log(10)*seq(d1, d2, length.out=n))
lambda.ridge.range <- signif(logspace(d1 <- -2, d2 <- 3, n=21), digits=3)

### tuning the hyper-parameters
stab1 <- logit.spls.stab(X=X, Y=Y, lambda.ridge.range=lambda.ridge.range, 
                         lambda.l1.range=lambda.l1.range, 
                         ncomp.range=ncomp.range, 
                         adapt=TRUE, maxIter=100, svd.decompose=TRUE, 
                         ncores=1, nresamp=100)
                       
str(stab1)

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

### selected covariates
stability.selection(stab1, piThreshold=0.6, rhoError=10)
}