order.variables: Rank the variables

In mht: Multiple Hypothesis Testing for Variable Selection in High-Dimensional Linear Models

Description

Gives an order to the variables and rearrange the input matrix following that order.

Usage

order.variables(data,Y,maxordre,ordre=c("bolasso","pval","pval_hd","FR"),
var_nonselect,m,showordre)

Arguments

data	Input matrix of dimension n * p; each of the n rows is an observation vector of p variables. The intercept should be included in the first column as (1,...,1). If not, it is added.
Y	Response variable of length n.
maxordre	Number of variables to be ordered. Default is min(n/2-1,p/2-1).
ordre	Several possible algorithms to order the variables, ordre=c("bolasso","pval","pval_hd","FR"). "bolasso" uses the dyadic algorithm with the Bolasso technique dyadiqueordre, "pval" uses the p-values obtained with a regression on the full set of variables (only when p<n), "pval_hd" uses marginal regression, "FR" uses Forward Regression. Default is "bolasso".
var_nonselect	Number of variables that don't undergo feature selection. They have to be in the first columns of data. Default is 1, the selection is not performed on the intercept.
m	Number of bootstrapped iteration of the Lasso. Only use if the algorithm is set to "bolasso". Default is m=100.
showordre	If showordre=TRUE, show the variables being ordered at each step of the algorithm.

Details

Rank the variables of data taking into account the vector of observations Y and rearrange the input matrix following that order.

Value

data	A list containing: X - The scaled matrix used in the algorithm, the first column being (1,...,1). Y - the input response vector means.X - Vector of means of the input data matrix. sigma.X - Vector of variances of the input data matrix.
data_ord	Input data matrix rearranged by ORDREBETA
ORDRE	Gives the maxordre most important variables of the data matrix.
ORDREBETA	Gives the order on all the variables of the data matrix (either arbitrary completion of ORDRE -‘Bolasso’ and ‘FR’, or the true order -‘pval’ and ‘pval_hd’).

References

Multiple hypotheses testing for variable selection; F. Rohart 2011

Examples

## Not run: 
x=matrix(rnorm(100*20),100,20)
beta=c(rep(2,5),rep(0,15))
y=x%*%beta+rnorm(100)

res.bolasso=order.variables(x,y,maxordre=15,ordre="bolasso")
res.pval=order.variables(x,y,ordre="pval")
res.FR=order.variables(x,y,maxordre=15,ordre="FR")
res.pval.hd=order.variables(x,y,maxordre=15,ordre="pval_hd")


## End(Not run)

mht documentation built on May 2, 2019, 11:49 a.m.