refit.mht: Refit a 'mht' object

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

Description

Refit a mht object for a new observation Ynew

Usage

## S3 method for class 'mht'
refit(object,Ynew,var_nonselect,sigma,maxordre,ordre,m,show,IT,...)

Arguments

object	Object of class "mht".
Ynew	Response variable of length n.
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.
sigma	Value of the variance if it is known; 0 otherwise. Default is 0.
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".
m	Number of bootstrapped iteration of the Lasso. Only use if the algorithm is set to "bolasso". Default is m=100.
show	Vector of logical values, show=(showordre,showtest,showresult). Default is (1,0,1). If showordre=TRUE, shows the variables being ordered at each step of the algorithm. If showtest=TRUE, the number of regularization parameters tested is printed to show the advancement of the dyadic algorithm. Only used if the algorithm is set to "bolasso". if showresult=TRUE, shows the value of the statistics and the estimated quantile at each step of the procedure.
IT	Number of simulations for the calculation of the quantile. Default is 1000.
...	not used.

Details

maxq is not a parameter of refit.mht as the same number of alternative is neccessary for a refit of the model.
For more details, see mht.

Value

A 'mht' object is returned.

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.
coefficients	Matrix of the estimated coefficients. Each row concerns a specific user level alpha.
residuals	Matrix of the residuals. Each row concerns a specific user level alpha.
relevant_var	Set of the relevant variables for each alpha.
fitted.values	Matrix of the fitted values, each column concerns a specific user level alpha.
ordre	Order obtained on the maxordre variable.
ordrebeta	The full order on all the p variables.
kchap	Vector containing the length of the estimated set of relevant variables, for the matrix containing the intercept, for each values of alpha.
quantile	The estimated quantiles used in the second step of the procedure.
call	The call that has been used.
call.old	The call that produced the initial 'object'.

Examples

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

# mht
mod=mht(x,y,alpha=c(0.1,0.05),maxordre=15)

# refit mht on a new vector of observation
mod2=refit(mod,ynew,maxordre=15)

## End(Not run)

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