From: emilie Date: Tue, 6 Dec 2016 17:47:00 +0000 (+0100) Subject: réécriture de selectionTotale.m X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/css/img/doc/pieces/cb.svg?a=commitdiff_plain;h=e01c9b1fc45d307b00a9a8a6f6395850107a1d60;p=valse.git réécriture de selectionTotale.m --- diff --git a/R/.Rhistory b/R/.Rhistory new file mode 100644 index 0000000..29f5836 --- /dev/null +++ b/R/.Rhistory @@ -0,0 +1,94 @@ +install.packages("shock") +library("shock", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") +source('~/Dropbox/GLLiM-shock/code prediction intervals/IC inverse intercept.R') +mean(res[1,]) +source('~/Dropbox/GLLiM-shock/code prediction intervals/IC inverse intercept.R') +mean(res[1,]) +&e-2 +1e-2 +library("devtools", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") +library("roxygen2", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") +setwd("~/valse") +document() +document() +document() +document() +document() +document() +document() +document() +setwd("~/") +document() +setwd("~/valse") +document() +setwd("~/") +install('valse') +install('valse') +?valse +install("valse") +?kmeans +x = rnorm(50) +kmeans(x) +library("mclust", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") +install.packages("Rmixmod") +A = matrix(0,dim = c(2,3)) +A = matrix(0,dim = c(2,3)) +A = matrix(0,ncol=3,nrow=2 +) +dim(A) +dim(A)[1] +mclust(x) +Mclust(x) +x +A +B = matrix(2,nrow=2,ncol=2) +matrix(c(A,B)) +matrix(c(A,B),nrow=2) +C =matrix(c(A,B),nrow=2) +Mclust(C,k=2) +Mclust(C) +Mclust(A) +A +Mclust(x) +Mclust(x,2) +class = Mclust(x,2) +names(class) +class$classification +generateI0default(10,5,5,2) +setwd("~/valse/R") +generateI0default(10,5,5,2) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIOdefault.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIOdefault.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIOdefault.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +generateIOdefault(10,5,5,2) +source('~/valse/R/generateIOdefault.R', echo=TRUE) +A = generateIOdefault(10,5,5,2) +source('~/valse/R/generateIO.R', echo=TRUE) +A = generateIOdefault(10,5,5,2) +A +A = generateIOdefault(10,5,6,2) +A +Mclust(A$Y) +library("mclust", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") +Mclust(A$Y) +X = A$X +Y = A$Y +X +Y +save(X,Y,file="data.RData") +load("~/valse/R/data.RData") diff --git a/R/selectVariables.R b/R/selectVariables.R new file mode 100644 index 0000000..be53d85 --- /dev/null +++ b/R/selectVariables.R @@ -0,0 +1,82 @@ +#' selectVaribles +#' It is a function which construct, for a given lambda, the sets of +#' relevant variables and irrelevant variables. +#' +#' @param phiInit an initial estimator for phi (size: p*m*k) +#' @param rhoInit an initial estimator for rho (size: m*m*k) +#' @param piInit an initial estimator for pi (size : k) +#' @param gamInit an initial estimator for gamma +#' @param mini minimum number of iterations in EM algorithm +#' @param maxi maximum number of iterations in EM algorithm +#' @param gamma power in the penalty +#' @param glambda grid of regularization parameters +#' @param X matrix of regressors +#' @param Y matrix of responses +#' @param thres threshold to consider a coefficient to be equal to 0 +#' @param tau threshold to say that EM algorithm has converged +#' +#' @return +#' @export +#' +#' @examples +selectVariables <- function(phiInit,rhoInit,piInit,gamInit, + mini,maxi,gamma,glambda,X,Y,thres,tau){ + + dimphi <- dim(phiInit) + p <- dimPhi[1] + m <- dimPhi[2] + k <- dimPhi[3] + L <- length(glambda); + A1 <- array(0, dim <- c(p,m+1,L)) + A2 <- array(0, dim <- c(p,m+1,L)) + Rho <- array(0, dim <- c(m,m,k,L)) + Pi <- array(0, dim <- c(k,L)); + + # For every lambda in gridLambda, comutation of the coefficients + for (lambdaIndex in c(1:L)) { + Res <- EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi, + gamma,glambda[lambdaIndex],X,Y,tau); + phi <- Res$phi + rho <- Res$rho + pi <- Res$pi + + # If a coefficient is larger than the threshold, we keep it + selectedVariables <- array(0, dim = c(p,m)) + discardedVariables <- array(0, dim = c(p,m)) + atLeastOneSelectedVariable <- false + for (j in c(1:p)){ + cpt <- 1 + cpt2 <-1 + for (mm in c(1:m)){ + if (max(abs(phi[j,mm,])) > thres){ + selectedVariables[j,cpt] <- mm + cpt <- cpt+1 + atLeastOneSelectedVariable <- true + } else{ + discardedVariables[j,cpt2] <- mm + cpt2 <- cpt2+1 + } + } + } + + # If no coefficients have been selected, we provide the zero matrix + # We delete zero coefficients: vec = indices of zero values + if atLeastOneSelectedVariable{ + vec <- c() + for (j in c(1:p)){ + if (selectedVariables(j,1) =! 0){ + vec <- c(vec,j) + } + } + # Else, we provide the indices of relevant coefficients + A1[,1,lambdaIndex] <- c(vec,rep(0,p-length(vec))) + A1[1:length(vec),2:(m+1),lambdaIndex] <- selectedVariables[vec,] + A2[,1,lambdaIndex] <- 1:p + A2[,2:(m+1),lambdaIndex] <- discardedVariables + Rho[,,,lambdaIndex] <- rho + Pi[,lambdaIndex] <- pi + } + + } + return(res = list(A1 = A1, A2 = A2 , Rho = Rho, Pi = Pi)) +} \ No newline at end of file