#' @return a list with estimators of parameters
#' @export
#-----------------------------------------------------------------------
-valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
- maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'BIC',gamma = 1,mini = 10,
+ maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,
rang.min = 1,rang.max = 10) {
##################################
#core workflow: compute all models
p = dim(phiInit)[1]
m = dim(phiInit)[2]
+ n = dim(X)[1]
+ tableauRecap = array(, dim=c(1000,4))
+ cpt = 0
print("main loop: over all k and all lambda")
- for (k in kmin:kmax)
- {
+
+for (k in kmin:kmax){
print(k)
-
print("Parameters initialization")
#smallEM initializes parameters by k-means and regression model in each component,
#doing this 20 times, and keeping the values maximizing the likelihood after 10
rhoInit <<- init$rhoInit
piInit <<- init$piInit
gamInit <<- init$gamInit
-
+ source('~/valse/pkg/R/gridLambda.R')
gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
print("Compute relevant parameters")
#select variables according to each regularization parameter
#from the grid: A1 corresponding to selected variables, and
#A2 corresponding to unselected variables.
- params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda,X,Y,1e-8,eps)
+
+ params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+ params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+ ## etrange : params et params 2 sont différents ...
+
selected <<- params$selected
Rho <<- params$Rho
Pi <<- params$Pi
print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
- model = constructionModelesLassoMLE(
- phiInit, rhoInit,piInit,tauInit,mini,maxi,
- gamma,gridLambda,X,Y,thresh,eps,selected)
- ################################################
- ### Regarder la SUITE
- r1 = runProcedure1()
- Phi2 = Phi
- Rho2 = Rho
- Pi2 = Pi
-
- if (is.null(dim(Phi2)))
- #test was: size(Phi2) == 0
- {
- Phi[, , 1:k] <<- r1$phi
- Rho[, , 1:k] <<- r1$rho
- Pi[1:k,] <<- r1$pi
- } else
- {
- Phi <<-
- array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
- Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
- Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
- Rho <<-
- array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
- Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
- Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
- Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
- Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
- Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
- }
+ model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
+ LLH = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
+ D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]
} else {
print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
}
}
+ tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
+ cpt = cpt+length(model[[k]])
}
print('Model selection')
+
+ tableauRecap = array(dim = c())
if (selecMod == 'SlopeHeuristic') {
} else if (selecMod == 'BIC') {
-
+ BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
+ indMinBIC = which.min(BIC)
+ return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])
} else if (selecMod == 'AIC') {
}