From: emilie <emilie@devijver.org>
Date: Wed, 22 Feb 2017 12:18:16 +0000 (+0100)
Subject: un peu de nettoyage, mais rien de fou
X-Git-Url: https://git.auder.net/variants/current/doc/scripts/%7B%7B%20asset%28%27mixstore/%7B%7B%20targetUrl%20%7D%7D?a=commitdiff_plain;h=22d21a222df140221657af24d71fe05af54a6adc;p=valse.git

un peu de nettoyage, mais rien de fou
---

diff --git a/DESCRIPTION b/DESCRIPTION
index cdda4e4..f8f5a29 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,13 @@ Package: valse
 Title: VAriabLe SElection with mixture of models
 Date: 2016-12-01
 Version: 0.1-0
-Description: TODO
+Description: Two methods are implemented to cluster data with finite mixture regression models.
+              Those procedures deal with high-dimensional covariates and responses through a variable selection
+              procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure.
+              A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected
+              using a model selection criterion (slope heuristic, BIC or AIC).
+              Details of the procedure are provided in 'Model-based clustering for high-dimensional data. Application to functional data'
+              by Emilie Devijver, published in Advances in Data Analysis and Clustering (2016)
 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
     Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
     Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
diff --git a/R/main.R b/R/main.R
index 4c4e87c..42852d3 100644
--- a/R/main.R
+++ b/R/main.R
@@ -27,9 +27,10 @@ Valse = setRefClass(
 		kmin = "integer",
 		# maximum number of components in the mixture
 		kmax = "integer",
-		rangmin = "integer",
-		rangmax = "integer",
-		
+		# ranks for the Lasso-Rank procedure
+		rank.min = "integer",
+		rank.max = "integer",
+
 		# Computed through the workflow
 
 		# initialisation for the reparametrized conditional mean parameter
@@ -54,7 +55,7 @@ Valse = setRefClass(
 		Pi = "numeric",
 
 		#immutable (TODO:?)
-		seuil = "numeric"
+		thresh = "numeric"
 	),
 
 	methods = list(
@@ -75,9 +76,9 @@ Valse = setRefClass(
 			eps <<- ifelse (hasArg("eps"), eps, 1e-6)
 			kmin <<- ifelse (hasArg("kmin"), kmin, as.integer(2))
 			kmax <<- ifelse (hasArg("kmax"), kmax, as.integer(3))
-			rangmin <<- ifelse (hasArg("rangmin"), rangmin, as.integer(2))
-			rangmax <<- ifelse (hasArg("rangmax"), rangmax, as.integer(3))
-			seuil <<- 1e-15 #immutable (TODO:?)
+			rank.min <<- ifelse (hasArg("rank.min"), rank.min, as.integer(2))
+			rank.max <<- ifelse (hasArg("rank.max"), rank.max, as.integer(3))
+			thresh <<- 1e-15 #immutable (TODO:?)
 		},
 
 		##################################
@@ -114,7 +115,7 @@ Valse = setRefClass(
 			#from the grid: A1 corresponding to selected variables, and
 			#A2 corresponding to unselected variables.
 			params = selectiontotale(
-				phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,seuil,eps)
+				phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps)
 			A1 <<- params$A1
 			A2 <<- params$A2
 			Rho <<- params$Rho
@@ -128,7 +129,7 @@ Valse = setRefClass(
 			#compute parameter estimations, with the Maximum Likelihood
 			#Estimator, restricted on selected variables.
 			return ( constructionModelesLassoMLE(
-				phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,seuil,eps,A1,A2) )
+				phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) )
 		},
 
 		runProcedure2 = function()
@@ -138,14 +139,14 @@ Valse = setRefClass(
 			#compute parameter estimations, with the Low Rank
 			#Estimator, restricted on selected variables.
 			return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps,
-				A1,rangmin,rangmax) )
+				A1,rank.min,rank.max) )
 		},
 
 		run = function()
 		{
 			"main loop: over all k and all lambda"
 
-			# Run the all procedure, 1 with the
+			# Run the whole procedure, 1 with the
 			#maximum likelihood refitting, and 2 with the Low Rank refitting.
 			p = dim(phiInit)[1]
 			m = dim(phiInit)[2]
@@ -205,6 +206,7 @@ Valse = setRefClass(
 		# 			#TODO
 		# 			#model = odel(...)
 		# 		end
+		# Give at least the slope heuristic and BIC, and AIC ?
 
 		)
 )