un peu de nettoyage, mais rien de fou

diff --git a/DESCRIPTION b/DESCRIPTION
index cdda4e4..f8f5a29 100644 (file)
--- 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
 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]
 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 (file)
--- 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",
                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
                # Computed through the workflow
 
                # initialisation for the reparametrized conditional mean parameter


@@ -54,7 +55,7 @@ Valse = setRefClass(
                Pi = "numeric",
 
                #immutable (TODO:?)
                Pi = "numeric",
 
                #immutable (TODO:?)

-               seuil = "numeric"
+               thresh = "numeric"

        ),
 
        methods = list(
        ),
 
        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))
                        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(
                        #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$Rho
                        A1 <<- params$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(
                        #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()
                },
 
                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,
                        #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 = 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]
                        #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
                #                       #TODO
                #                       #model = odel(...)
                #               end

+               # Give at least the slope heuristic and BIC, and AIC ?

 
                )
 )
 
                )
 )