+#' @useDynLib valse
+
Valse = setRefClass(
Class = "Valse",
# regression data (size n*p, where n is the number of observations,
# and p is the number of regressors)
- X = "numeric",
+ X = "matrix",
# response data (size n*m, where n is the number of observations,
# and m is the number of responses)
- Y = "numeric",
+ Y = "matrix",
# Optionally user defined (some default values)
# power in the penalty
- gamma = "double",
+ gamma = "numeric",
# minimum number of iterations for EM algorithm
mini = "integer",
# maximum number of iterations for EM algorithm
maxi = "integer",
# threshold for stopping EM algorithm
- eps = "double",
+ eps = "numeric",
# minimum number of components in the mixture
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
- phiInit,
+ phiInit = "numeric",
# initialisation for the reparametrized variance parameter
- rhoInit,
+ rhoInit = "numeric",
# initialisation for the proportions
- piInit,
+ piInit = "numeric",
# initialisation for the allocations probabilities in each component
- tauInit,
+ tauInit = "numeric",
# values for the regularization parameter grid
- gridLambda = c(),
+ gridLambda = "numeric",
# je ne crois pas vraiment qu'il faille les mettre en sortie, d'autant plus qu'on construit
# une matrice A1 et A2 pour chaque k, et elles sont grandes, donc ca coute un peu cher ...
- A1,
- A2,
+ A1 = "integer",
+ A2 = "integer",
# collection of estimations for the reparametrized conditional mean parameters
- Phi,
+ Phi = "numeric",
# collection of estimations for the reparametrized variance parameters
- Rho,
+ Rho = "numeric",
# collection of estimations for the proportions parameters
- Pi,
+ Pi = "numeric",
- #immutable
- seuil = 1e-15
+ #immutable (TODO:?)
+ thresh = "numeric"
),
methods = list(
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))
+ 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:?)
},
##################################
#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$Rho
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
return ( constructionModelesLassoMLE(
- phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,seuil,eps,A1,A2) )
+ phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) )
},
runProcedure2 = function()
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps,
- A1,rangmin,rangmax) )
+ A1,rank.min,rank.max) )
},
run = function()
{
"main loop: over all k and all lambda"
- # Run the all procedure, 1 with the
+ # Run the whole procedure, 1 with the
#maximum likelihood refitting, and 2 with the Low Rank refitting.
p = dim(phiInit)[1]
m = dim(phiInit)[2]
Pi2 = Pi
p = ncol(X)
m = ncol(Y)
- if size(Phi2) == 0
+ if (is.null(dim(Phi2))) #test was: size(Phi2) == 0
{
- Phi[,,1:k] = r1$phi
- Rho[,,1:k] = r1$rho
- Pi[1:k,] = r1$pi
+ 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$pi
+ Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(r1$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
}
} else
{
Phi2 = Phi
if (dim(Phi2)[1] == 0)
{
- Phi(:,:,1:k,:) = phi
+ Phi[,,1:k,] <<- phi
} else
{
- size(Phi2)
- Phi = zeros(p,m,kmax,size(Phi2,4)+size(phi,4))
- size(Phi)
- Phi(:,:,1:size(Phi2,3),1:size(Phi2,4)) = Phi2
- Phi(:,:,1:k,size(Phi2,4)+1:end) = phi
+ Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(phi)[4]))
+ Phi[,,1:(dim(Phi2)[3]),1:(dim(Phi2)[4])] <<- Phi2
+ Phi[,,1:k,-(1:(dim(Phi2)[4]))] <<- phi
}
}
}
# #TODO
# #model = odel(...)
# end
+ # Give at least the slope heuristic and BIC, and AIC ?
)
)
projects / valse.git / blobdiff