+++ /dev/null
-# Generated by roxygen2: do not edit by hand
-
-export(basicInitParameters)
-export(discardSimilarModels_EMGLLF)
-export(discardSimilarModels_EMGrank)
-export(generateXY)
-export(generateXYdefault)
-export(gridLambda)
-export(initSmallEM)
-export(modelSelection)
-export(selectVariables)
-export(valse)
-importFrom(methods,new)
-importFrom(stats,cutree)
-importFrom(stats,dist)
-importFrom(stats,hclust)
-importFrom(stats,runif)
-useDynLib(valse)
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/generateSampleInputs.R
-\name{basicInitParameters}
-\alias{basicInitParameters}
-\title{Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
-identity for covariance matrices, and uniformly distributed for the clustering)}
-\usage{
-basicInitParameters(n, p, m, k)
-}
-\arguments{
-\item{n}{sample size}
-
-\item{p}{number of covariates}
-
-\item{m}{size of the response}
-
-\item{k}{number of clusters}
-}
-\value{
-list with phiInit, rhoInit,piInit,gamInit
-}
-\description{
-Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
-identity for covariance matrices, and uniformly distributed for the clustering)
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/discardSimilarModels.R
-\name{discardSimilarModels_EMGLLF}
-\alias{discardSimilarModels_EMGLLF}
-\title{Discard models which have the same relevant variables - for EMGLLF}
-\usage{
-discardSimilarModels_EMGLLF(B1, B2, glambda, rho, pi)
-}
-\arguments{
-\item{B1}{array of relevant coefficients (of size p*m*length(gridlambda))}
-
-\item{B2}{array of irrelevant coefficients (of size p*m*length(gridlambda))}
-
-\item{glambda}{grid of regularization parameters (vector)}
-
-\item{rho}{covariance matrix (of size m*m*K*size(gridLambda))}
-
-\item{pi}{weight parameters (of size K*size(gridLambda))}
-}
-\value{
-a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
-of selected models.
-}
-\description{
-Discard models which have the same relevant variables - for EMGLLF
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/discardSimilarModels.R
-\name{discardSimilarModels_EMGrank}
-\alias{discardSimilarModels_EMGrank}
-\title{Discard models which have the same relevant variables
- - for Lasso-rank procedure (focus on columns)}
-\usage{
-discardSimilarModels_EMGrank(B1, rho, pi)
-}
-\arguments{
-\item{B1}{array of relevant coefficients (of size p*m*length(gridlambda))}
-
-\item{rho}{covariance matrix}
-
-\item{pi}{weight parameters}
-}
-\value{
-a list with B1, in, rho, pi
-}
-\description{
-Discard models which have the same relevant variables
- - for Lasso-rank procedure (focus on columns)
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/generateSampleInputs.R
-\name{generateXY}
-\alias{generateXY}
-\title{Generate a sample of (X,Y) of size n}
-\usage{
-generateXY(meanX, covX, covY, pi, beta, n)
-}
-\arguments{
-\item{meanX}{matrix of group means for covariates (of size p)}
-
-\item{covX}{covariance for covariates (of size p*p)}
-
-\item{covY}{covariance for the response vector (of size m*m*K)}
-
-\item{pi}{proportion for each cluster}
-
-\item{beta}{regression matrix, of size p*m*k}
-
-\item{n}{sample size}
-}
-\value{
-list with X and Y
-}
-\description{
-Generate a sample of (X,Y) of size n
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/generateSampleInputs.R
-\name{generateXYdefault}
-\alias{generateXYdefault}
-\title{Generate a sample of (X,Y) of size n with default values}
-\usage{
-generateXYdefault(n, p, m, k)
-}
-\arguments{
-\item{n}{sample size}
-
-\item{p}{number of covariates}
-
-\item{m}{size of the response}
-
-\item{k}{number of clusters}
-}
-\value{
-list with X and Y
-}
-\description{
-Generate a sample of (X,Y) of size n with default values
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gridLambda.R
-\name{gridLambda}
-\alias{gridLambda}
-\title{Construct the data-driven grid for the regularization parameters used for the Lasso estimator}
-\usage{
-gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
-}
-\arguments{
-\item{phiInit}{value for phi}
-
-\item{rhoInit}{value for rho}
-
-\item{piInit}{value for pi}
-
-\item{gamInit}{value for gamma}
-
-\item{X}{matrix of covariates (of size n*p)}
-
-\item{Y}{matrix of responses (of size n*m)}
-
-\item{gamma}{power of weights in the penalty}
-
-\item{mini}{minimum number of iterations in EM algorithm}
-
-\item{maxi}{maximum number of iterations in EM algorithm}
-
-\item{tau}{threshold to stop EM algorithm}
-}
-\value{
-the grid of regularization parameters
-}
-\description{
-Construct the data-driven grid for the regularization parameters used for the Lasso estimator
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/initSmallEM.R
-\name{initSmallEM}
-\alias{initSmallEM}
-\title{initialization of the EM algorithm}
-\usage{
-initSmallEM(k, X, Y)
-}
-\arguments{
-\item{k}{number of components}
-
-\item{X}{matrix of covariates (of size n*p)}
-
-\item{Y}{matrix of responses (of size n*m)}
-}
-\value{
-a list with phiInit, rhoInit, piInit, gamInit
-}
-\description{
-initialization of the EM algorithm
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/modelSelection.R
-\name{modelSelection}
-\alias{modelSelection}
-\title{Among a collection of models, this function constructs a subcollection of models with
-models having strictly different dimensions, keeping the model which minimizes
-the likelihood if there were several with the same dimension}
-\usage{
-modelSelection(LLF)
-}
-\arguments{
-\item{LLF}{a matrix, the first column corresponds to likelihoods for several models
-the second column corresponds to the dimensions of the corresponding models.}
-}
-\value{
-a list with indices, a vector of indices selected models,
- and D1, a vector of corresponding dimensions
-}
-\description{
-Among a collection of models, this function constructs a subcollection of models with
-models having strictly different dimensions, keeping the model which minimizes
-the likelihood if there were several with the same dimension
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/selectVariables.R
-\name{selectVariables}
-\alias{selectVariables}
-\title{selectVariables}
-\usage{
-selectVariables(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, glambda,
- X, Y, thresh, tau, ncores = 1)
-}
-\arguments{
-\item{phiInit}{an initial estimator for phi (size: p*m*k)}
-
-\item{rhoInit}{an initial estimator for rho (size: m*m*k)}
-
-\item{piInit}{an initial estimator for pi (size : k)}
-
-\item{gamInit}{an initial estimator for gamma}
-
-\item{mini}{minimum number of iterations in EM algorithm}
-
-\item{maxi}{maximum number of iterations in EM algorithm}
-
-\item{gamma}{power in the penalty}
-
-\item{glambda}{grid of regularization parameters}
-
-\item{X}{matrix of regressors}
-
-\item{Y}{matrix of responses}
-
-\item{tau}{threshold to say that EM algorithm has converged}
-
-\item{thres}{threshold to consider a coefficient to be equal to 0}
-}
-\value{
-a list of outputs, for each lambda in grid: selected,Rho,Pi
-}
-\description{
-It is a function which construct, for a given lambda, the sets of relevant variables.
-}
-\examples{
-TODO
-
-}
-
+++ /dev/null
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/valse.R
-\name{valse}
-\alias{valse}
-\title{Main function}
-\usage{
-valse(X, Y, procedure = "LassoMLE", selecMod = "BIC", gamma = 1,
- mini = 10, maxi = 100, eps = 1e-04, kmin = 2, kmax = 5,
- rang.min = 1, rang.max = 10)
-}
-\arguments{
-\item{X}{matrix of covariates (of size n*p)}
-
-\item{Y}{matrix of responses (of size n*m)}
-
-\item{procedure}{among 'LassoMLE' or 'LassoRank'}
-
-\item{selecMod}{method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'}
-
-\item{gamma}{integer for the power in the penaly, by default = 1}
-
-\item{mini}{integer, minimum number of iterations in the EM algorithm, by default = 10}
-
-\item{maxi}{integer, maximum number of iterations in the EM algorithm, by default = 100}
-
-\item{eps}{real, threshold to say the EM algorithm converges, by default = 1e-4}
-
-\item{kmin}{integer, minimum number of clusters, by default = 2}
-
-\item{kmax}{integer, maximum number of clusters, by default = 10}
-
-\item{rang.min}{integer, minimum rank in the low rank procedure, by default = 1}
-
-\item{rang.max}{integer, maximum rank in the}
-}
-\value{
-a list with estimators of parameters
-}
-\description{
-Main function
-}
-
projects / valse.git / commitdiff
