From: Benjamin Auder Date: Sat, 18 Mar 2017 02:35:28 +0000 (+0100) Subject: Delete NAMESPACE + all Rd files but valse-package.Rd: they are generated by roxygen2 X-Git-Url: https://git.auder.net/?p=valse.git;a=commitdiff_plain;h=6f32817b8258e4ff1bb0bf82fc7dc3d2056df3e3 Delete NAMESPACE + all Rd files but valse-package.Rd: they are generated by roxygen2 --- diff --git a/pkg/NAMESPACE b/pkg/NAMESPACE deleted file mode 100644 index a4e6bf0..0000000 --- a/pkg/NAMESPACE +++ /dev/null @@ -1,18 +0,0 @@ -# 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) diff --git a/pkg/man/basicInitParameters.Rd b/pkg/man/basicInitParameters.Rd deleted file mode 100644 index 6ed8547..0000000 --- a/pkg/man/basicInitParameters.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% 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) -} - diff --git a/pkg/man/discardSimilarModels_EMGLLF.Rd b/pkg/man/discardSimilarModels_EMGLLF.Rd deleted file mode 100644 index 20ac957..0000000 --- a/pkg/man/discardSimilarModels_EMGLLF.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% 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 -} - diff --git a/pkg/man/discardSimilarModels_EMGrank.Rd b/pkg/man/discardSimilarModels_EMGrank.Rd deleted file mode 100644 index 932c4ff..0000000 --- a/pkg/man/discardSimilarModels_EMGrank.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% 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) -} - diff --git a/pkg/man/generateXY.Rd b/pkg/man/generateXY.Rd deleted file mode 100644 index 6645c59..0000000 --- a/pkg/man/generateXY.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% 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 -} - diff --git a/pkg/man/generateXYdefault.Rd b/pkg/man/generateXYdefault.Rd deleted file mode 100644 index 3f80c08..0000000 --- a/pkg/man/generateXYdefault.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% 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 -} - diff --git a/pkg/man/gridLambda.Rd b/pkg/man/gridLambda.Rd deleted file mode 100644 index cc203a7..0000000 --- a/pkg/man/gridLambda.Rd +++ /dev/null @@ -1,36 +0,0 @@ -% 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 -} - diff --git a/pkg/man/initSmallEM.Rd b/pkg/man/initSmallEM.Rd deleted file mode 100644 index 86be0cb..0000000 --- a/pkg/man/initSmallEM.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% 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 -} - diff --git a/pkg/man/modelSelection.Rd b/pkg/man/modelSelection.Rd deleted file mode 100644 index 30aeaa5..0000000 --- a/pkg/man/modelSelection.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% 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 -} - diff --git a/pkg/man/selectVariables.Rd b/pkg/man/selectVariables.Rd deleted file mode 100644 index af79059..0000000 --- a/pkg/man/selectVariables.Rd +++ /dev/null @@ -1,45 +0,0 @@ -% 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 - -} - diff --git a/pkg/man/valse.Rd b/pkg/man/valse.Rd deleted file mode 100644 index 48271b1..0000000 --- a/pkg/man/valse.Rd +++ /dev/null @@ -1,42 +0,0 @@ -% 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 -} -