export(indicesSelection)
export(initSmallEM)
export(modelSelection)
+export(selectVariables)
+useDynLib(valse)
+#' @useDynLib valse
+
Valse = setRefClass(
Class = "Valse",
#' @param thres threshold to consider a coefficient to be equal to 0
#' @param tau threshold to say that EM algorithm has converged
#'
-#' @return
-#' @export
+#' @return TODO
+#'
+#' @examples TODO
#'
-#' @examples
+#' @export
selectVariables <- function(phiInit,rhoInit,piInit,gamInit,
mini,maxi,gamma,glambda,X,Y,thres,tau)
{
\name{basic_Init_Parameters}
\alias{basic_Init_Parameters}
\title{Initialize the parameters in a basic way (zero for the conditional mean,
- uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)}
+uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)}
\usage{
basic_Init_Parameters(n, p, m, k)
}
}
\description{
Initialize the parameters in a basic way (zero for the conditional mean,
- uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)
+uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)
}
}
\value{
a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
- of selected models.
+of selected models.
}
\description{
Discard models which have the same relevant variables
generateIO(covX, covY, pi, beta, n)
}
\arguments{
-\item{covX}{covariance for covariates}
+\item{covX}{covariance for covariates (of size p*p*K)}
-\item{covY}{covariance for the response vector}
+\item{covY}{covariance for the response vector (of size m*m*K)}
\item{pi}{proportion for each cluster}
% Please edit documentation in R/indicesSelection.R
\name{indicesSelection}
\alias{indicesSelection}
-\title{Construct the set of relevant indices}
+\title{Construct the set of relevant indices -> ED: je crois que cette fonction n'est pas utile}
\usage{
indicesSelection(phi, thresh = 1e-06)
}
}
\value{
a list with A, a matrix with relevant indices (size = p*m) and B, a
- matrix with irrelevant indices (size = p*m)
+ matrix with irrelevant indices (size = p*m)
}
\description{
-Construct the set of relevant indices
+Construct the set of relevant indices -> ED: je crois que cette fonction n'est pas utile
}
}
\value{
a list with indices, a vector of indices selected models,
- and D1, a vector of corresponding dimensions
+ and D1, a vector of corresponding dimensions
}
\description{
Among a collection of models, this function constructs a subcollection of models with
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/selectVariables.R
+\name{selectVariables}
+\alias{selectVariables}
+\title{selectVaribles
+It is a function which construct, for a given lambda, the sets of
+relevant variables and irrelevant variables.}
+\usage{
+selectVariables(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, glambda,
+ X, Y, thres, tau)
+}
+\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{thres}{threshold to consider a coefficient to be equal to 0}
+
+\item{tau}{threshold to say that EM algorithm has converged}
+}
+\value{
+TODO
+}
+\description{
+selectVaribles
+It is a function which construct, for a given lambda, the sets of
+relevant variables and irrelevant variables.
+}
+\examples{
+TODO
+
+}
+
#Prod flags:
#PKG_CFLAGS=-O2 -I./sources
-PKG_LIBS=-lm
+PKG_LIBS=-lm -lgsl -lcblas
SOURCES = $(wildcard adapters/*.c sources/*.c)