# R/cvlars.R In MPAlars: lars algorithm

#' cross validation function for lars algorithm
#'
#' @title cross validation
#' @author Quentin Grimonprez
#' @param X the matrix (of size n*p) of the covariates.
#' @param y a vector of length n with the response.
#' @param nbFolds the number of folds for the cross-validation.
#' @param index Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).
#' @param maxSteps Maximal number of steps for lars algorithm.
#' @param eps Tolerance of the algorithm.
#' @return A list containing
#' \describe{
#'   \item{cv}{Mean prediction error for each value of index.}
#'   \item{cvError}{Standard error of cv.}
#'   \item{minCv}{Minimal cv criterion.}
#'   \item{fraction}{Value of lambda for which the cv criterion is minimal.}
#'   \item{index}{Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).}
#'   \item{maxSteps}{Maximum number of steps of the lars algorithm.}
#' }
#' @examples
#' dataset=MPA.simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
#' result=MPA.cvlars(dataset$data,dataset$response,5)
#' @export
#'
MPA.cvlars <- function(X,y,nbFolds=10,index=seq(0,1,by=0.01),maxSteps=3*min(dim(X)),eps=.Machine$double.eps^0.5) { #check arguments if(missing(X)) stop("X is missing.") if(missing(y)) stop("y is missing.") index=unique(index) .checkcvlars(X,y,maxSteps,eps,nbFolds,index) # call lars algorithm val=.Call( "cvlars",X,y,nrow(X),ncol(X),maxSteps,eps,nbFolds,index,PACKAGE = "MPAlars" ) #create the output object cv=list(cv=val$cv,cvError=val$cvError,minCv=min(val$cv),fraction=index[which.min(val$cv)],index=index,maxSteps=maxSteps) class(cv)="cvlars" #plot.cv(cv) return(cv) } # check arguments from cvlars .checkcvlars=function(X,y,maxSteps,eps,nbFolds,index) { ## X: matrix of real if(!is.numeric(X) || !is.matrix(X)) stop("X must be a matrix of real") ## y: vector of real if(!is.numeric(y) || !is.vector(y)) stop("y must be a vector of real") if(length(y)!=nrow(X)) stop("The number of rows of X doesn't match with the length of y") ## maxSteps if(!.is.wholenumber(maxSteps)) stop("maxSteps must be a positive integer") if(maxSteps<=0) stop("maxSteps must be a positive integer") ## nbFolds if(!.is.wholenumber(nbFolds)) stop("nbFolds must be a positive integer") if(nbFolds<=0 || nbFolds>length(y)) stop("nbFolds must be a positive integer") ## eps if(!is.double(eps)) stop("eps must be a positive real") if(eps<=0) stop("eps must be a positive real") ## index if(!is.numeric(index) || !is.vector(index)) stop("index must be a vector of real between 0 and 1") if(max(index)>1 || min(index)<0) stop("index must be a vector of real between 0 and 1") } #' plot cross validation mean square error #' #' @title plot cross validation mean square error #' @author Quentin Grimonprez #' @param x Output from MPA.cvlars function. #' @examples #' dataset=MPA.simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2)) #' result=MPA.cvlars(dataset$data,dataset$response,5) #' plot.cv(result) #' @export #' plot.cv=function(x) { if(missing(x)) stop("x is missing.") if(class(x)!="cvlars") stop("x must be an output of the MPA.cvlars function.") plot(x$index, x$cv, type = "b", ylim = range(x$cv, x$cv + x$cvError, x$cv - x$cvError),xlab="Fraction L1 Norm",ylab="Cross-Validated MSE")
lines(x$index, x$cv+x$cvError,lty=2) lines(x$index, x$cv-x$cvError,lty=2)
}


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MPAlars documentation built on May 2, 2019, 5:47 p.m.