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Last data update: 2014.03.03

R: cross validation for EM fused-lasso

EMcvfusedlasso

R Documentation

cross validation for EM fused-lasso

Description

cross validation function for EMfusedlasso.

Usage

EMcvfusedlasso(X, y, lambda1, lambda2, nbFolds = 10, maxSteps = 1000,
  burn = 50, intercept = TRUE, model = c("linear", "logistic"),
  eps = 1e-05, eps0 = 1e-08, epsCG = 1e-08)

Arguments

`X`	the matrix (of size n*p) of the covariates.
`y`	a vector of length n with the response.
`lambda1`	Values of lambda1 at which prediction error should be computed. Can be a single value.
`lambda2`	Values of lambda2 at which prediction error should be computed. Can be a single value.
`nbFolds`	the number of folds for the cross-validation.
`maxSteps`	Maximal number of steps for EM algorithm.
`burn`	Number of steps for the burn period.
`intercept`	If TRUE, there is an intercept in the model.
`model`	"linear" or "logistic".
`eps`	Tolerance of the algorithm.
`eps0`	Zero tolerance. Coefficients under this value are set to zero.
`epsCG`	Epsilon for the convergence of the conjugate gradient.

Value

A list containing

cv: Mean prediction error for each value of index.
cvError: Standard error of cv.
minCv: Minimal cv criterion.
lambda1: Values of lambda1 at which prediction error should be computed.
lambda2: Values of lambda2 at which prediction error should be computed.
lambda.optimal: Value of (lambda1,lambda2) for which the cv criterion is minimal.

Author(s)

Quentin Grimonprez, Serge Iovleff

Examples

dataset=simul(50,100,0.4,1,10,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result=EMcvfusedlasso(dataset$data,dataset$response,3:1,3:1,5,intercept=FALSE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDPenReg)
Loading required package: rtkore
Loading required package: Rcpp

Attaching package: 'rtkore'

The following object is masked from 'package:Rcpp':

    LdFlags

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDPenReg/EMcvfusedlasso.Rd_%03d_medium.png", width=480, height=480)
> ### Name: EMcvfusedlasso
> ### Title: cross validation for EM fused-lasso
> ### Aliases: EMcvfusedlasso
> 
> ### ** Examples
> 
> dataset=simul(50,100,0.4,1,10,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
> result=EMcvfusedlasso(dataset$data,dataset$response,3:1,3:1,5,intercept=FALSE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>