EMcvfusedlasso: cross validation for EM fused-lasso
In HDPenReg: High-Dimensional Penalized Regression

Description Usage Arguments Value Author(s) Examples

cross validation function for EMfusedlasso.

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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)

`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.

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.

Quentin Grimonprez, Serge Iovleff

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dataset <- simul(50, 100, 0.4, 1, 10, matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result <- EMcvfusedlasso(X = dataset$data, y = dataset$response, lambda1 = 3:1, 
                         lambda2 = 3:1, nbFolds = 5,intercept = FALSE)