Function that calculates cross-validation selection criteria

Function that calculates cross-validation selection criteria

Usage

infoCriterion(ynew, pred, family, type, size = NULL, npar = 0)

Arguments

ynew

data matrix corresponding to the observations used as test sample.

pred

predicted value of the linear predictor obtained from Xnew and the estimated parameters.

family

a vector of the same length as the number of responses containing characters identifying the distribution families of the dependent variables. "bernoulli", "binomial", "poisson" or "gaussian" are allowed.

type

information criterion used. Likelihood, aic, bic, aicc or Mean Square Prediction Error (mspe) are defined. Area Under ROC Curve (auc) also defined for Bernoulli cases only.

size

describes the number of trials for the binomial dependent variables. A (number of statistical units * number of binomial dependent variables) matrix is expected.

npar

number of parameters used for penalisation.

Value

a matrix containing the criterion value for each dependent variable (row) and each number of components (column).