Function that calculates cross-validation selection criteria
infoCriterion(ynew, pred, family, type, size = NULL, npar = 0)data matrix corresponding to the observations used as test sample.
predicted value of the linear predictor obtained from Xnew and the estimated parameters.
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.
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.
describes the number of trials for the binomial dependent variables. A (number of statistical units * number of binomial dependent variables) matrix is expected.
number of parameters used for penalisation.
a matrix containing the criterion value for each dependent variable (row) and each number of components (column).