selection of the regularization parameters (lambda1 and lambda2) of the mimi function by cross-validation
cv.mimi(y, model = c("low-rank", "covariates"), var.type, x = NULL,
groups = NULL, N = 5, algo = c("mcgd", "bcgd"), thresh = 1e-05,
maxit = 100, max.rank = NULL, trace.it = F, parallel = F,
len = 15)[matrix, data.frame] incomplete and mixed data frame (nxp)
either one of "groups", "covariates" or "low-rank", indicating which model should be fitted
vector of length p indicating types of y columns (gaussian, binomial, poisson)
[matrix, data.frame] covariate matrix (npxq)
factor of length n indicating groups (optional)
[integer] number of cross-validation folds
type of algorithm to use, either one of "bcgd" (small dimensions, gaussian and binomial variables) or "mcgd" (large dimensions, poisson variables)
[positive number] convergence threshold, default is 1e-5
[integer] maximum number of iterations, default is 100
[integer] maximum rank of interaction matrix, default is 2
[boolean] whether information about convergence should be printed
[boolean] whether the N-fold cross-validation should be parallelized, default value is TRUE
[integer] the size of the grid
A list with the following elements
regularization parameter estimated by cross-validation for nuclear norm penalty (interaction matrix)
regularization parameter estimated by cross-validation for l1 norm penalty (main effects)
a table containing the prediction errors for all pairs of parameters