cv.mimi: selection of the regularization parameters (lambda1 and...
In mimi: Main Effects and Interactions in Mixed and Incomplete Data

Description Usage Arguments Value

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)

`y`	[matrix, data.frame] incomplete and mixed data frame (nxp)
`model`	either one of "groups", "covariates" or "low-rank", indicating which model should be fitted
`var.type`	vector of length p indicating types of y columns (gaussian, binomial, poisson)
`x`	[matrix, data.frame] covariate matrix (npxq)
`groups`	factor of length n indicating groups (optional)
`N`	[integer] number of cross-validation folds
`algo`	type of algorithm to use, either one of "bcgd" (small dimensions, gaussian and binomial variables) or "mcgd" (large dimensions, poisson variables)
`thresh`	[positive number] convergence threshold, default is 1e-5
`maxit`	[integer] maximum number of iterations, default is 100
`max.rank`	[integer] maximum rank of interaction matrix, default is 2
`trace.it`	[boolean] whether information about convergence should be printed
`parallel`	[boolean] whether the N-fold cross-validation should be parallelized, default value is TRUE
`len`	[integer] the size of the grid

A list with the following elements

`lambda1`	regularization parameter estimated by cross-validation for nuclear norm penalty (interaction matrix)
`lambda2`	regularization parameter estimated by cross-validation for l1 norm penalty (main effects)
`errors`	a table containing the prediction errors for all pairs of parameters