pESCA_CV: pESCA model selection based on cross validation error
In YipengUva/RpESCA: R implementaion of pESCA models with various concave penalties

Description Usage Arguments Value Examples

This function implements a missing value based CV model selection approach for the pESCA model on mutliple data sets with the same data type. The details can be found in https://arxiv.org/abs/1902.06241.

1 2	pESCA_CV(dataSets, dataTypes, lambdas_CV = NULL, penalty = "L2", fun_concave = "gdp", opts = list())

`dataSets`	a list contains multiple matrices with same number of rows. Each matrix (`samples * variables`) indicates a data set.
`dataTypes`	a string indicates the data types of the multiple data sets.
`lambdas_CV`	a vector cotains a sequence of values of lambda
`penalty`	The name of the penalty you want to used. "L2": group-wise concave L2 norm penalty; "L1": group-wise concave L1 norm penalty; "element": element-wise concave penalty; "composite": the composition of group- and element-wise penalty.
`fun_concave`	a string indicates the used concave function. Three options are included in the algorithm. "gdp": GDP penalty; "lq": Lq penalty; "scad": SCAD penalty.
`opts`	a list contains the options of the algorithms. tol_obj: tolerance for relative change of joint loss function, default:1E-6; maxit: max number of iterations, default: 1000; gamma: hyper-parameter of the concave penalty, default: 1; R: the initial number of PCs, default: 0.5 `0.5*min(I,J)`; rand_start: initilization method, random (1), SCA(0), default: 0; alphas: dispersion parameters of exponential dispersion families, default: 1. thr_path: the option to generate thresholding path, default: 0; quiet: quiet==1, not show the progress when running the algorithm, default: 0.

This function returns a list contains the results of a pESCA mdoel.

cvErrors_mat: a matrix contains the CV errors for the full data set and each single data set;
inits: a list contains the initilizations of all the constructed models;
outs: a list contains the outputs of all the constructed models;

## Not run: 
result_CV <- pESCA_CV(dataSets, dataTypes,
                            lambdas_CV, penalty='L2', fun_concave='gdp', opts=opts)

## End(Not run)