pESCA_CV_twoSteps: 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 different data types, such as quantitative and binary. The details can be found in https://arxiv.org/abs/1902.06241.

1 2	pESCA_CV_twoSteps(dataSets, dataTypes, lambdas_g, lambdas_b, 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_g`	a vector cotains a sequence of values of lambda for the loadings related to quantitative data sets
`lambdas_b`	a vector cotains a sequence of values of lambda for the loadings related to binary data sets
`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.

opts_opt: selected lambdas;
opts_opt: the initialization of the selected model;
cvErrors_b: the CV errors during the tuning of lambda for loadings related to binary data sets;
cvErrors_g: the CV errors during the tuning of lambda for loadings related to quantitative data sets;

## Not run: 
result_CV <- pESCA_CV_twoSteps(dataSets, dataTypes,
                               lambdas_g, lambdas_b,
                               penalty='L2', fun_concave='gdp', opts=opts)

## End(Not run)