pESCA_CV_fullInfo: pESCA model selection based on CV error when full information...
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_fullInfo(simulatedData, lambdas_CV = NULL, penalty = "L2", fun_concave = "gdp", opts = list())

`simulatedData`	the output of function `dataSimu_group_sparse`
`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;
RVs_mat: a matrix contains the RV coefficients in estimating the simulated common and distinct structures;
ranks_mat: a matrix contains the rank estimation of the common and distinct structures;
RMSEs_mat: a matrix contains the RMSEs in estimating the simulated parameters;
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_fullInfo <- pESCA_CV_fullInfo(simulatedData,
                            lambdas_CV, penalty='L2', fun_concave='gdp', opts=opts)

## End(Not run)