# Cross-validation for ispca Performs K-fold cross validation for the integrative sparse principal component analysis over a grid of values for the regularization parameter mu1 and mu2. ```r ispca.cv(x, L, K = 5, mu1, mu2, eps = 1e-04, pen1 = "homogeneity", pen2 = "magnitude", scale.x = TRUE, maxstep = 50, submaxstep = 10) ``` ## Arguments - `x`: list of data matrices, L datasets of explanatory variables. - `L`: numeric, number of datasets. - `K`: numeric, number of cross-validation folds. Default is 5. - `mu1`: numeric, the feasible set of sparsity penalty parameter. - `mu2`: numeric, the feasible set of contrasted penalty parameter. - `eps`: numeric, the threshold at which the algorithm terminates. - `pen1`: character, "homogeneity" or "heterogeneity" type of the sparsity structure. If not specified, the default is homogeneity. - `pen2`: character, "magnitude" or "sign" based contrasted penalty. If not specified, the default is magnitude. - `scale.x`: character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE. - `maxstep`: numeric, maximum iteration steps. The default value is 50. - `submaxstep`: numeric, maximum iteration steps in the sub-iterations. The default value is 10. ## Returns An 'ispca.cv' object that contains the list of the following items. * x:list of data matrices, L datasets of explanatory variables with centered columns. If scale.x is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1. * y:list of data matrices, L datasets of dependent variables with centered columns. If scale.y is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1. * mu1:the sparsity penalty parameter selected from the feasible set of parameter mu1 provided by users. * mu2:the contrasted penalty parameter selected from the feasible set of parameter mu2 provided by users. * fold:The fold assignments for cross-validation for each observation. * eigenvalue:the estimated first eigenvalue with selected tuning parameters mu1 and mu2. * eigenvector:the estimated first eigenvector with selected tuning parameters mu1 and mu2. * component:the estimated first component with selected tuning parameters mu1 and mu2. * variable:the screening results of variables. * meanx:list of numeric vectors, column mean of the original datasets x. * normx:list of numeric vectors, column standard deviation of the original datasets x. ## Examples ```r # Load a list with 3 data sets library(iSFun) data("simData.pca") x <- simData.pca$x L <- length(x) mu1 <- c(0.3, 0.5) mu2 <- 0.002 res_homo_m <- ispca.cv(x = x, L = L, K = 5, mu1 = mu1, mu2 = mu2, pen1 = "homogeneity", pen2 = "magnitude", scale.x = TRUE, maxstep = 50, submaxstep = 10) res_homo_s <- ispca.cv(x = x, L = L, K = 5, mu1 = mu1, mu2 = mu2, pen1 = "homogeneity", pen2 = "sign", scale.x = TRUE, maxstep = 50, submaxstep = 10) mu1 <- c(0.1, 0.15) mu2 <- 0.05 res_hete_m <- ispca.cv(x = x, L = L, K = 5, mu1 = mu1, mu2 = mu2, pen1 = "heterogeneity", pen2 = "magnitude", scale.x = TRUE, maxstep = 50, submaxstep = 10) res_hete_s <- ispca.cv(x = x, L = L, K = 5, mu1 = mu1, mu2 = mu2, pen1 = "heterogeneity", pen2 = "sign", scale.x = TRUE, maxstep = 50, submaxstep = 10) ``` ## References * Fang K, Fan X, Zhang Q, et al. Integrative sparse principal component analysis[J]. Journal of Multivariate Analysis, 2018, 166: 1-16. ## See Also See Also as `ispca`.

ispca.cv function