# Cross-validation for iscca Performs K-fold cross validation for the integrative sparse canonical correlation analysis over a grid of values for the regularization parameter mu1, mu2, mu3 and mu4. ```r iscca.cv(x, y, L, K = 5, mu1, mu2, mu3, mu4, eps = 1e-04, pen1 = "homogeneity", pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) ``` ## Arguments - `x`: list of data matrices, L datasets of explanatory variables. - `y`: list of data matrices, L datasets of dependent 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 for vector u. - `mu2`: numeric, the feasible set of contrasted penalty parameter for vector u. - `mu3`: numeric, the feasible set of sparsity penalty parameter for vector v. - `mu4`: numeric, the feasible set of contrasted penalty parameter for vector v. - `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. - `scale.y`: character, "TRUE" or "FALSE", whether or not to scale the variables y. 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 'iscca.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. * mu3:the sparsity penalty parameter selected from the feasible set of parameter mu3 provided by users. * mu4:the contrasted penalty parameter selected from the feasible set of parameter mu4 provided by users. * fold:The fold assignments for cross-validation for each observation. * loading.x:the estimated canonical vector of variables x with selected tuning parameters. * loading.y:the estimated canonical vector of variables y with selected tuning parameters. * variable.x:the screening results of variables x. * variable.y:the screening results of variables y. * 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. * meany:list of numeric vectors, column mean of the original datasets y. * normy:list of numeric vectors, column standard deviation of the original datasets y. ## Examples ```r # Load a list with 3 data sets library(iSFun) data("simData.cca") x <- simData.cca$x y <- simData.cca$y L <- length(x) mu1 <- c(0.2, 0.4) mu3 <- 0.4 mu2 <- mu4 <- 2.5 res_homo_m <- iscca.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4, eps = 1e-2, pen1 = "homogeneity", pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) res_homo_s <- iscca.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4, eps = 1e-2, pen1 = "homogeneity", pen2 = "sign", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) mu1 <- mu3 <- c(0.1, 0.3) mu2 <- mu4 <- 2 res_hete_m <- iscca.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4, eps = 1e-2, pen1 = "heterogeneity", pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) res_hete_s <- iscca.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4, eps = 1e-2, pen1 = "heterogeneity", pen2 = "sign", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) ``` ## See Also See Also as `iscca`.

iscca.cv function