# Cross-validation for ispls Performs K-fold cross validation for the integrative sparse partial least squares over a grid of values for the regularization parameter mu1 and mu2. ```r ispls.cv(x, y, L, K, mu1, mu2, eps = 1e-04, kappa = 0.05, 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. - `mu2`: numeric, the feasible set of contrasted penalty parameter. - `eps`: numeric, the threshold at which the algorithm terminates. - `kappa`: numeric, 0 < kappa < 0.5 and the parameter reduces the effect of the concave part of objective function. - `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 'ispls.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. * betahat:the estimated regression coefficients with selected tuning parameters mu1 and mu2. * loading:the estimated first direction vector with selected tuning parameters mu1 and mu2. * variable:the screening results of variables x. * 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.pls") x <- simData.pls$x y <- simData.pls$y L <- length(x) mu1 <- c(0.04, 0.05) mu2 <- 0.25 res_homo_m <- ispls.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, eps = 1e-2, kappa = 0.05, pen1 = "homogeneity", pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) res_homo_s <- ispls.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, eps = 1e-2, kappa = 0.05, pen1 = "homogeneity", pen2 = "sign", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) res_hete_m <- ispls.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, eps = 1e-2, kappa = 0.05, pen1 = "heterogeneity", pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) res_hete_s <- ispls.cv(x = x, y = y, L = L, K = 5, mu1 = mu1, mu2 = mu2, eps = 1e-2, kappa = 0.05, pen1 = "heterogeneity", pen2 = "sign", scale.x = TRUE, scale.y = TRUE, maxstep = 50, submaxstep = 10) ``` ## References * Liang W, Ma S, Zhang Q, et al. Integrative sparse partial least squares[J]. Statistics in Medicine, 2021, 40(9): 2239-2256. ## See Also See Also as `ispls`.

ispls.cv function