| ispls.cv {iSFun} | R Documentation |
Cross-validation for ispls
Description
Performs K-fold cross validation for the integrative sparse partial least squares over a grid of values for the regularization parameter mu1 and mu2.
Usage
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. |
Value
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.
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.
Examples
# 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)