meta.spls | R Documentation |
This function provides penalty-based sparse canonical correlation meta-analytic method to handle the multiple datasets with high dimensions generated under similar protocols, which is based on the principle of maximizing the summary statistics.
meta.spls(x, y, L, mu1, eps = 1e-04, kappa = 0.05, scale.x = TRUE, scale.y = TRUE, maxstep = 50, trace = FALSE)
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. |
mu1 |
numeric, sparsity 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. |
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. |
trace |
character, "TRUE" or "FALSE". If TRUE, prints out its screening results of variables. |
A 'meta.spls' 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.
betahat: the estimated regression coefficients.
loading: the estimated first direction vector.
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.
See Also as ispls
, spls
.
library(iSFun) data("simData.pls") x <- simData.pls$x y <- simData.pls$y L <- length(x) res <- meta.spls(x = x, y = y, L = L, mu1 = 0.03, trace = TRUE)
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