iSFun (version 1.1.0)

iscca: Integrative sparse canonical correlation analysis

Description

This function provides a penalty-based integrative sparse canonical correlation analysis method to handle the multiple datasets with high dimensions generated under similar protocols, which consists of two built-in penalty items for selecting the important variables for users to choose, and two contrasted penalty functions for eliminating the diffierence (magnitude or sign) between estimators within each group.

Usage

iscca(x, y, L, mu1, mu2, mu3, mu4, eps = 1e-04, pen1 = "homogeneity",
  pen2 = "magnitude", scale.x = TRUE, scale.y = TRUE, maxstep = 50,
  submaxstep = 10, trace = FALSE, draw = FALSE)

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.

mu1

numeric, sparsity penalty parameter for vector u.

mu2

numeric, contrasted penalty parameter for vector u.

mu3

numeric, sparsity penalty parameter for vector v.

mu4

numeric, 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.

trace

character, "TRUE" or "FALSE". If TRUE, prints out its screening results of variables.

draw

character, "TRUE" or "FALSE". If TRUE, plot the convergence path of loadings and the heatmap of coefficient beta.

Value

An 'iscca' 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.

• loading.x: the estimated canonical vector of variables x.

• loading.y: the estimated canonical vector of variables y.

• 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.

See Also

See Also as preview.cca, iscca.cv, meta.scca, scca.

Examples

# NOT RUN {
# Load a list with 3 data sets
library(iSFun)
data("simData.cca")
x <- simData.cca$x
y <- simData.cca$y
L <- length(x)
mu1 <- mu3 <- 0.4
mu2 <- mu4 <- 2.5

prev_cca <- preview.cca(x = x, y = y, L = L, scale.x = TRUE, scale.y = TRUE)
res_homo_m <- iscca(x = x, y = y, L = L, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4,
                    eps = 5e-2, maxstep = 50, submaxstep = 10, trace = TRUE, draw = TRUE)

# }
# NOT RUN {
res_homo_s <- iscca(x = x, y = y, L = L, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4,
                    eps = 5e-2, pen1 = "homogeneity", pen2 = "sign", scale.x = TRUE,
                    scale.y = TRUE, maxstep = 50, submaxstep = 10, trace = FALSE, draw = FALSE)

mu1 <- mu3 <- 0.3
mu2 <- mu4 <- 2
res_hete_m <- iscca(x = x, y = y, L = L, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4,
                    eps = 5e-2, pen1 = "heterogeneity", pen2 = "magnitude", scale.x = TRUE,
                    scale.y = TRUE, maxstep = 50, submaxstep = 10, trace = FALSE, draw = FALSE)

res_hete_s <- iscca(x = x, y = y, L = L, mu1 = mu1, mu2 = mu2, mu3 = mu3, mu4 = mu4,
                    eps = 5e-2, pen1 = "heterogeneity", pen2 = "sign", scale.x = TRUE,
                    scale.y = TRUE, maxstep = 50, submaxstep = 10, trace = FALSE, draw = FALSE)
# }