ColMeans: Calculate Column Means and Vars of a "datacggm" Object

In cglasso: Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values

Calculate Column Means and Vars of a “datacggm” Object

Description

Retrieve column means and column variances of a “datacggm” object.

Usage

ColMeans(x)
ColVars(x)

Arguments

x	an object of class ‘datacggm’.

Details

For an R object x of class ‘datacggm’, ColMeans (ColVars) retrieves the column means (variances) of the matrices obtained by getMatrix(x, "Y") and getMatrix(x, "X"). For the response variables, marginal means and variances are estimated using a EM-algorithm under the assumption that the p response variables are marginally normally distributed (see also Details section in datacggm). For the numeric predictor variables, marginal means and variances are computed by mean and var, whereas, for categorical data, ColMeans (ColVars) retrieves the statistical mode and the Gini-Simpson Index, respectively.

Value

ColMeans (ColVars) returns a named list with the columns means (variances).

Author(s)

Luigi Augugliaro ([e-mail])

See Also

datacggm, rcggm, qqcnorm and hist.datacggm.

Examples

set.seed(123)
n <- 1000L
p <- 3L
b0 <- rep(0, p)
Z <- rcggm(n = n, b0 = b0, probl = 0.05, probr = 0.05)
ColMeans(Z)
ColVars(Z)

n <- 1000L
p <- 3L
q <- 2
b0 <- runif(p)
B <- matrix(runif(q * p), nrow = q, ncol = p)
X <- matrix(rnorm(n * q), nrow = n, ncol = q)
Z <- rcggm(n = n, b0 = b0, X = X, B = B, probl = 0.05, probr = 0.05)
ColMeans(Z)
ColVars(Z)

cglasso documentation built on May 29, 2024, 3:04 a.m.