psychmeta (version 2.3.4)

unmix_matrix: Estimate average within-group covariance matrices from a mixture covariance matrix

Description

Estimate average within-group covariance matrices from a mixture covariance matrix

Usage

unmix_matrix(sigma_mat, mu_mat, p_vec, N = Inf, group_names = NULL,
  var_names = NULL)

Arguments

sigma_mat

Mixture covariance matrix.

mu_mat

Matrix of mean parameters, with groups on the rows and variables on the columns.

p_vec

Vector of proportion of cases in each group.

N

Optional total sample size across all groups (used to compute unbiased covariance estimates).

group_names

Optional vector of group names.

var_names

Optional vector of variable names.

Value

List of within-group covariances and means.

Examples

# NOT RUN {
out <- unmix_matrix(sigma_mat = reshape_vec2mat(.5, order = 2),
                    mu_mat = rbind(c(0, 0), c(.5, 1)),
                    p_vec =  c(.3, .7), N = 100)

## Result of unmix_matrix:
out

## Simulated data reproduce the total parameter matrix:
dat <- NULL
for(i in 1:2){
     dat <- rbind(dat, cbind(group = i,
                             data.frame(MASS::mvrnorm(n = round(out$p_group[i] * out$N),
                                                      mu = out$means_raw[i,],
                                                      Sigma = out$cov_group_unbiased[[i]],
                                                      empirical = TRUE))))
}
cov(dat[,-1])
# }