estimate_matrix_prods: Estimate covariance matrices and mean vectors containing...

In psychmeta: Psychometric Meta-Analysis Toolkit

Estimate covariance matrices and mean vectors containing product terms

Description

Estimate covariance matrices and mean vectors containing product terms

Usage

estimate_matrix_prods(sigma_mat, mu_vec, prod_list)

Arguments

sigma_mat	Covariance parameter matrix.
mu_vec	Mean parameter matrix.
prod_list	List of 2-element vectors containing the names of variables in sigma_mat.

Value

Augmented covariance matrix and mean vector containing product terms.

Examples

## Establish mean and covariance parameters
mu_vec <- 1:4
sigma_mat <- reshape_vec2mat(c(.1, .2, .3, .4, .5, .6), var_names = LETTERS[1:4])
names(mu_vec) <- colnames(sigma_mat)

## Define a list of variables to be used in estimating products:
prod_list <- list(c("A", "B"),
                  c("A", "C"),
                  c("A", "D"),
                  c("B", "C"),
                  c("B", "D"),
                  c("C", "D"))

## Generate data for the purposes of comparison:
set.seed(1)
dat <- data.frame(MASS::mvrnorm(100000, mu = mu_vec, Sigma = sigma_mat, empirical = TRUE))

## Create product terms in simulated data:
for(i in 1:length(prod_list))
     dat[,paste(prod_list[[i]], collapse = "_x_")] <-
     dat[,prod_list[[i]][1]] * dat[,prod_list[[i]][2]]

## Analytically estimate product variables and compare to simulated data:
estimate_matrix_prods(sigma_mat = sigma_mat, mu_vec = mu_vec, prod_list = prod_list)
round(cov(dat), 2)
round(apply(dat, 2, mean), 2)

psychmeta documentation built on Aug. 26, 2022, 5:14 p.m.