gendat_mvn_ram: Generate Multivariate Normal Data from RAM Matrices

In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Description

Generates multivariate normal data from the RAM matices, and p dimensional vector of means.

Usage

gendat_mvn_ram(n, A, S, F, I, mu = NULL, ...)

Arguments

n	Sample size.
A	Asymmetric paths, such as regression coefficients and factor loadings.
S	Symmetric matrix representing variances and covariances.
F	Filter matrix used to select the observed variables.
I	Identity matrix.
mu	p dimensional mean vector. Defaults to zeros if unspecified.
...	Arguments to pass to FUN.

Details

The function interally uses the ram function to derive the Σ_{p \times p} matrix from the matices provided. The generated Σ_{p \times p} matrix is then used together with the p dimensional vector of means to generate data using gendat_mvn.

Value

Returns an n \times p multivariate normal data matrix generated using the variance-covariance matrix derived from the RAM matrices and the mean vector provided.

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

A <- matrix(
  data = c(
    0, 0.26^(1 / 2), 0,
    0, 0, 0.26^(1 / 2),
    0, 0, 0
  ),
  ncol = 3
)
S <- F <- I <- diag(3)
S[1, 1] <- 225
S[2, 2] <- 166.5
S[3, 3] <- 166.5
mu <- c(100, 100, 100)
data <- gendat_mvn_ram(
  n = 100,
  A = A,
  S = S,
  F = F,
  I = I,
  mu = mu
)

jeksterslabds/jeksterslabRds documentation built on July 16, 2020, 3:41 p.m.