mvnramsigma2: Generate Multivariate Normal Data \mathbf{X} \sim...
In jeksterslabds/jeksterslabRdata: Jekster's Lab Data Generation Utilities

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/mvn.R

Generates an n \times k multivariate data matrix or a list of n \times k multivariate data matrices of length R from the multivariate normal distribution

\mathbf{X} \sim \mathcal{N}_{k} ≤ft( \boldsymbol{μ}, \boldsymbol{Σ} \right) . %(\#eq:dist-X-mvn)

The model-implied matrices used to generate data is derived from the Reticular Action Model (RAM) Matrices.

mvnramsigma2(
  n,
  mu = NULL,
  M = NULL,
  A,
  sigma2,
  F,
  I,
  tol = 1e-06,
  empirical = FALSE,
  df = FALSE,
  varnames = NULL,
  R = NULL,
  par = FALSE,
  ncores = NULL,
  mc = TRUE,
  lb = FALSE,
  cl_eval = FALSE,
  cl_export = FALSE,
  cl_expr,
  cl_vars
)

`n`	Integer. Sample size.
`mu`	Numeric vector. Location parameter mean vector \boldsymbol{μ} of length k.
`M`	Numeric vector. Mean structure. Vector of means and intercepts.
`A`	\mathbf{A} matrix. Asymmetric paths (single-headed arrows), such as regression coefficients and factor loadings.
`sigma2`	Numeric vector. Vector of variances σ^2. The first element should be the variance of an exogenous variable.
`F`	\mathbf{F} matrix. Filter matrix used to select the observed variables.
`I`	\mathbf{I} matrix. Identity matrix.
`tol`	Numeric. Tolerance (relative to largest variance) for numerical lack of positive-definiteness in `Sigma`.
`empirical`	Logical. If `TRUE`, `mu` and `Sigma` specify the empirical, not population. mean and covariance matrix.
`df`	Logical. If `TRUE`, the function returns a data frame. If `FALSE`, the function returns a matrix.
`varnames`	Character string. Optional column names with the same length as `mu`.
`R`	Integer. Number of Monte Carlo replications. If `R` is not provided, the function produces a single random data set. If `R` is an integer greater than 1, (e.g., `R = 10`), the function produces multiple random data sets stored in each element of a list of length `R`. `par` and all succeeding arguments are only relevant when `R > 1`.
`par`	Logical. If `TRUE`, use multiple cores. If `FALSE`, use `lapply()`.
`ncores`	Integer. Number of cores to use if `par = TRUE`. If unspecified, defaults to `detectCores() - 1`.
`mc`	Logical. If `TRUE`, use `parallel::mclapply()`. If `FALSE`, use `parallel::parLapply()` or `parallel::parLapplyLB()`. Ignored if `par = FALSE`.
`lb`	Logical. If `TRUE` use `parallel::parLapplyLB()`. If `FALSE`, use `parallel::parLapply()`. Ignored if `par = FALSE` and `mc = TRUE`.
`cl_eval`	Logical. Execute `parallel::clusterEvalQ()` using `cl_expr`. Ignored if `mc = TRUE`.
`cl_export`	Logical. Execute `parallel::clusterExport()` using `cl_vars`. Ignored if `mc = TRUE`.
`cl_expr`	Expression. Expression passed to `parallel::clusterEvalQ()` Ignored if `mc = TRUE`.
`cl_vars`	Character vector. Names of objects to pass to `parallel::clusterExport()` Ignored if `mc = TRUE`.

The \mathbf{S} matrix is derived from a vector of variances sigma2 ≤ft( σ^2 \right) and the proceeds to generating data using the mvnram() function. The first element in sigma2 should be the variance of an exogenous variable.

If R = NULL or R = 1, returns an n \times k multivariate normal data matrix or data frame . If R is an integer greater than 1, (e.g., R = 10) returns a list of length R of n \times k multivariate normal data matrix or data frame.

Ivan Jacob Agaloos Pesigan

Other multivariate data functions: mvnram(), mvn()

mu <- c(100, 100, 100)
A <- matrix(
  data = c(0, sqrt(0.26), 0, 0, 0, sqrt(0.26), 0, 0, 0),
  ncol = 3
)
sigma2 <- c(225, 225, 225)
F <- I <- diag(3)
X <- mvnramsigma2(n = 100, mu = mu, A = A, sigma2 = sigma2, F = F, I = I)
str(X)
Xstar <- mvnramsigma2(n = 100, mu = mu, A = A, sigma2 = sigma2, F = F, I = I, R = 100)
str(Xstar, list.len = 6)