simulate_lmmelsm: Simulate data from latent uni/multidimensional MELSM

In LMMELSM: Fit Latent Multivariate Mixed Effects Location Scale Models

Simulate data from latent uni/multidimensional MELSM

Description

Simulate data from latent uni/multidimensional MELSM

Usage

simulate_lmmelsm(
  n,
  K,
  lambda,
  resid,
  nu,
  mu_beta = NULL,
  logsd_beta = NULL,
  P_random_ind = NULL,
  Q_random_ind = NULL,
  mu_logsd_betas_cor,
  mu_logsd_betas_sigma,
  epsilon_cor,
  zeta = NULL,
  X_loc = NULL,
  X_sca = NULL,
  X_bet = NULL,
  L2_pred_only = FALSE
)

Arguments

n	Integer. Number of repeated observations per group.
K	Integer. Number of groups.
lambda	Matrix (FxJ). Loading matrix.
resid	Numeric vector (J). Residual SDs.
nu	Numeric vector (J). Intercepts.
mu_beta	Matrix (PxF). Location coefficient matrix.
logsd_beta	Matrix (QxF). Scale coefficient matrix.
P_random_ind	Integer vector (P_random). Which location predictors have random slopes.
Q_random_ind	Integer vector (Q_random). Which scale predictors have random slopes.
mu_logsd_betas_cor	Matrix (Symmetric, SPD; F2 + P_randomF + Q_random*F). Correlation matrix of random effects (slopes and intercepts, for location and scale models).
mu_logsd_betas_sigma	Numeric vector (Positive; F2 + P_randomF + Q_random*F). RE SDs (intercepts on exponentiated scale, if zeta is specified).
epsilon_cor	Matrix (Symmetric, SPD; F). Stochastic error term correlation between factors.
zeta	Matrix (Rx[F*2 + P_random*F + Q_random*F]). Coefficient matrix for predicting RE SDs.
X_loc	Matrix (Optional; NxP). Location design matrix.
X_sca	Matrix (Optional; NxQ). Scale design matrix.
X_bet	Matrix (Optional; NxR). Between-SD design matrix.
L2_pred_only	Logical. Whether predictors should be group-level (TRUE) or observation level (FALSE).

Value

List of params (list), data (list), and df (data.frame).

Author(s)

Stephen R. Martin

LMMELSM documentation built on Dec. 28, 2022, 1:32 a.m.