Simulate Hierarchical Linear Model Data

Generates simulated data from a hierarchical linear model with optional observed heterogeneity (linear or nonlinear via Z covariates).

Usage

sim_hier_linear(
  nreg = 100,
  nobs = 10,
  nvar = 2,
  nz = 3,
  const = TRUE,
  het_observed = c("none", "linear", "step", "friedman"),
  target_var_betabar = 1,
  target_var_eps = 0.5,
  sigma_sq = 1,
  seed = NULL
)

Arguments

nreg

Number of cross-sectional units.

nobs

Number of observations per unit.

nvar

Number of X variables (excluding intercept if const=TRUE).

nz

Number of Z variables. Set to 0 for no observed heterogeneity.

const

Logical. Include an intercept in X? Default TRUE.

het_observed

Character. Functional form of observed heterogeneity. Options: "none", "linear", "step", "friedman".

target_var_betabar

Numeric. Target variance for the first coefficient of the observed component betabar_i = f(Z_i). Default 1.0.

target_var_eps

Numeric. Target variance for each coefficient in the unobserved component eps_i. Default 0.5.

sigma_sq

Numeric. Error variance for y_i = X_i * beta_i + e_i. Default 1.0.

seed

Integer. Optional random seed.

Value

A list containing:

regdata

List of length nreg. Each element is a list with y, X, XpX, Xpy.

Z

nreg x nz matrix of covariates (NULL if nz=0).

true_values

List with beta_true, betabar_true, eps_true, sigma_sq, Delta (if linear).