replext_gls: Replications and Extension of Generalized Least Squares...

In scdtb: Single Case Design Tools

Replications and Extension of Generalized Least Squares Simulation

Description

This function performs multiple simulations of Generalized Least Squares (GLS) models across various conditions, extending the work of Maric et al. (2014). It allows for the exploration of different numbers of timepoints per phase and autocorrelation parameters, providing a comprehensive analysis of model performance across these conditions.

Usage

replext_gls(
  n_timepoints_list,
  rho_list,
  iterations,
  n_phases = 2,
  n_IDs = 1,
  betas,
  formula,
  covariate_specs = NULL,
  alpha_level = 0.05,
  verbose = FALSE
)

Arguments

n_timepoints_list	Numeric vector. The numbers of timepoints per phase to simulate.
rho_list	Numeric vector. The autocorrelation parameters to simulate.
iterations	Integer. The number of simulations to run for each combination of conditions.
n_phases	Integer. The number of phases in the design. Default is 2.
n_IDs	Integer. The number of subjects (IDs) to simulate. Default is 1.
betas	Named numeric vector. The true coefficient values for the model.
formula	Formula object. The model formula to be fitted.
covariate_specs	List of lists. Specifications for generating covariates. Each inner list should contain elements 'vars' (variable names), 'dist' (distribution function name), 'args' (list of distribution parameters), and optionally 'expr' (a function to generate data).
alpha_level	Numeric. The significance level for hypothesis tests. Default is 0.05.
verbose	Logical. If TRUE, print progress messages. Default is FALSE.

Value

A data frame of class 'replext_gls' containing simulation results, including:

term	Character. The name of the model term.
tppp	Integer. The number of timepoints per phase.
rho	Numeric. The autocorrelation parameter.
success_rate	Numeric. The proportion of successful model fits.
mean_estimates	Numeric. The mean of the estimated coefficients.
mean_bias	Numeric. The mean bias of the estimated coefficients.
se_estimates	Numeric. The standard error of the estimated coefficients.
rejection_rates	Numeric. The proportion of significant hypothesis tests.
se_rejection_rates	Numeric. The standard error of the rejection rates.
mean_rmse	Numeric. The mean root mean square error of the estimates.
alpha_level	Numeric. The significance level used for hypothesis tests.
iterations	Integer. The number of iterations performed.
n_phases	Integer. The number of phases in the design.
n_IDs	Integer. The number of subjects simulated.
formula	Character. The model formula used.
covariate_specs	Character. The covariate specifications used.

References

Maric, M., de Haan, E., Hogendoorn, S.M., Wolters, L.H. & Huizenga, H.M. (2014). Evaluating statistical and clinical significance of intervention effects in single-case experimental designs: An SPSS method to analyze univariate data. Behavior Therapy. doi: 10.1016/j.beth.2014.09.009

Examples

results <- replext_gls(
  n_timepoints_list = c(10),
  rho_list = c(0.2),
  iterations = 10,
  betas = c("(Intercept)" = 0, "phase1" = 1),
  formula = y ~ phase
)

scdtb documentation built on Sept. 30, 2024, 9:35 a.m.