pwr_func_lmer {mmiCATs}

Power Analysis for Clustered Data

Description

Conducts a power analysis for clustered data using simulation. This function allows for comparing the performance of different estimation methods in terms of power, rejection rate, root mean square error (RMSE), relative RMSE, coverage probability, and average confidence interval width.

Usage

pwr_func_lmer(
  betas = list(int = 0, x1 = -5, x2 = 2, x3 = 10),
  dists = list(x1 = stats::rnorm, x2 = stats::rbinom, x3 = stats::rnorm),
  distpar = list(x1 = list(mean = 0, sd = 1), x2 = list(size = 1, prob = 0.4), x3 =
    list(mean = 1, sd = 2)),
  N = 25,
  reps = 1000,
  alpha = 0.05,
  var_intr = "x1",
  grp = "ID",
  mod = paste0("out ~ x1 + x2 + x3 + (1|", grp, ")"),
  catsmod = "out ~ x1 + x2 + x3",
  r_slope = "x1",
  r_int = "int",
  n_time = 20,
  mean_i = 0,
  var_i = 1,
  mean_s = 0,
  var_s = 1,
  cov_is = 0,
  mean_r = 0,
  var_r = 1,
  cor_mat = NULL,
  corvars = NULL
)

Arguments

betas

Named list of true coefficient values for the fixed effects.

dists

Named list of functions to generate random distributions for each predictor.

distpar

Named list of parameter lists for each distribution function in dists.

N

Integer specifying the number of groups.

reps

Integer specifying the number of replications for the simulation.

alpha

Numeric value specifying the significance level for hypothesis testing.

var_intr

Name of the variable of interest (for power calculations) as a string.

grp

Name of the grouping variable as a string.

mod

Formula for the mixed-effects model.

catsmod

Formula for the CATs model.

r_slope

Name of the random slope variable as a string.

r_int

Name of the random intercept as a string.

n_time

Integer specifying the number of time points per group.

mean_i

Mean for the random intercept.

var_i

Variance for the random intercept.

mean_s

Mean for the random slope.

var_s

Variance for the random slope.

cov_is

Covariance between the random intercept and slope.

mean_r

Mean for the residual error.

var_r

Variance for the residual error.

cor_mat

Correlation matrix for correlated predictors, if any.

corvars

List of vectors, each vector containing names of correlated variables.

Value

A dataframe summarizing the results of the power analysis, including average coefficient estimate, rejection rate, root mean square error, relative root mean square error, coverage probability, and average confidence interval width for each method.

Examples

pwr_func_lmer(reps = 2)