In rpsychologist/powerlmm: Power Analysis for Longitudinal Multilevel Models

knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(purrr)
compare_power <- function(x, title, R = 1, cores = 1) {
    p <- x$p
    pow <- get_power(p, df = "satterth", R = cores, cores = cores)

    res <- summary(x)

    res <- res$summary$correct$FE[4, c("Power", "Power_bw", "Power_satt")]

    df <- data.frame("title" = title,
                     "get_power_Satt" = pow$power,
                       "sim_Satt"= res$Power_satt,
                       "diff %" = (pow$power - res$Power_satt)*100,
                       "sim_Wald" = res$Power,
                       "sim_BW" = res$Power_bw, 
                       check.names = FALSE,
                     stringsAsFactors = FALSE)

    df

}

library(powerlmm)
res <- readRDS("simres.rds")

Power results

cmp <- pmap_dfr(list(x = res, 
              title = c("1. 4 clusters",
                        "2. 4 clusters, PN",
                        "3. 12 clusters",
                        "4. 12 unequal clusters",
                        "5. Random clusters"),
              R = c(1, 1, 1, 50, 100), 
              cores = c(1, 1, 1, 30, 30)),
            compare_power)
kable(cmp, digits = 3)

1. 4 clusters

res[[1]]$p
summary(res[[1]])

2. 4 clusters, partially nested

res[[2]]$p
summary(res[[2]])

3. 12 clusters

res[[3]]$p
summary(res[[3]])

4. Unequal clusters

res[[4]]$p
summary(res[[4]])

5. Random clusters

res[[5]]$p
summary(res[[5]])

rpsychologist/powerlmm documentation built on May 11, 2023, 12:24 a.m.