multid (version 1.0.2)

ml_dadas: Predicting algebraic difference scores in multilevel model

Description

Decomposes difference score predictions to predictions of difference score components by probing simple effects at the levels of the binary moderator.

Usage

ml_dadas(
  model,
  predictor,
  diff_var,
  diff_var_values,
  scaled_estimates = FALSE,
  re_cov_test = FALSE,
  var_boot_test = FALSE,
  nsim = NULL,
  level = 0.95,
  seed = NULL,
  abs_diff_test = 0
)

Value

dadas

A data frame including main effect, interaction, regression coefficients for component scores, dadas, and comparison between interaction and main effect.

scaled_estimates

Scaled regression coefficients for difference score components and difference score.

vpc_at_reduced

Variance partition coefficients in the model without the predictor and interactions.

re_cov_test

Likelihood ratio significance test for random effect covariation.

boot_var_diffs

List of different variance bootstrap tests.

Arguments

model

Multilevel model fitted with lmerTest.

predictor

Character string. Variable name of independent variable predicting difference score.

diff_var

Character string. A variable indicative of difference score components (two groups).

diff_var_values

Vector. Values of the component score groups in diff_var.

scaled_estimates

Logical. Are scaled estimates obtained? Does fit a reduced model for correct standard deviations. (Default FALSE)

re_cov_test

Logical. Significance test for random effect covariation? Does fit a reduced model without the correlation. (Default FALSE)

var_boot_test

Logical. Compare variance by lower-level groups at the upper-level in a reduced model with bootstrap? (Default FALSE)

nsim

Numeric. Number of bootstrap simulations.

level

Numeric. The confidence level required for the var_boot_test output (Default .95)

seed

Numeric. Seed number for bootstrap simulations.

abs_diff_test

Numeric. A value against which absolute difference between component score predictions is tested (Default 0).

Examples

if (FALSE) {
set.seed(95332)
n1 <- 10 # groups
n2 <- 10 # observations per group

dat <- data.frame(
  group = rep(c(LETTERS[1:n1]), each = n2),
  w = sample(c(-0.5, 0.5), n1 * n2, replace = TRUE),
  x = rep(sample(1:5, n1, replace = TRUE), each = n2),
  y = sample(1:5, n1 * n2, replace = TRUE)
)
library(lmerTest)
fit <- lmerTest::lmer(y ~ x * w + (w | group),
  data = dat
)

round(ml_dadas(fit,
  predictor = "x",
  diff_var = "w",
  diff_var_values = c(0.5, -0.5)
)$dadas, 3)
}