ml_dadas: Predicting algebraic difference scores in multilevel model

In multid: Multivariate Difference Between Two Groups

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
)

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).

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.

Examples

## Not run: 
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)

## End(Not run)

multid documentation built on June 22, 2024, 11:33 a.m.