ddsc_ml {multid}

Deconstructing difference score correlation with multi-level modeling

Description

Deconstructs a bivariate association between x and a difference score y1-y2 with multi-level modeling approach. Within each upper-level unit (lvl2_unit) there can be multiple observations of y1 and y2. Can be used for either pre-fitted lmer-models or to long format data. A difference score correlation is indicative that slopes for y1 as function of x and y2 as function of x are non-parallel. Deconstructing the bivariate association to these slopes allows for understanding the pattern and magnitude of this non-parallelism.

Usage

ddsc_ml(
  model = NULL,
  data = NULL,
  predictor,
  moderator,
  moderator_values,
  DV = NULL,
  lvl2_unit = NULL,
  re_cov_test = FALSE,
  var_boot_test = FALSE,
  boot_slopes = FALSE,
  nsim = NULL,
  level = 0.95,
  seed = NULL,
  covariates = NULL,
  scaling_sd = "observed"
)

Arguments

model

Multilevel model fitted with lmerTest.

data

Data frame.

predictor

Character string. Variable name of independent variable predicting difference score (i.e., x).

moderator

Character string. Variable name indicative of difference score components (w).

moderator_values

Vector. Values of the component score groups in moderator (i.e., y1 and y2).

DV

Character string. Name of the dependent variable (if model is not supplied as input).

lvl2_unit

Character string. Name of the level-2 clustering variable (if model is not supplied as input).

re_cov_test

Logical. Significance test for random effect covariation? (Default FALSE)

var_boot_test

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

boot_slopes

Logical. Are bootstrap estimates and percentile confidence intervals obtained for the estimates presented in results? (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.

covariates

Character string or vector. Variable names of covariates (Default NULL).

scaling_sd

Character string (either default "observed" or "model"). Are the simple slopes scaled with observed or model-based SDs?

Value

results

Summary of key results.

descriptives

Means, standard deviations, and intercorrelations at level 2.

vpc_at_moderator_values

Variance partition coefficients for moderator values in the model without the predictor and interactions.

model

Fitted lmer object.

reduced_model

Fitted lmer object without the predictor.

lvl2_data

Data summarized at level 2.

ddsc_sem_fit

ddsc_sem object fitted to level 2 data.

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(ddsc_ml(model=fit,
              predictor="x",
              moderator="w",
              moderator_values=c(0.5,-0.5))$results,3)

round(ddsc_ml(data=dat,
              DV="y",
              lvl2_unit="group",
              predictor="x",
              moderator="w",
              moderator_values=c(0.5,-0.5))$results,3)


## End(Not run)