mlmtools (version 1.0.2)

ICCm: Computes ICC values for mixed-effects models

Description

Computes ICC values for lme4-fitted mixed-effects models.

Usage

ICCm(model, re_type = c("NA"))

Value

If re_type is "NA", the proportion of variance at the random effect is computed.

If re_type = "nested", the likeness of y scores in the same level 3 unit (the proportion of variance at Level3_factor), the likeness of y scores in the same level 2 units in the same level 3 unit (proportion of variance at Level3_factor and Level2_factor), and the likeness of level 2 units in the same level 3 unit (proportion of Level2_factor variance at Level3_factor) are computed.

If re_type = "cc", the likeness of y scores in the same C1_factor unit (correlation between outcome values of units in same C1_factor but different C2_factor), the likeness of y scores in the same C2_factor (correlation between outcome values of units in the same C2_factor but different C2_factor), and the likeness of y scores in the same C1_factor and C2_factor combination (correlation between outcome values of units in the same C2_factor and C2_factor) are computed.

Arguments

model

A linear mixed-effects model of class lmerMod or lmerModLmerTest

re_type

A value indicating whether a model with two random effects is nested or cross-classified

References

Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel Analysis (2nd Ed.). Sage Publications Ltd. Goldstein, H., Browne, W., & Rasbash, J. (2002). Partitioning variation in multilevel models. Understanding statistics: statistical issues in psychology, education, and the social sciences, 1(4), 223-231.

Examples

# Gaussian
## Read in data
data(instruction)
## Create model
mod <- lme4::lmer(mathgain ~ (1 | classid), data = instruction)
## Estimate ICC
ICCm(mod)

# Logistic
## Read in data
data(reporting)
## Create model
mod <- lme4::glmer(mention.outliers ~ Basics + (1 | Journal), data = reporting, family = "binomial")
## Estimate ICC
ICCm(mod)