HLM_summary: Tidy report of HLM (`lmer` and `glmer` models).

Description

NOTE: model_summary is preferred.

Usage

HLM_summary(model = NULL, test.rand = FALSE, digits = 3, ...)

Value

No return value.

Arguments

model: A model fitted with lmer or glmer function using the lmerTest package.
test.rand: [Only for lmer and glmer] TRUE or FALSE (default). Test random effects (i.e., variance components) by using the likelihood-ratio test (LRT), which is asymptotically chi-square distributed. For large datasets, it is much time-consuming.
digits: Number of decimal places of output. Defaults to 3.
...: Other arguments. You may re-define formula, data, or family.

References

Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R^2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4, 133--142.

Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527--3541.

Examples

Run this code

library(lmerTest)

## Example 1: data from lme4::sleepstudy
# (1) 'Subject' is a grouping/clustering variable
# (2) 'Days' is a level-1 predictor nested within 'Subject'
# (3) No level-2 predictors
m1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
m2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
m3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
HLM_summary(m1)
HLM_summary(m2)
HLM_summary(m3)

## Example 2: data from lmerTest::carrots
# (1) 'Consumer' is a grouping/clustering variable
# (2) 'Sweetness' is a level-1 predictor
# (3) 'Age' and 'Frequency' are level-2 predictors
hlm.1 = lmer(Preference ~ Sweetness + Age + Frequency +
               (1 | Consumer), data=carrots)
hlm.2 = lmer(Preference ~ Sweetness + Age + Frequency +
               (Sweetness | Consumer) + (1 | Product), data=carrots)
HLM_summary(hlm.1)
HLM_summary(hlm.2)