r2_pve {mlmhelpr}

Proportion of variance explained

Description

r2_pve calculates the proportional reduction in variance explained (PVE) by adding variables to a prior, nested model. The PVE is considered a local effect size estimate (Peugh, 2010; Raudenbush & Bryk, 2002).

Usage

r2_pve(model1, model2 = NULL)

Arguments

model1

Previous model, produced using the lme4::lmer() function. Usually, this is the null or unconditional model.

model2

Current model, produced using the lme4::lmer() function.

Value

Data frame containing the proportion of variance explained at each level

References

Peugh JL (2010). “A Practical Guide to Multilevel Modeling.” Journal of School Psychology, 48(1), 85–112. ISSN 00224405, doi:10.1016/j.jsp.2009.09.002.

Raudenbush SW, Bryk AS (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE. ISBN 9780761919049.

Examples

fit1 <- lme4::lmer(mathach ~ 1 + (1|id), data=hsb, REML=FALSE)
fit2 <- lme4::lmer(mathach ~ 1 + ses + (1|id), data=hsb, REML=FALSE)

r2_pve(fit1, fit2)