modelPerformance method for merMod objects

For pseudo R2 by cluster, the squared correlation between observed and predicted values for each cluster unit is returned. For the overall model, the marginal and conditional R2 are calculated as described in the references.

Usage

# S3 method for merMod
modelPerformance(object, ...)

Arguments

object

A model from lmer.

...

Additional arguments, not currently used.

Value

a named vector with the marginal and conditional R2 values, if CLUSTER = FALSE, otherwise, a data table with the pseudo R2 for each cluster unit. A list with a data.table with the following elements:

Model

A character string indicating the model type, here merMod

Estimator

A character string indicating whether the model was estimated with REML or ML

N_Obs

The number of observations

N_Groups

A character string indicating the number of unique units in each grouping/clustering variable.

AIC

Akaike Information Criterion

BIC

Bayesian Information Criterion

LL

log likelihood

LLDF

log likelihood degrees of freedom

Sigma

Residual standard deviation

MarginalR2

in sample variance explained by the fixed effects

ConditionalR2

in sample variance explained by the fixed and random effects

MarginalF2

Cohen's F2 effect size R2 / (1 - R2) based off the Marginal R2

ConditionalF2

Cohen's F2 effect size R2 / (1 - R2) based off the Conditional R2

References

For estimating the marginal and conditional R-squared values, see: Nakagawa, S. and Schielzeth, H. (2013) <doi:10.1111/j.2041-210x.2012.00261.x> "A general and simple method for obtaining R2 from generalized linear mixed-effects models" and also: Johnson, P. C. (2014) <doi:10.1111/2041-210X.12225> "Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models"

Examples

library(JWileymisc)
data(aces_daily, package = "JWileymisc")
m1 <- lme4::lmer(PosAff ~ 1 + (1 | UserID),
  data = aces_daily)
modelPerformance(m1)
#> $Performance
#>     Model Estimator N_Obs     N_Groups      AIC      BIC        LL  LLDF
#>    <char>    <char> <num>       <char>    <num>    <num>     <num> <num>
#> 1: merMod      REML  6399 UserID (191) 14800.56 14820.85 -7397.281     3
#>        Sigma MarginalR2 ConditionalR2 MarginalF2 ConditionalF2
#>        <num>      <num>         <num>      <num>         <num>
#> 1: 0.7273136          0     0.5431473          0       1.18889
#> 
#> attr(,"class")
#> [1] "modelPerformance.merMod" "modelPerformance"       

m1 <- lme4::lmer(PosAff ~ STRESS + (1 + STRESS | UserID),
  data = aces_daily)
modelPerformance(m1)
#> $Performance
#>     Model Estimator N_Obs     N_Groups      AIC      BIC        LL  LLDF
#>    <char>    <char> <num>       <char>    <num>    <num>     <num> <num>
#> 1: merMod      REML  6399 UserID (191) 13184.76 13225.35 -6586.381     6
#>        Sigma MarginalR2 ConditionalR2 MarginalF2 ConditionalF2
#>        <num>      <num>         <num>      <num>         <num>
#> 1: 0.6311269  0.1480118     0.6381639  0.1737251      1.763683
#> 
#> attr(,"class")
#> [1] "modelPerformance.merMod" "modelPerformance"       

rm(m1)