Summary of Mixed Models as HTML Table"

knitr::opts_chunk$set(
  collapse = TRUE, 
  comment = "#>", 
  message = FALSE
)

if (!requireNamespace("lme4", quietly = TRUE) ||
    !requireNamespace("glmmTMB", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
} else {
  knitr::opts_chunk$set(eval = TRUE)
}

This vignette shows examples for using tab_model() to create HTML tables for mixed models. Basically, tab_model() behaves in a very similar way for mixed models as for other, simple regression models, as shown in this vignette.

# load required packages
library(sjPlot)
library(lme4)
data("sleepstudy")
data("efc")
efc$cluster <- as.factor(efc$e15relat)

Mixed models summaries as HTML table

Unlike tables for non-mixed models, tab_models() adds additional information on the random effects to the table output for mixed models. You can hide these information with show.icc = FALSE and show.re.var = FALSE. Furthermore, the R-squared values are marginal and conditional R-squared statistics, based on Nakagawa et al. 2017.

m1 <- lmer(neg_c_7 ~ c160age + c161sex + e42dep + (1 | cluster), data = efc)
m2 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)

tab_model(m1, m2)

The marginal R-squared considers only the variance of the fixed effects, while the conditional R-squared takes both the fixed and random effects into account.

The p-value is a simple approximation, based on the t-statistics and using the normal distribution function. A more precise p-value can be computed using p.val = "kr". In this case, which only applies to linear mixed models, the computation of p-values is based on conditional F-tests with Kenward-Roger approximation for the degrees of freedom (using the using the pbkrtest-package). Note that here the computation is more time consuming and thus not used as default. You can also display the approximated degrees of freedom with show.df.

tab_model(m1, p.val = "kr", show.df = TRUE)

Generalized linear mixed models

tab_model() can also print and combine models with different link-functions.

data("efc")
efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1)
efc$cluster <- as.factor(efc$e15relat)
m3 <- glmer(
  neg_c_7d ~ c160age + c161sex + e42dep + (1 | cluster),
  data = efc, 
  family = binomial(link = "logit")
)

tab_model(m1, m3)

More complex models

Finally, an example from the glmmTMB-package to show how easy it is to print zero-inflated generalized linear mixed models as HTML table.

library(glmmTMB)
data("Salamanders")
m4 <- glmmTMB(
  count ~ spp + mined + (1 | site),
  ziformula = ~ spp + mined, 
  family = truncated_poisson(link = "log"), 
  data = Salamanders
)

tab_model(m1, m3, m4, show.ci = FALSE)

References

Nakagawa S, Johnson P, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisted and expanded. J. R. Soc. Interface 14. doi: 10.1098/rsif.2017.0213



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sjPlot documentation built on July 10, 2021, 5:07 p.m.