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)

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)

`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)

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)

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

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.