Beautiful table-outputs: Summarizing mixed effects models #rstats

The current version 1.8.1 of my sjPlot package has two new functions to easily summarize mixed effects models as HTML-table: sjt.lmer and sjt.glmer. Both are very similar, so I focus on showing how to use sjt.lmer here.

# load required packages
library(sjPlot) # table functions
library(sjmisc) # sample data
library(lme4) # fitting models

Linear mixed models summaries as HTML table

The sjt.lmer function prints summaries of linear mixed models (fitted with the lmer function of the lme4-package) as nicely formatted html-tables. First, some sample models are fitted:

# load sample data
data(efc)
# prepare grouping variables
efc$grp = as.factor(efc$e15relat)
levels(x = efc$grp) <- get_val_labels(efc$e15relat)
efc$care.level <- as.factor(rec(efc$n4pstu, "0=0;1=1;2=2;3:4=4"))
levels(x = efc$care.level) <- c("none", "I", "II", "III")

# data frame for fitted model
mydf <- data.frame(neg_c_7 = as.numeric(efc$neg_c_7),
                   sex = as.factor(efc$c161sex),
                   c12hour = as.numeric(efc$c12hour),
                   barthel = as.numeric(efc$barthtot),
                   education = as.factor(efc$c172code),
                   grp = efc$grp,
                   carelevel = efc$care.level)

# fit sample models
fit1 <- lmer(neg_c_7 ~ sex + c12hour + barthel + (1|grp), data = mydf)
fit2 <- lmer(neg_c_7 ~ sex + c12hour + education + barthel + (1|grp), data = mydf)
fit3 <- lmer(neg_c_7 ~ sex + c12hour + education + barthel +
              (1|grp) +
              (1|carelevel), data = mydf)

The simplest way of producing the table output is by passing the fitted models as parameter. By default, estimates (B), confidence intervals (CI) and p-values (p) are reported. The models are named Model 1 and Model 2. The resulting table is divided into three parts:

• Fixed parts – the model’s fixed effects coefficients, including confidence intervals and p-values.
• Random parts – the model’s group count (amount of random intercepts) as well as the Intra-Class-Correlation-Coefficient ICC.
• Summary – Observations, AIC etc.

Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

In the first part on visualizing (generalized) linear mixed effects models, I showed examples of the new functions in the sjPlot package to visualize fixed and random effects (estimates and odds ratios) of (g)lmer results. Meanwhile, I added further features to the functions, which I like to introduce here. This posting is based on the online manual of the sjPlot package.

In this posting, I’d like to give examples for diagnostic and probability plots of odds ratios. The latter examples, of course, only refer to the sjp.glmer function (generalized mixed models). To reproduce these examples, you need the version 1.59 (or higher) of the package, which can be found at GitHub. A submission to CRAN is planned for the next days…