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.

sjt.lmer(fit1, fit2)

Note that, due to WordPress-CSS, the resulting HTML-table looks different in this blog-posting compared to the usual output in R!

Customizing labels

Here is an example how to change the labels. Note that showHeaderStrings makes the two labels on top and top left corner appear in the table.

sjt.lmer(fit1,
         fit2,
         showHeaderStrings = TRUE,
         stringB = "Estimate",
         stringCI = "Conf. Int.",
         stringP = "p-value",
         stringDependentVariables = "Response",
         stringPredictors = "Coefficients",
         stringIntercept = "Konstante",
         labelDependentVariables = c("Negative Impact",
                                     "Negative Impact"))

Custom variable labels

To change variable labels in the plot, use the labelPredictors parameter:

sjt.lmer(fit1, fit2,
         labelPredictors = c("Carer's Sex",
                             "Hours of Care",
                             "Elder's Dependency",
                             "Mid Educational Level",
                             "High Educational Level"))

Changing table style

You can change the table style with specific parameters, e.g. to include CI into the same table cell as the estimates, print asterisks instead of numeric p-values etc.

sjt.lmer(fit1, fit2,
         separateConfColumn = FALSE, # ci in same cell as estimates
         showStdBeta = TRUE,         # also show standardized beta values
         pvaluesAsNumbers = FALSE)   # "*" instead of numeric values

Models with different random intercepts

When models have different random intercepts, the sjt.lmer function tries to detect these information from each model. In the Random parts section of the table, information on multiple grouping levels and ICC’s are printed then.

sjt.lmer(fit1, fit2, fit3)

Note that in certain cases, depending on the order of fitted models with several random intercepts, the group label might be incorrect.

Further examples

More details on the sjt.lmer function can be found in this online-manual.