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!

		Model 1				Model 2
		B	CI	p		B	CI	p
Fixed Parts
(Intercept)		14.14	13.15 – 15.12	<.001		13.75	12.63 – 14.87	<.001
sex2		0.48	-0.07 – 1.03	.087		0.67	0.10 – 1.25	.020
c12hour		0.00	-0.00 – 0.01	.233		0.00	-0.00 – 0.01	.214
barthel		-0.05	-0.06 – -0.04	<.001		-0.05	-0.06 – -0.04	<.001
education2						0.19	-0.43 – 0.80	.098
education3						0.80	0.03 – 1.58	.098
Random Parts
Ngrp		8				8
ICCgrp		0.022				0.021
Observations		872				815

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"))

Coefficients	Response
		Negative Impact				Negative Impact
		Estimate	Conf. Int.	p-value		Estimate	Conf. Int.	p-value
Fixed Parts
Konstante		14.14	13.15 – 15.12	<.001		13.75	12.63 – 14.87	<.001
sex2		0.48	-0.07 – 1.03	.087		0.67	0.10 – 1.25	.020
c12hour		0.00	-0.00 – 0.01	.233		0.00	-0.00 – 0.01	.214
barthel		-0.05	-0.06 – -0.04	<.001		-0.05	-0.06 – -0.04	<.001
education2						0.19	-0.43 – 0.80	.098
education3						0.80	0.03 – 1.58	.098
Random Parts
Ngrp		8				8
ICCgrp		0.022				0.021
Observations		872				815

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"))

		Model 1				Model 2
		B	CI	p		B	CI	p
Fixed Parts
(Intercept)		14.14	13.15 – 15.12	<.001		13.75	12.63 – 14.87	<.001
Carer’s Sex		0.48	-0.07 – 1.03	.087		0.67	0.10 – 1.25	.020
Hours of Care		0.00	-0.00 – 0.01	.233		0.00	-0.00 – 0.01	.214
Elder’s Dependency		-0.05	-0.06 – -0.04	<.001		-0.05	-0.06 – -0.04	<.001
Mid Educational Level						0.19	-0.43 – 0.80	.098
High Educational Level						0.80	0.03 – 1.58	.098
Random Parts
Ngrp		8				8
ICCgrp		0.022				0.021
Observations		872				815

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

		Model 1			Model 2
		B (CI)	std. Beta (CI)		B (CI)	std. Beta (CI)
Fixed Parts
(Intercept)		14.14 (13.15 – 15.12) ***			13.75 (12.63 – 14.87) ***
sex2		0.48 (-0.07 – 1.03)	0.05 (-0.01 – 0.11)		0.67 (0.10 – 1.25) *	0.07 (0.01 – 0.14)
c12hour		0.00 (-0.00 – 0.01)	0.04 (-0.03 – 0.12)		0.00 (-0.00 – 0.01)	0.05 (-0.03 – 0.12)
barthel		-0.05 (-0.06 – -0.04) ***	-0.37 (-0.44 – -0.30)		-0.05 (-0.06 – -0.04) ***	-0.37 (-0.44 – -0.30)
education2					0.19 (-0.43 – 0.80)	0.02 (-0.05 – 0.10)
education3					0.80 (0.03 – 1.58)	0.08 (0.00 – 0.16)
Random Parts
Ngrp		8			8
ICCgrp		0.022			0.021
Observations		872			815
Notes	* p<.05 ** p<.01 *** p<.001

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)

		Model 1				Model 2				Model 3
		B	CI	p		B	CI	p		B	CI	p
Fixed Parts
(Intercept)		14.14	13.15 – 15.12	<.001		13.75	12.63 – 14.87	<.001		13.76	12.63 – 14.88	<.001
sex2		0.48	-0.07 – 1.03	.087		0.67	0.10 – 1.25	.020		0.65	0.08 – 1.22	.026
c12hour		0.00	-0.00 – 0.01	.233		0.00	-0.00 – 0.01	.214		0.00	-0.00 – 0.01	.205
barthel		-0.05	-0.06 – -0.04	<.001		-0.05	-0.06 – -0.04	<.001		-0.05	-0.06 – -0.04	<.001
education2						0.19	-0.43 – 0.80	.098		0.16	-0.46 – 0.79	.103
education3						0.80	0.03 – 1.58	.098		0.79	0.01 – 1.57	.103
Random Parts
Ngrp		8				8				8
Ncarelevel										4
ICCgrp		0.022				0.021				0.021
ICCcarelevel										0.000
Observations		872				815				807

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.