Summary of Bayesian Models as HTML Table

Daniel Lüdecke

2020-05-23

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(insight)
library(httr)
library(brms)

# load sample models

# zinb <- read.csv("http://stats.idre.ucla.edu/stat/data/fish.csv")
# set.seed(123)
# m1 <- brm(bf(
#     count ~ persons + child + camper + (1 | persons),
#     zi ~ child + camper + (1 | persons)
#   ),
#   data = zinb,
#   family = zero_inflated_poisson()
# )
m1 <- insight::download_model("brms_zi_2")

# data(epilepsy)
# set.seed(123)
# epilepsy$visit <- as.numeric(epilepsy$visit)
# epilepsy$Base2 <- sample(epilepsy$Base, nrow(epilepsy), replace = TRUE)
# f1 <- bf(Base ~ zAge + count + (1 |ID| patient))
# f2 <- bf(Base2 ~ zAge + Trt + (1 |ID| patient))
# m2 <- brm(f1 + f2 + set_rescor(FALSE), data = epilepsy)
m2 <- insight::download_model("brms_mv_3")

Bayesian models summaries as HTML table

For Bayesian regression models, some of the differences to the table output from simple models or mixed models of tab_models() are the use of Highest Density Intervals instead of confidence intervals, the Bayes-R-squared values, and a different “point estimate” (which is, by default, the median from the posterior draws).

tab_model(m1)

	count
Predictors	Incidence Rate Ratios	CI (95%)
Count Model
Intercept	0.43	0.21 – 1.06
persons	2.31	1.70 – 2.99
child	0.32	0.26 – 0.39
camper	2.08	1.73 – 2.52
Zero-Inflated Model
Intercept	0.53	0.08 – 2.26
child	6.54	3.55 – 12.83
camper	0.43	0.21 – 0.90
Random Effects
σ²	5.23
τ₀₀	33.88
ICC	0.13
N _persons	4
Observations	250
Marginal R² / Conditional R²	0.188 / 0.248

Multivariate response models

For multivariate response models, like mediator-analysis-models, it is recommended to print just one model in the table, as each regression is displayed as own “model” in the output.

tab_model(m2)

	Base		Base2
Predictors	Estimates	CI (95%)	Estimates	CI (95%)
Intercept	24.33	12.60 – 54.21	-15.22	-24.86 – 10.73
Age	-0.14	-1.59 – 0.09	0.96	0.32 – 1.14
count	0.00	-0.00 – 0.00
Trt: Trt 1			0.90	-0.85 – 3.11
Random Effects
σ²	130.27
τ₀₀	1.26
ICC	0.98
N _patient	59
Observations	236

Show two Credible Interval-column

To show a second CI-column, use show.ci50 = TRUE.

tab_model(m2, show.ci50 = TRUE)

	Base			Base2
Predictors	Estimates	CI (50%)	CI (95%)	Estimates	CI (50%)	CI (95%)
Intercept	24.33	19.40 – 33.81	12.60 – 54.21	-15.22	-22.98 – -3.37	-24.86 – 10.73
Age	-0.14	-0.64 – 0.05	-1.59 – 0.09	0.96	0.68 – 1.12	0.32 – 1.14
count	0.00	-0.00 – 0.00	-0.00 – 0.00
Trt: Trt 1				0.90	0.41 – 1.49	-0.85 – 3.11
Random Effects
σ²	129.53
τ₀₀	1.38
ICC	0.98
N _patient	59
Observations	236

Mixing multivariate and univariate response models

When both multivariate and univariate response models are displayed in one table, a column Response is added for the multivariate response model, to indicate the different outcomes.

tab_model(m1, m2)

	count		Base,Base 2
Predictors	Incidence Rate Ratios	CI (95%)	Estimates	CI (95%)	Response
Intercept	0.43	0.21 – 1.06	24.33	12.60 – 54.21	Base
Intercept	0.43	0.21 – 1.06	-15.22	-24.86 – 10.73	Base2
persons	2.31	1.70 – 2.99
child	0.32	0.26 – 0.39
camper	2.08	1.73 – 2.52
Age			-0.14	-1.59 – 0.09	Base
count			0.00	-0.00 – 0.00	Base
Age			0.96	0.32 – 1.14	Base2
Trt: Trt 1			0.90	-0.85 – 3.11	Base2
Zero-Inflated Model
Intercept	0.53	0.08 – 2.26
child	6.54	3.55 – 12.83
camper	0.43	0.21 – 0.90
Random Effects
σ²	5.12		131.76
τ₀₀	33.88		1.28
ICC	0.13		0.98
N	4 _persons		59 _patient
Observations	250		236
Marginal R² / Conditional R²	0.188 / 0.248		NA