Movatterモバイル変換

This vignette shows examples for usingtab_model() tocreate HTML tables for mixed models. Basically,tab_model()behaves in a very similar way for mixed models as for other, simpleregression models, as shownin thisvignette.

# load required packageslibrary(sjPlot)library(brms)# sample modelszinb<-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())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)

Bayesian models summaries as HTML table

For Bayesian regression models, some of the differences to the tableoutput fromsimple models ormixed models oftab_models() arethe use ofHighest Density Intervals instead of confidenceintervals, 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	0.42	0.22 – 0.88
persons	2.32	1.86 – 2.93
child	0.32	0.26 – 0.38
camper	2.08	1.73 – 2.53
Zero-Inflated Model
Intercept	0.52	0.11 – 2.21
child	6.44	3.46 – 12.95
camper	0.43	0.21 – 0.87
Random Effects
σ²	5.58
τ₀₀	33.62
ICC	0.14
N_persons	4
Observations	250
Marginal R² / Conditional R²	0.186 / 0.248

Multivariate response models

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

tab_model(m2)

	Base		Base2
Predictors	Estimates	CI (95%)	Estimates	CI (95%)
Intercept	28.61	11.35 – 34.20	26.61	11.24 – 29.03
z Age	-4.85	-5.42 – -1.76	1.21	-0.31 – 2.15
count	0.00	-0.00 – 0.00
Trt: Trt 1			-0.32	-4.36 – 1.43
Random Effects
σ²	54.49
τ₀₀	4.28
ICC	0.96
N_patient	59
Observations	236

Show two Credible Interval-column

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

tab_model(m2,show.ci50 =TRUE)

	Base			Base2
Predictors	Estimates	CI (50%)	CI (95%)	Estimates	CI (50%)	CI (95%)
Intercept	28.61	24.07 – 30.23	11.35 – 34.20	26.61	21.53 – 28.45	11.24 – 29.03
z Age	-4.85	-5.17 – -3.89	-5.42 – -1.76	1.21	0.74 – 1.54	-0.31 – 2.15
count	0.00	-0.00 – 0.00	-0.00 – 0.00
Trt: Trt 1				-0.32	-1.91 – 0.69	-4.36 – 1.43
Random Effects
σ²	54.89
τ₀₀	4.68
ICC	0.96
N_patient	59
Observations	236

Mixing multivariate and univariate response models

When both multivariate and univariate response models are displayedin one table, a columnResponse is added for the multivariateresponse 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	0.42	0.22 – 0.88	28.61	11.35 – 34.20	Base
Intercept	0.42	0.22 – 0.88	26.61	11.24 – 29.03	Base2
persons	2.32	1.86 – 2.93
child	0.32	0.26 – 0.38
camper	2.08	1.73 – 2.53
z Age			-4.85	-5.42 – -1.76	Base
count			0.00	-0.00 – 0.00	Base
z Age			1.21	-0.31 – 2.15	Base2
Trt: Trt 1			-0.32	-4.36 – 1.43	Base2
Zero-Inflated Model
Intercept	0.52	0.11 – 2.21
child	6.44	3.46 – 12.95
camper	0.43	0.21 – 0.87
Random Effects
σ²	5.65		53.91
τ₀₀	33.45		4.58
ICC	0.14		0.96
N	4_persons		59_patient
Observations	250		236
Marginal R² / Conditional R²	0.186 / 0.248		NA

Movatterモバイル変換

Summary of Bayesian Models as HTMLTable

Daniel Lüdecke

2025-07-10

Bayesian models summaries as HTML table

Multivariate response models

Show two Credible Interval-column

Mixing multivariate and univariate response models