Summary of Baysian Models as HTML Table

Daniel Lüdecke

2018-07-11

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(sjmisc)
library(brms)

# load sample datasets
data("efc")
efc <- to_factor(efc, e42dep, c172code, c161sex, e15relat)
zinb <- read.csv("http://stats.idre.ucla.edu/stat/data/fish.csv")

# fit two sample models
m1 <- brm(
  bf(count ~ child + camper + (1 | persons), 
     zi ~ child + camper),
  data = zinb,
  family = zero_inflated_poisson(),
  cores = 4,
  iter = 1000
)

f1 <- bf(neg_c_7 ~ e42dep + c12hour + c172code + (1 |ID| e15relat))
f2 <- bf(c12hour ~ c172code + (1 |ID| e15relat))
m2 <- brm(
  f1 + f2 + set_rescor(FALSE), 
  data = efc, 
  cores = 4,
  iter = 1000
)

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 HDI (50%) HDI (95%)
Intercept 3.08 1.72 – 4.81 0.42 – 24.50
child 0.31 0.29 – 0.33 0.25 – 0.37
camper 2.10 1.99 – 2.24 1.79 – 2.51
Zero-Inflated Model
Intercept 0.53 0.42 – 0.67 0.27 – 1.04
child 3.81 3.20 – 4.81 2.20 – 7.29
camper 0.50 0.38 – 0.62 0.25 – 1.06
Random Effects
σ2 1.00
τ00 persons 4.62
ICC persons 0.72
Observations 250
Bayes R2 / Standard Error 0.185 / 0.028

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)
  average number of hours
of care per week 		Negative impact with 7
items
Predictors Estimates HDI (50%) HDI (95%) Estimates HDI (50%) HDI (95%)
Intercept 36.10 31.38 – 43.26 17.41 – 54.74 8.70 8.39 – 9.16 7.55 – 9.85
intermediate level of
education 		-1.11 -4.00 – 1.46 -8.89 – 7.15 0.20 0.01 – 0.43 -0.39 – 0.83
high level of education -7.51 -11.30 – -4.45 -16.79 – 3.09 0.71 0.47 – 1.00 -0.07 – 1.52
slightly dependent 1.12 0.84 – 1.51 0.13 – 2.06
moderately dependent 2.31 1.93 – 2.58 1.33 – 3.24
severely dependent 3.88 3.61 – 4.33 2.82 – 4.83
average number of hours
of care per week 		0.01 0.00 – 0.01 0.00 – 0.01
Random Effects
σ2 12.81
τ00 e15relat 0.51
ICC e15relat 0.04
Observations 834
Bayes R2 / Standard Error 0.169 / 0.166

Just show one HDI-column

To show just one HDI-column, use show.hdi50 = FALSE.

tab_model(m2, show.hdi50 = FALSE)
  average number of hours
of care per week 		Negative impact with 7
items
Predictors Estimates HDI (95%) Estimates HDI (95%)
Intercept 36.10 17.41 – 54.74 8.70 7.55 – 9.85
intermediate level of
education 		-1.11 -8.89 – 7.15 0.20 -0.39 – 0.83
high level of education -7.51 -16.79 – 3.09 0.71 -0.07 – 1.52
slightly dependent 1.12 0.13 – 2.06
moderately dependent 2.31 1.33 – 3.24
severely dependent 3.88 2.82 – 4.83
average number of hours
of care per week 		0.01 0.00 – 0.01
Random Effects
σ2 12.81
τ00 e15relat 0.51
ICC e15relat 0.04
Observations 834
Bayes R2 / Standard Error 0.169 / 0.166

Mixing multivariate and univariate response models

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

tab_model(m1, m2, show.hdi50 = F)
  count Negative impact with 7
items
Predictors Incidence Rate Ratios HDI (95%) Estimates HDI (95%) Response
Intercept 3.08 0.42 – 24.50 8.70 7.55 – 9.85 negc7
Intercept 3.08 0.42 – 24.50 36.10 17.41 – 54.74 c12hour
child 0.31 0.25 – 0.37
camper 2.10 1.79 – 2.51
slightly dependent 1.12 0.13 – 2.06 negc7
moderately dependent 2.31 1.33 – 3.24 negc7
severely dependent 3.88 2.82 – 4.83 negc7
average number of hours
of care per week 		0.01 0.00 – 0.01 negc7
intermediate level of
education 		0.20 -0.39 – 0.83 negc7
high level of education 0.71 -0.07 – 1.52 negc7
intermediate level of
education 		-1.11 -8.89 – 7.15 c12hour
high level of education -7.51 -16.79 – 3.09 c12hour
Zero-Inflated Model
Intercept 0.53 0.27 – 1.04
child 3.81 2.20 – 7.29
camper 0.50 0.25 – 1.06
Random Effects
σ2 1.00 12.81
τ00 4.62 persons 0.51 e15relat
ICC 0.72 persons 0.04 e15relat
Observations 250 834
Bayes R2 / Standard Error 0.185 / 0.028 0.169 / 0.166