Summary of Bayesian Models as HTML Table (original) (raw)

2024-11-29

## Loading 'brms' package (version 2.22.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').

## 
## Attaching package: 'brms'

## The following objects are masked from 'package:rstanarm':
## 
##     dirichlet, exponential, get_y, lasso, ngrps

## The following object is masked from 'package:stats':
## 
##     ar

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(brms)

# 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()
)

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 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).

| | 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 | 5.01 | | | τ00 | 33.76 | | | ICC | 0.13 | | | N persons | 4 | | | Observations | 250 | | | Marginal R2 / Conditional R2 | 0.186 / 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.

| | 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 | 54.02 | | | | | τ00 | 4.05 | | | | | ICC | 0.96 | | | | | 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 | 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 | 56.50 | | | | | | | τ00 | 4.18 | | | | | | | ICC | 0.96 | | | | | | | 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.

| | 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 | 5.43 | 54.47 | | | | | τ00 | 33.65 | 4.51 | | | | | ICC | 0.14 | 0.96 | | | | | N | 4 persons | 59 patient | | | | | Observations | 250 | 236 | | | | | Marginal R2 / Conditional R2 | 0.186 / 0.248 | NA | | | |