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## Loading 'brms' package (version 2.21.0). 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)
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 | 5.63 | ||
τ00 | 33.53 | ||
ICC | 0.14 | ||
N persons | 4 | ||
Observations | 250 | ||
Marginal R2 / Conditional R2 | 0.186 / 0.248 |
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 | 54.64 | |||
τ00 | 4.04 | |||
ICC | 0.96 | |||
N patient | 59 | |||
Observations | 236 |
To show a second CI-column, use 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 | 55.75 | |||||
τ00 | 4.52 | |||||
ICC | 0.96 | |||||
N patient | 59 | |||||
Observations | 236 |
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 | 5.63 | 56.36 | |||
τ00 | 33.59 | 4.36 | |||
ICC | 0.14 | 0.96 | |||
N | 4 persons | 59 patient | |||
Observations | 250 | 236 | |||
Marginal R2 / Conditional R2 | 0.186 / 0.248 | NA |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.