knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE ) m1 <- m2 <- NULL if (!requireNamespace("insight", quietly = TRUE) || !requireNamespace("httr", quietly = TRUE) || !requireNamespace("brms", quietly = TRUE)) { knitr::opts_chunk$set(eval = FALSE) } else { knitr::opts_chunk$set(eval = TRUE) library(insight) library(httr) library(sjPlot) library(brms) m1 <- tryCatch(insight::download_model("brms_zi_2"), error = function(e) NULL) m2 <- tryCatch(insight::download_model("brms_mv_3"), error = function(e) NULL) } if (is.null(m1) || is.null(m2)) { knitr::opts_chunk$set(eval = FALSE) }
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).
tab_model(m1)
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)
To show a second CI-column, use show.ci50 = TRUE
.
tab_model(m2, show.ci50 = TRUE)
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)
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