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params <-
list(EVAL = FALSE)
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
message = FALSE
)
if (!requireNamespace("dplyr", quietly = TRUE) ||
!requireNamespace("sandwich", quietly = TRUE) ||
!requireNamespace("lme4", quietly = TRUE) ||
!requireNamespace("clubSandwich", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
} else {
knitr::opts_chunk$set(eval = TRUE)
library(sjPlot)
library(dplyr)
}
set.seed(333)
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
data(iris)
model <- lm(Petal.Length ~ Sepal.Length * Species + Sepal.Width, data = iris)
# model parameters, where SE, CI and p-values are based on robust estimation
tab_model(model, vcov.fun = "HC3", show.se = TRUE)
# compare standard errors to result from sandwich-package
unname(sqrt(diag(sandwich::vcovHC(model))))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# change estimation-type
tab_model(model, vcov.fun = "CL", vcov.args = list(type = "HC1"), show.se = TRUE)
# compare standard errors to result from sandwich-package
unname(sqrt(diag(sandwich::vcovCL(model))))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris)))
# change estimation-type, defining additional arguments
tab_model(
model,
vcov.fun = "CL",
vcov.args = list(type = "HC1", cluster = iris$cluster),
show.se = TRUE
)
# compare standard errors to result from sandwich-package
unname(sqrt(diag(sandwich::vcovCL(model, cluster = iris$cluster))))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# create fake-cluster-variable, to demonstrate cluster robust standard errors
iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris)))
# cluster-robust estimation
tab_model(
model,
vcov.fun = "CR1",
vcov.args = list(cluster = iris$cluster),
show.se = TRUE
)
# compare standard errors to result from clubSsandwich-package
unname(sqrt(diag(clubSandwich::vcovCR(model, type = "CR1", cluster = iris$cluster))))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# model parameters, robust estimation on standardized model
tab_model(
model,
show.std = "std",
vcov.fun = "HC"
)
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(lme4)
data(iris)
set.seed(1234)
iris$grp <- as.factor(sample(1:3, nrow(iris), replace = TRUE))
# fit example model
model <- lme4::lmer(
Sepal.Length ~ Species * Sepal.Width + Petal.Length + (1 | grp),
data = iris
)
# normal model parameters, like from 'summary()'
tab_model(model)
# model parameters, cluster robust estimation for mixed models
tab_model(
model,
vcov.fun = "CR1",
vcov.args = list(cluster = iris$grp)
)
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# model parameters, cluster robust estimation on standardized mixed model
tab_model(
model,
show.std = "std",
vcov.fun = "CR1",
vcov.args = list(cluster = iris$grp)
)
|
