In javifar/ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "png", fig.width = 7, fig.height = 5, message = FALSE, warning = FALSE)
if (!requireNamespace("sandwich", quietly = TRUE) ||
    !requireNamespace("clubSandwich", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
}

This vignette demonstrate how to compute confidence intervals based on (cluster) robust variance-covariance matrices for standard errors.

First, we load the required packages and create a sample data set with a binomial and continuous variable as predictor as well as a group factor.

library(ggeffects)
set.seed(123)

# example taken from "?clubSandwich::vcovCR"
m <- 8
cluster <- factor(rep(LETTERS[1:m], 3 + rpois(m, 5)))
n <- length(cluster)
X <- matrix(rnorm(3 * n), n, 3)
nu <- rnorm(m)[cluster]
e <- rnorm(n)
y <- X %*% c(.4, .3, -.3) + nu + e
dat <- data.frame(y, X, cluster, row = 1:n)

# fit linear model
model <- lm(y ~ X1 + X2 + X3, data = dat)

Predictions with normal standard errors

In this example, we use the normal standard errors, as returned by predict(), to compute confidence intervals.

ggpredict(model, "X1")

me <- ggpredict(model, "X1")
plot(me)

Predictions with HC-estimated standard errors

Now, we use sandwich::vcovHC() to estimate heteroskedasticity-consistent standard errors. To do so, first the function name, vcovHC(), must be supplied to the vcov.fun-argument. sandwich::vcovHC(), in turn, has different types of estimation. This must be specified in vcov.type.

ggpredict(model, "X1", vcov.fun = "vcovHC", vcov.type = "HC0")

me <- ggpredict(model, "X1", vcov.fun = "vcovHC", vcov.type = "HC0")
plot(me)

Predictions with cluster-robust standard errors

The last example shows how to define cluster-robust standard errors. These are based on clubSandwich::vcovCR(). Thus, vcov.fun = "vcovCR" is always required when estimating cluster robust standard errors. clubSandwich::vcovCR() has also different estimation types, which must be specified in vcov.type. Furthermore, clubSandwich::vcovCR() requires the cluster-argument, which must be specified in vcov.args:

ggpredict(
  model, "X1", vcov.fun = "vcovCR", vcov.type = "CR0", 
  vcov.args = list(cluster = dat$cluster)
)

me <- ggpredict(
  model, "X1", vcov.fun = "vcovCR", vcov.type = "CR0", 
  vcov.args = list(cluster = dat$cluster)
)
plot(me)

javifar/ggeffects documentation built on Jan. 21, 2022, 12:04 a.m.