# Practical example: (Cluster) Robust Standard Errors

#### 2020-03-14

## Registered S3 method overwritten by 'clubSandwich':
##   method    from
##   bread.mlm sandwich

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. To avoid convergence warnings, the continuous variable is standardized.

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")
#>
#> # Predicted values of y
#> # x = X1
#>
#>     x | Predicted |   SE |         95% CI
#> -----------------------------------------
#> -2.50 |     -1.54 | 0.44 | [-2.40, -0.69]
#> -2.00 |     -1.28 | 0.36 | [-1.99, -0.57]
#> -1.00 |     -0.74 | 0.22 | [-1.18, -0.30]
#> -0.50 |     -0.47 | 0.17 | [-0.81, -0.13]
#>  0.00 |     -0.20 | 0.15 | [-0.50,  0.09]
#>  0.50 |      0.07 | 0.17 | [-0.27,  0.40]
#>  1.00 |      0.34 | 0.22 | [-0.10,  0.77]
#>  2.50 |      1.14 | 0.43 | [ 0.29,  1.99]
#>
#> * X2 = -0.08
#> * X3 =  0.09
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")
#>
#> # Predicted values of y
#> # x = X1
#>
#>     x | Predicted |   SE |         95% CI
#> -----------------------------------------
#> -2.50 |     -1.54 | 0.43 | [-2.40, -0.69]
#> -2.00 |     -1.28 | 0.35 | [-1.97, -0.59]
#> -1.00 |     -0.74 | 0.20 | [-1.13, -0.34]
#> -0.50 |     -0.47 | 0.15 | [-0.77, -0.17]
#>  0.00 |     -0.20 | 0.15 | [-0.49,  0.08]
#>  0.50 |      0.07 | 0.19 | [-0.30,  0.43]
#>  1.00 |      0.34 | 0.25 | [-0.16,  0.83]
#>  2.50 |      1.14 | 0.50 | [ 0.16,  2.12]
#>
#> * X2 = -0.08
#> * X3 =  0.09
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$cluster) ) #> #> # Predicted values of y #> # x = X1 #> #> x | Predicted | SE | 95% CI #> ----------------------------------------- #> -2.50 | -1.54 | 0.40 | [-2.33, -0.76] #> -2.00 | -1.28 | 0.35 | [-1.96, -0.59] #> -1.00 | -0.74 | 0.29 | [-1.30, -0.18] #> -0.50 | -0.47 | 0.29 | [-1.03, 0.09] #> 0.00 | -0.20 | 0.31 | [-0.81, 0.40] #> 0.50 | 0.07 | 0.35 | [-0.62, 0.75] #> 1.00 | 0.34 | 0.40 | [-0.45, 1.12] #> 2.50 | 1.14 | 0.60 | [-0.03, 2.31] #> #> Adjusted for: #> * X2 = -0.08 #> * X3 = 0.09 me <- ggpredict( model, "X1", vcov.fun = "vcovCR", vcov.type = "CR0", vcov.args = list(cluster = dat$cluster)
)
plot(me)