Returns the variance-covariance matrix for the predicted values from object.
# S3 method for ggeffects
vcov(object, vcov.fun = NULL, vcov.type = NULL, vcov.args = NULL, ...)An object of class "ggeffects", as returned by ggpredict().
String, indicating the name of the vcov*()-function
from the sandwich or clubSandwich-package, e.g.
vcov.fun = "vcovCL", which is used to compute (cluster) robust
standard errors for predictions. If NULL, standard errors (and
confidence intervals) for predictions are based on the standard errors as
returned by the predict()-function. Note that probably not
all model objects that work with ggpredict() are also supported
by the sandwich or clubSandwich-package.
Character vector, specifying the estimation type for the
robust covariance matrix estimation (see ?sandwich::vcovHC
or ?clubSandwich::vcovCR for details).
List of named vectors, used as additional arguments that
are passed down to vcov.fun.
Currently not used.
The variance-covariance matrix for the predicted values from object.
The returned matrix has as many rows (and columns) as possible combinations
of predicted values from the ggpredict() call. For example, if there
are two variables in the terms-argument of ggpredict() with 3 and 4
levels each, there will be 3*4 combinations of predicted values, so the returned
matrix has a 12x12 dimension. In short, nrow(object) is always equal to
nrow(vcov(object)). See also 'Examples'.
data(efc)
model <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
result <- ggpredict(model, c("c12hour [meansd]", "c161sex"))
vcov(result)
#> 1 2 3 4 5 6
#> 1 3.62642428 0.71518593 2.922676591 0.011438238 2.21892890 -0.69230945
#> 2 0.71518593 1.74243837 -0.073477976 0.953774462 -0.86214188 0.16511056
#> 3 2.92267659 -0.07347798 2.987294098 -0.008860469 3.05191160 0.05575704
#> 4 0.01143824 0.95377446 -0.008860469 0.933475755 -0.02915918 0.91317705
#> 5 2.21892890 -0.86214188 3.051911604 -0.029159177 3.88489431 0.80382353
#> 6 -0.69230945 0.16511056 0.055757037 0.913177047 0.80382353 1.66124354
# compare standard errors
sqrt(diag(vcov(result)))
#> 1 2 3 4 5 6
#> 1.9043173 1.3200145 1.7283790 0.9661655 1.9710135 1.2888924
as.data.frame(result)
#> x predicted std.error conf.low conf.high group
#> 1 -8.3 76.75375 1.9043173 73.02136 80.48615 Male
#> 2 -8.3 77.79518 1.3200145 75.20800 80.38236 Female
#> 3 42.2 63.96204 1.7283790 60.57448 67.34960 Male
#> 4 42.2 65.00347 0.9661655 63.10982 66.89712 Female
#> 5 92.7 51.17033 1.9710135 47.30721 55.03344 Male
#> 6 92.7 52.21175 1.2888924 49.68557 54.73794 Female
# only two predicted values, no further terms
# vcov() returns a 2x2 matrix
result <- ggpredict(model, "c161sex")
vcov(result)
#> 1 2
#> 1 2.987290958 -0.008861546
#> 2 -0.008861546 0.933476742
# 2 levels for c161sex multiplied by 3 levels for c172code
# result in 6 combinations of predicted values
# thus vcov() returns a 6x6 matrix
result <- ggpredict(model, c("c161sex", "c172code"))
vcov(result)
#> 1 2 3 4 5 6
#> 1 4.87803294 3.024738355 1.17144377 1.76091479 -0.092379792 -1.94567437
#> 2 3.02473835 2.983277938 2.94181752 0.03225611 -0.009204306 -0.05066472
#> 3 1.17144377 2.941817522 4.71219127 -1.69640257 0.073971180 1.84434493
#> 4 1.76091479 0.032256111 -1.69640257 2.58228744 0.853628756 -0.87502993
#> 5 -0.09237979 -0.009204306 0.07397118 0.85362876 0.936804241 1.01997973
#> 6 -1.94567437 -0.050664722 1.84434493 -0.87502993 1.019979727 2.91498938