Calculate variance-covariance matrix for marginal effects

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, ...)

Arguments

object	An object of class "ggeffects", as returned by ggpredict().
vcov.fun	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.
vcov.type	Character vector, specifying the estimation type for the robust covariance matrix estimation (see ?sandwich::vcovHC or ?clubSandwich::vcovCR for details).
vcov.args	List of named vectors, used as additional arguments that are passed down to vcov.fun.
...	Currently not used.

Value

The variance-covariance matrix for the predicted values from object.

Details

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'.

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