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Calculate variance-covariance matrix for adjusted predictions

Source: R/vcov.R
vcov.Rd

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

Usage

# S3 method for class 'ggeffects'
vcov(
  object,
  vcov_fun = NULL,
  vcov_type = NULL,
  vcov_args = NULL,
  vcov.fun = vcov_fun,
  vcov.type = vcov_type,
  vcov.args = vcov_args,
  verbose = TRUE,
  ...
)

Arguments

object

An object of class "ggeffects", as returned by predict_response().

vcov_fun

Variance-covariance matrix used to compute uncertainty estimates (e.g., for confidence intervals based on robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.

  • A (variance-covariance) matrix

  • A function which returns a covariance matrix (e.g., stats::vcov())

  • A string which indicates the estimation type for the heteroscedasticity-consistent variance-covariance matrix, e.g. vcov_fun = "HC0". Possible values are "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", and "HC5", which will then call the vcovHC()-function from the sandwich package, using the specified type. Further possible values are "CR0", "CR1", "CR1p", "CR1S", "CR2", and "CR3", which will call the vcovCR()-function from the clubSandwich package.

  • A string which indicates the name of the vcov*()-function from the sandwich or clubSandwich packages, 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 packages.

See details in this vignette.

vcov_type

Character vector, specifying the estimation type for the robust covariance matrix estimation (see ?sandwich::vcovHC or ?clubSandwich::vcovCR for details). Only used when vcov_fun is a character string indicating one of the functions from those packages.

vcov_args

List of named vectors, used as additional arguments that are passed down to vcov_fun.

vcov.fun, vcov.type, vcov.args

Deprecated. Use vcov_fun, vcov_type and vcov_args instead.

verbose

Toggle messages or warnings.

...

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 predict_response() call. For example, if there are two variables in the terms-argument of predict_response() 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 <- predict_response(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 73.01577  80.49173   Male
#> 2 -8.3  77.79518 1.3200145 75.20412  80.38623 Female
#> 3 42.2  63.96204 1.7283790 60.56941  67.35467   Male
#> 4 42.2  65.00347 0.9661655 63.10698  66.89995 Female
#> 5 92.7  51.17033 1.9710135 47.30143  55.03923   Male
#> 6 92.7  52.21175 1.2888924 49.68179  54.74172 Female

# only two predicted values, no further terms
# vcov() returns a 2x2 matrix
result <- predict_response(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 <- predict_response(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