vcov | R Documentation |
Returns the variance-covariance matrix for the predicted values from object
.
## S3 method for class 'ggeffects'
vcov(object, vcov = NULL, vcov_args = NULL, verbose = TRUE, ...)
object |
An object of class |
vcov |
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.
If See details in this vignette. |
vcov_args |
List of arguments to be passed to the function identified by
the |
verbose |
Toggle messages or warnings. |
... |
Currently not used. |
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'.
The variance-covariance matrix for the predicted values from object
.
data(efc)
model <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
result <- predict_response(model, c("c12hour [meansd]", "c161sex"))
vcov(result)
# compare standard errors
sqrt(diag(vcov(result)))
as.data.frame(result)
# only two predicted values, no further terms
# vcov() returns a 2x2 matrix
result <- predict_response(model, "c161sex")
vcov(result)
# 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)
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