vcov | R Documentation |
Returns the variance-covariance matrix for the predicted values from object
.
## S3 method for class 'ggeffects' vcov(object, vcov.fun = NULL, vcov.type = NULL, vcov.args = NULL, ...)
object |
An object of class |
vcov.fun |
String, indicating the name of the |
vcov.type |
Character vector, specifying the estimation type for the
robust covariance matrix estimation (see |
vcov.args |
List of named vectors, used as additional arguments that
are passed down to |
... |
Currently not used. |
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'.
The variance-covariance matrix for the predicted values from object
.
data(efc) model <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc) result <- ggpredict(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 <- ggpredict(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 <- ggpredict(model, c("c161sex", "c172code")) vcov(result)
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