Implemented S3 methods for objects of class ddm
as returned by
function ddm()
.
# S3 method for ddm
print(x, digits = max(3, getOption("digits") - 3), ...)# S3 method for ddm
summary(object, ...)
# S3 method for summary.ddm
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for ddm
coef(object, dpar = c("drift", "boundary", "ndt", "bias", "sv", "full"), ...)
# S3 method for ddm
vcov(object, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)
# S3 method for ddm
model.frame(formula, ...)
# S3 method for ddm
model.matrix(object, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)
# S3 method for ddm
terms(x, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)
# S3 method for ddm
logLik(object, ...)
# S3 method for ddm
update(object, ...)
recover_data.ddm(object, data, ...)
emm_basis.ddm(
object,
trms,
xlev,
grid,
dpar = c("drift", "boundary", "ndt", "bias", "sv"),
...
)
minimal number of significant digits, see
print.default
.
further arguments passed to or from other methods.
object of class ddm
which distributional parameter or DDM parameter to focus on. In
addition to the five DDM parameters c("drift", "boundary", "ndt",
"bias", "sv")
, some methods accept "full"
which returns information
for all estimated parameters.
see model.frame
arguments needed for emmeans support.
The methods should fail with an informative error if a
distributional parameter is selected in dpar
that is fixed and not
estimated.