ddm-methods {fddm}

Methods for ddm objects

Description

Implemented S3 methods for objects of class ddm as returned by function ddm().

Usage

## S3 method for class 'ddm'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'ddm'
summary(object, ...)

## S3 method for class 'summary.ddm'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'ddm'
coef(object, dpar = c("drift", "boundary", "ndt", "bias", "sv", "full"), ...)

## S3 method for class 'ddm'
vcov(object, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)

## S3 method for class 'ddm'
model.frame(formula, ...)

## S3 method for class 'ddm'
model.matrix(object, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)

## S3 method for class 'ddm'
terms(x, dpar = c("drift", "boundary", "ndt", "bias", "sv"), ...)

## S3 method for class 'ddm'
logLik(object, ...)

## S3 method for class 'ddm'
update(object, ...)

recover_data.ddm(object, data, ...)

emm_basis.ddm(
  object,
  trms,
  xlev,
  grid,
  dpar = c("drift", "boundary", "ndt", "bias", "sv"),
  ...
)

Arguments

digits

minimal number of significant digits, see print.default.

...

further arguments passed to or from other methods.

object, x

object of class ddm

dpar

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.

formula

see model.frame

data, trms, xlev, grid

arguments needed for emmeans support.

Details

The methods should fail with an informative error if a distributional parameter is selected in dpar that is fixed and not estimated.