Wrapper around modeling function to make them behave enough alike that Wald tests and Likelihood ratio are easy to do.
To implement a new type of zero-inflated model, extend this class.
Depending on how different the method is, you will definitely need to override the fit
method, and possibly the model.matrix
, model.matrix<-
, update
, coef
, vcov
, and logLik
methods.
# S4 method for LMlike
summary(object)
# S4 method for LMlike
update(object, formula., design, keepDefaultCoef = FALSE, ...)
# S4 method for LMlike,CoefficientHypothesis
waldTest(object, hypothesis)
# S4 method for LMlike,matrix
waldTest(object, hypothesis)
# S4 method for LMlike,character
lrTest(object, hypothesis)
# S4 method for LMlike,CoefficientHypothesis
lrTest(object, hypothesis)
# S4 method for LMlike,Hypothesis
lrTest(object, hypothesis)
# S4 method for LMlike,matrix
lrTest(object, hypothesis)
# S4 method for GLMlike
logLik(object)
LMlike
formula
something coercible to a data.frame
logical
. Should the coefficient names be preserved from object
or updated if the model matrix has changed?
passed to model.matrix
one of a CoefficientHypothesis
, Hypothesis
or contrast matrix
.
see section "Methods (by generic)"
summary
: Print a summary of the coefficients in each component.
update
: update the formula or design from which the model.matrix
is constructed
waldTest
: Wald test dropping single term specified by CoefficientHypothesis
hypothesis
waldTest
: Wald test of contrast specified by contrast matrix hypothesis
lrTest
: Likelihood ratio test dropping entire term specified by character
hypothesis
naming a term in the symbolic formula.
lrTest
: Likelihood ratio test dropping single term specified by CoefficientHypothesis
hypothesis
lrTest
: Likelihood ratio test dropping single term specified by Hypothesis
hypothesis
lrTest
: Likelihood ratio test dropping single term specified by contrast matrix hypothesis
logLik
: return the log-likelihood of a fitted model
a data.frame from which variables are taken for the right hand side of the regression
The continuous fit
The discrete fit
The left hand side of the regression
A logical
with components "C" and "D", TRUE if the respective component has converged
A formula
for the regression
Both list
s giving arguments that will be passed to the fitter (such as convergence criteria or case weights)
coef
lrTest
waldTest
vcov
logLik