LMlike-class {MAST} | R Documentation |
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 signature 'LMlike' summary(object) ## S4 method for signature 'LMlike' update(object, formula., design, ...) ## S4 method for signature 'LMlike,CoefficientHypothesis' waldTest(object, hypothesis) ## S4 method for signature 'LMlike,matrix' waldTest(object, hypothesis) ## S4 method for signature 'LMlike,character' lrTest(object, hypothesis) ## S4 method for signature 'LMlike,CoefficientHypothesis' lrTest(object, hypothesis) ## S4 method for signature 'LMlike,Hypothesis' lrTest(object, hypothesis) ## S4 method for signature 'LMlike,matrix' lrTest(object, hypothesis) ## S4 method for signature 'GLMlike' logLik(object)
object |
|
formula. |
|
design |
something coercible to a |
... |
passed to |
hypothesis |
one of a |
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