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 lists giving arguments that will be passed to the fitter (such as convergence criteria or case weights)
coef
lrTest
waldTest
vcov
logLik