Linear Model-like Class

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, ...)

# 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)

Arguments

object
LMlike
formula.
formula
design
something coercible to a data.frame
...
passed to model.matrix
hypothesis
one of a CoefficientHypothesis, Hypothesis or contrast matrix.

Value

see section "Methods (by generic)"

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

Slots

design
a data.frame from which variables are taken for the right hand side of the regression
fitC
The continuous fit
fitD
The discrete fit
response
The left hand side of the regression
fitted
A logical with components "C" and "D", TRUE if the respective component has converged
formula
A formula for the regression
fitArgsC
fitArgsD
Both lists giving arguments that will be passed to the fitter (such as convergence criteria or case weights)

See also

coef

lrTest

waldTest

vcov

logLik