R: Methods for Mixture of Curves.

residuals.moc {moc}

R Documentation

Methods for Mixture of Curves.

Description

print.moc computes the posterior mixture probabilities of each subject.

residuals.moc computes the response or deviance residuals of an object of class `moc'. These can be weighted by the posterior mixture probabilities within each group or globally.

post.moc computes the posterior mixture probabilities of each subject.

fitted.moc computes the expected values for each observation of a `moc' object.

AIC.moc generates a table of log likelihood, AIC or BIC values along with the degrees of freedom of multiple `moc' objects. The computed values are -2 log Like + k * npar. For the BIC k = log(nobs).

logLik returns on object of class logLik containing the log likelihood, degrees of freedom and number of observations.

Usage


       print.moc(x,...)

       residuals.moc(object,...,type="deviance",post.weight=TRUE,within=TRUE)

       post.moc(object,...)

       fitted.moc(object,...)

       AIC.moc(object,...,k=2)

       logLik.moc(object,...)

Arguments

`x,object,...`	Objects of class `moc'.
`type`	Either deviance ( the default ) or response. Deviance residuals are defined as properly scaled difference in the log likelihood at the observed and fitted value. Response residuals are simply the difference between the observed and expected values.
`post.weight`	Specify if the residuals must be weighted by the posterior mixture probabilities. Weighthing is preferable, it is the default.
`within`	Specify if the weights are rescaled within each mixture group.
`k`	can be any real number or the string "BIC".

Author(s)

Bernard Boulerice <Bernard.Boulerice@umontreal.ca>

References