pscl (version 1.02)

predict.zeroinfl: Methods for zeroinfl Objects

Description

Methods for extracting information from fitted zero-inflated regression model objects of class "zeroinfl".

Usage

## S3 method for class 'zeroinfl':
predict(object, newdata,
  type = c("response", "prob", "count", "zero"), na.action = na.pass, ...)
## S3 method for class 'zeroinfl':
residuals(object, type = c("pearson", "response"), ...)
## S3 method for class 'zeroinfl':
coef(object, model = c("full", "count", "zero"), ...)
## S3 method for class 'zeroinfl':
vcov(object, model = c("full", "count", "zero"), ...)
## S3 method for class 'zeroinfl':
terms(x, model = c("count", "zero"), ...)
## S3 method for class 'zeroinfl':
model.matrix(object, model = c("count", "zero"), ...)

Arguments

object, x

an object of class "zeroinfl" as returned by zeroinfl.

newdata

optionally, a data frame in which to look for variables with which to predict. If omitted, the original observations are used.

type

character specifying the type of predictions or residuals, respectively. For details see below.

na.action

function determining what should be done with missing values in newdata. The default is to predict NA.

model

character specifying for which component of the model the terms or model matrix should be extracted.

...

currently not used.

Details

A set of standard extractor functions for fitted model objects is available for objects of class "zeroinfl", including methods to the generic functions print and summary which print the estimated coefficients along with some further information. The summary in particular supplies partial Wald tests based on the coefficients and the covariance matrix (estimated from the Hessian in the numerical optimization of the log-likelihood). As usual, the summary method returns an object of class "summary.zeroinfl" containing the relevant summary statistics which can subsequently be printed using the associated print method. The methods for coef and vcov by default return a single vector of coefficients and their associated covariance matrix, respectively, i.e., all coefficients are concatenated. By setting the model argument, the estimates for the corresponding model components can be extracted. Both the fitted and predict methods can compute fitted responses. The latter additionally provides the predicted density (i.e., probabilities for the observed counts), the predicted mean from the count component (without zero inflation) and the predicted probability for the zero component. The residuals method can compute raw residuals (observed - fitted) and Pearson residuals (raw residuals scaled by square root of variance function). The terms and model.matrix extractors can be used to extract the relevant information for either component of the model.

A logLik method is provided, hence AIC can be called to compute information criteria.

See Also

zeroinfl

Examples

data("bioChemists", package = "pscl")

fm_zip <- zeroinfl(art ~ ., data = bioChemists)
plot(residuals(fm_zip) ~ fitted(fm_zip))

coef(fm_zip)
coef(fm_zip, model = "count")

summary(fm_zip)
logLik(fm_zip)