Methods for extracting information from fitted zero-inflated
regression model objects of class
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## S3 method for class 'zeroinfl' predict(object, newdata, type = c("response", "prob", "count", "zero"), na.action = na.pass, at = NULL, ...) ## 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"), ...)
an object of class
optionally, a data frame in which to look for variables with which to predict. If omitted, the original observations are used.
character specifying the type of predictions or residuals, respectively. For details see below.
function determining what should be done with missing values
character specifying for which component of the model the terms or model matrix should be extracted.
currently not used.
A set of standard extractor functions for fitted model objects is available for
objects of class
"zeroinfl", including methods to the generic functions
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
containing the relevant summary statistics which can subsequently be printed
using the associated
The methods for
vcov by default
return a single vector of coefficients and their associated covariance matrix,
respectively, i.e., all coefficients are concatenated. By setting the
argument, the estimates for the corresponding model components can be extracted.
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
residuals method can compute
raw residuals (observed - fitted) and Pearson residuals (raw residuals scaled by
square root of variance function).
model.matrix extractors can
be used to extract the relevant information for either component of the model.
logLik method is provided, hence
can be called to compute information criteria.
Achim Zeileis <[email protected]>
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