predict.hurdle {pscl} | R Documentation

## Methods for hurdle Objects

### Description

Methods for extracting information from fitted hurdle regression model objects of class `"hurdle"`.

### Usage

    ## S3 method for class 'hurdle'
    predict(object, newdata,
      type = c("response", "prob", "count", "zero"), na.action = na.pass,
      at = NULL, ...)
    ## S3 method for class 'hurdle'
    residuals(object, type = c("pearson", "response"), ...)

    ## S3 method for class 'hurdle'
    coef(object, model = c("full", "count", "zero"), ...)
    ## S3 method for class 'hurdle'
    vcov(object, model = c("full", "count", "zero"), ...)

    ## S3 method for class 'hurdle'
    terms(x, model = c("count", "zero"), ...)
    ## S3 method for class 'hurdle'
    model.matrix(object, model = c("count", "zero"), ...)

### Arguments

`object, x` | an object of class `"hurdle"` as returned by `hurdle`.
`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`.
`at` | optionally, if `type = "prob"`, a numeric vector at which the probabilities are evaluated. By default `0:max(y)` is used where `y` is the original observed response.
`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 `"hurdle"`, 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.hurdle"` 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 component 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 hurdle) and the predicted ratio of probabilities for observing a non-zero count. The latter is the ratio of probabilities for a non-zero implied by the zero hurdle component and a non-zero count in the non-truncated count distribution. See also Appendix C in Zeileis et al. (2008).

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.

### Author(s)

Achim Zeileis <Achim.Zeileis@R-project.org>

### References

Zeileis, Achim, Christian Kleiber and Simon Jackman 2008. “Regression Models for Count Data in R.” Journal of Statistical Software, 27(8). URL http://www.jstatsoft.org/v27/i08/.

### See Also

`hurdle`

### Examples

    data("bioChemists", package = "pscl")
    fm <- hurdle(art ~ ., data = bioChemists)

    plot(residuals(fm) ~ fitted(fm))

    coef(fm)
    coef(fm, model = "zero")

    summary(fm)
    logLik(fm)

[Package pscl version 1.5.2 Index]