Description Usage Arguments Details Author(s) References See Also Examples

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

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## 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"), ...)
``` |

`object, x` |
an object of class |

`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 |

`at` |
optionally, if |

`model` |
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 `"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.

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

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/.

1 2 3 4 5 6 7 8 9 10 |

```
Classes and Methods for R developed in the
Political Science Computational Laboratory
Department of Political Science
Stanford University
Simon Jackman
hurdle and zeroinfl functions by Achim Zeileis
count_(Intercept) count_femWomen count_marMarried count_kid5
0.67113931 -0.22858266 0.09648499 -0.14218756
count_phd count_ment zero_(Intercept) zero_femWomen
-0.01272637 0.01874548 0.23679601 -0.25115113
zero_marMarried zero_kid5 zero_phd zero_ment
0.32623358 -0.28524872 0.02221940 0.08012135
(Intercept) femWomen marMarried kid5 phd ment
0.23679601 -0.25115113 0.32623358 -0.28524872 0.02221940 0.08012135
Call:
hurdle(formula = art ~ ., data = bioChemists)
Pearson residuals:
Min 1Q Median 3Q Max
-2.4105 -0.8913 -0.2817 0.5530 7.0324
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.67114 0.12246 5.481 4.24e-08 ***
femWomen -0.22858 0.06522 -3.505 0.000457 ***
marMarried 0.09649 0.07283 1.325 0.185209
kid5 -0.14219 0.04845 -2.934 0.003341 **
phd -0.01273 0.03130 -0.407 0.684343
ment 0.01875 0.00228 8.222 < 2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.23680 0.29552 0.801 0.4230
femWomen -0.25115 0.15911 -1.579 0.1144
marMarried 0.32623 0.18082 1.804 0.0712 .
kid5 -0.28525 0.11113 -2.567 0.0103 *
phd 0.02222 0.07956 0.279 0.7800
ment 0.08012 0.01302 6.155 7.52e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 12
Log-likelihood: -1605 on 12 Df
'log Lik.' -1605.312 (df=12)
```

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