Usage
## S3 method for class 'hurdle'
predict(object, newdata,
type = c("mean", "variance", "quantile", "probability", "density", "loglikelihood", "parameters", "distribution"),
model = c("full", "count", "zero", "truncated"),
na.action = na.pass, at = NULL, drop = TRUE, ...)
## 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("full", "count", "zero"), ...)
## S3 method for class 'hurdle'
model.matrix(object, model = c("count", "zero"), ...)
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.
