mpath (version 0.1-20)

predict.zipath: Methods for zipath Objects

Description

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

Usage

## S3 method for class 'zipath':
predict(object, newdata,  which = 1:object$nlambda, 
  type = c("response", "prob", "count", "zero", "nonzero"), na.action = na.pass,
  at = NULL, ...)
## S3 method for class 'zipath':
residuals(object, type = c("pearson", "response"), ...)
## S3 method for class 'zipath':
coef(object, which=1:object$nlambda, model = c("full", "count", "zero"), ...)
## S3 method for class 'zipath':
terms(x, model = c("count", "zero"), ...)
## S3 method for class 'zipath':
model.matrix(object, model = c("count", "zero"), ...)

Arguments

object, x

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

newdata

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

which

Indices of the penalty parameters lambda at which predictions are required. By default, all indices are returned.

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

Re-uses the design of function zeroinfl in package pscl (see reference). A set of standard extractor functions for fitted model objects is available for objects of class "zipath", including methods to the generic functions print and summary which print the estimated coefficients along with some further information. As usual, the summary method returns an object of class "summary.zipath" containing the relevant summary statistics which can subsequently be printed using the associated print method. The methods for coef 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).

References

Zhu Wang, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang and Prasad Devarajan (2014) Penalized Count Data Regression with Application to Hospital Stay after Pediatric Cardiac Surgery, Statistical Methods in Medical Research. 2014 Apr 17. [Epub ahead of print]

Zhu Wang, Shuangge Ma, Ching-Yun Wang, Michael Zappitelli, Prasad Devarajan and Chirag R. Parikh (2014) EM for Regularized Zero Inflated Regression Models with Applications to Postoperative Morbidity after Cardiac Surgery in Children, Statistics in Medicine. 33(29):5192-208.

Zhu Wang, Shuangge Ma and Ching-Yun Wang (2015) Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany, Biometrical Journal. 57(5):867-84.

See Also

zipath

Examples

data("bioChemists", package = "pscl")
fm_zip <- zipath(art ~ . | ., data = bioChemists, nlambda=10)
plot(residuals(fm_zip) ~ fitted(fm_zip))
coef(fm_zip, model = "count")
coef(fm_zip, model = "zero")
summary(fm_zip)
logLik(fm_zip)