predict.nbreg | R Documentation |
Methods for extracting information from fitted negative binomial
count regression model objects of class "nbreg"
.
## S3 method for class 'nbreg'
predict(object, newdata,
type = c("response", "prob", "theta", "parameters"), na.action = na.pass, ...)
## S3 method for class 'nbreg'
residuals(object, type = c("pearson", "deviance", "response"), ...)
## S3 method for class 'nbreg'
coef(object, model = c("full", "mu", "theta"), ...)
## S3 method for class 'nbreg'
vcov(object, model = c("full", "mu", "theta"), ...)
## S3 method for class 'nbreg'
terms(x, model = c("full", "mu", "theta"), ...)
## S3 method for class 'nbreg'
model.matrix(object, model = c("mu", "theta"), ...)
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 |
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 "nbreg"
, 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.
As usual, the summary
method returns an object of class "summary.nbreg"
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) and the predicted dispersion
parameter theta. The residuals
method can compute raw residuals
(observed - fitted), Pearson residuals (raw residuals scaled by square root of
variance function), and deviance residuals. The latter are only supported for
negative binomial type 2 models (dist = NB2)
(includes NBH).
A logLik
method is provided, hence AIC
can be called to compute information criteria.
nbreg
data("CrabSatellites", package = "countreg")
fm <- nbreg(satellites ~ width + color, data = CrabSatellites)
plot(residuals(fm, type = "pearson") ~ fitted(fm))
coef(fm)
summary(fm)
logLik(fm)
AIC(fm)
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