Description Usage Arguments Details Value Examples
Negative binomial models (NB2) with non-constant theta.
1 2 3 4 5 6 7 |
formula |
a formula expression of syntax |
data |
an optional data frame containing the variables occurring in the formulas. |
subset |
an optional vector specifying a subset of observations to be used for fitting. |
na.action |
a function which indicates what should happen when the data
contain |
model |
logical. If |
x, y |
for |
z |
a design matrix with regressors for the scale. |
... |
arguments to be used to form the default |
control, maxit, start |
a list of control parameters passed to |
grad |
logical. Should gradients be used for optimization? If |
hessian |
logical or character. Should a numeric approximation of the
(negative) Hessian matrix be computed? Either |
enbin
fits negative binomial regression models with optionally covariate-dependent theta
using analytical gradient based maximum likelihood estimation
y* ~ N(mu, theta)
,
The mean mu and scale theta are linked to two linear predictors
log(mu) = x'b
log(theta) = z'g
.
enbin_fit
is the actual workhorse, where the fitting takes place.
A set of standard extractor functions for fitted model objects is available for
objects of class "enbin"
, including methods to the generic functions
print
, summary
, coef
,
vcov
, logLik
, residuals
,
predict
, terms
,
model.frame
, model.matrix
, update
,
estfun
and bread
(from the sandwich package),
and
getSummary
(from the memisc package, enabling mtable
).
See predict.enbin
and coef.enbin
for more details
on some methods with non-standard arguments.
enbin
returns an object of class "enbin"
, i.e., a list with components as follows.
enbin_fit
returns an unclassed list with components up to df
.
coefficients |
a list with elements |
counts |
count of function and gradient evaluations from |
convergence |
convergence code from |
message |
optional further information from |
vcov |
covariance matrix of all parameters in the model, |
residuals |
a vector of raw residuals (observed - fitted), |
fitted.values |
a list with elements |
method |
the method argument passed to the |
nobs |
number of observations, |
df |
number of estimated parameters, |
call |
the original function call, |
formula |
the original formula, |
terms |
a list with elements |
levels |
a list with elements |
contrasts |
a list with elements |
model |
the full model frame (if |
y |
the numeric response vector (if |
x |
a list with elements |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## data on alcohol and tobacco expenditures in Belgian households
data("RecreationDemand", package = "AER")
## model comparison of negative binomial (NB2) model with constant vs. non-constant theta
m1 <- enbin(trips ~ . - income, data = RecreationDemand)
m2 <- enbin(trips~ . - income | . - income, data = RecreationDemand)
## comparison of the two models
AIC(m1, m2)
BIC(m1, m2)
## comparison with glm.nb
if(require("MASS")) {
c1 <- glm.nb(trips ~ . - income, data = RecreationDemand)
summary(c1)
}
|
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