Bootstrap goodness-of-fit test for a Poisson regression model
Usage
sm.poisson.bootstrap(x, y, h, nboot=100, degree=1, ...)
Arguments
x
|
vector of the covariate values
|
y
|
vector of the response values; they must be nonnegative integers.
|
h
|
the smoothing parameter; it must be positive.
|
nboot
|
number of bootstrap samples (default=100).
|
degree
|
specifies the degree of the fitted polynomial in x on the logit scale
(default=1).
|
...
|
additional parameters passed to sm.poisson
|
Description
This function is associated with sm.poisson
for the underlying fitting
procedure.
It performs a Pseudo-Likelihood Ratio Test for the goodness-of-fit of
a standard parametric Poisson regression of specified degree
in the
covariate x
.Details
see Section 5.4 of the reference below.Value
a list containing the observed value of the Pseudo-Likelihood Ratio Test
statistic, its observed p-value as estimated via the bootstrap method,
and the vector of estimated dispersion parameters when this value is not
forced to be 1.Side Effects
Graphical output representing the bootstrap samples is produced on
the current graphical device.
The estimated dispersion parameter, the value of the test statistic
and the observed significance level are printed.References
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for
Data Analysis: the Kernel Approach with S-Plus Illustrations.
Oxford University Press, Oxford.See Also
sm.poisson
, sm.logit.bootstrap
Examples
sm.poisson.bootstrap(exposure.time, N.events, 0.5, degree=2)