| sm.poisson.bootstrap {sm} | R Documentation |
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.
sm.poisson.bootstrap(x, y, h, degree = 1,
fixed.disp = FALSE, intercept = TRUE, ...)
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. |
degree |
specifies the degree of the fitted polynomial in x on the logit scale
(default=1).
|
fixed.disp |
if TRUE, the dispersion
parameter is kept at value 1 across the simulated samples, otherwise
the dispersion parameter estimated from the sample is used to generate
samples with that dispersion parameter (default=FALSE).
|
intercept |
TRUE (default) if an intercept is to be included
in the fitted model. |
... |
additional parameters passed to sm.poisson.
|
see Section 5.4 of the reference below.
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.
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.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.poisson, sm.binomial.bootstrap
## takes a while: extend sm.script(muscle) provide.data(muscle, options=list(describe=FALSE)) TypeI <- TypeI.P + TypeI.R + TypeI.B sm.poisson.bootstrap(log(TypeI), TypeII, h = 0.5)