Description Usage Arguments Details Value Side Effects References See Also Examples
This function is associated with sm.poisson
for the underlying
fitting procedure. It performs a PseudoLikelihood Ratio Test for the
goodnessoffit of a standard parametric Poisson regression of specified
degree
in the covariate x
.
1 2  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 
fixed.disp 
if 
intercept 

... 
additional parameters passed to 
see Section 5.4 of the reference below.
a list containing the observed value of the PseudoLikelihood Ratio Test statistic, its observed pvalue 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 SPlus Illustrations. Oxford University Press, Oxford.
sm.poisson
, sm.binomial.bootstrap
1 2 3 4 5  ## takes a while: extend sm.script(muscle)
with(muscle, {
TypeI < TypeI.P + TypeI.R + TypeI.B
sm.poisson.bootstrap(log(TypeI), TypeII, h = 0.5)
})

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