This function estimates the regression curve using the local likelihood
approach for a vector of Poisson observations and an associated vector
of covariate values.
Usage
sm.poisson(x, y, h, ...)
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.
...
other optional parameters are passed to the sm.options function, through
a mechanism which limits their effect only to this call of the function;
those relevant for this function are the following:
add
{
if graphical output
Value
A list containing vectors with the evaluation points, the corresponding
probability estimates, the linear predictors, the upper and lower points
of the variability bands and the standard errors on the linear predictor
scale.
Side Effects
graphical output will be produced, depending on the value of the
display parameter.
Details
see Sections 3.4 and 5.4 of the reference below.
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.