Nonparametric Poisson regression
This function estimates the regression curve using the local likelihood approach for a vector of Poisson observations and an associated vector of covariate values.
sm.poisson(x, y, h, ...)
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 |
see Sections 3.4 and 5.4 of the reference below.
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.
graphical output will be produced, depending on the value of the
display option.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
with(muscle, {
TypeI <- TypeI.R+ TypeI.P+TypeI.B
sm.poisson(x=log(TypeI), y=TypeII, h=0.25,display="se")
sm.poisson(x=log(TypeI), y=TypeII, h=0.75, col=2, add=TRUE)
})Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.