sm (version 2.0-2)

sm.poisson: Nonparametric Poisson regression

Description

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 is produced, this parameter controls whether a new plot is created, or graphical output is added to the existing one.

col

colour used for plotting curves and points. Default: col=1.

display

controls the type of graphical output; possible values are "estimate" (default), "se", "none".

eval.points

the vector of points on the x axis where the regression must be estimated. If the parameter eval.points is not given, this vector is chosen to be formed by ngrid equally spaced points between min(x) and

nbins

The number of bins used when binning operation is performed. If nbins=0, binning is not performed; if nbins=NA (default), binning is switched on when the number of design points exceeds 100.

ngrid

the number of points where the regression curve must be estimated (only used if eval.points is not given). Default: ngrid=25.

pch

plotting character of the raw observed frequency. Default: pch=1.

xlab

label of the x-axis. Default is the name of x object.

ylab

label of the y-axis .Default is the name of y object.

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.

See Also

sm.binomial, sm.binomial.bootstrap, binning, glm

Examples

sm.poisson(exposure.time, N.events, 0.5, display="se")