# sm.poisson: Nonparametric Poisson regression In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

## 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

 `1` ```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 specifically relevant for this function are the following: `add`, `col`, `display`, `eval.points`, `nbins`, `ngrid`, `pch`, `xlab`, `ylab`; see the documentation of `sm.options` for their description.

## Details

see Sections 3.4 and 5.4 of the reference below.

## 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` option.

## 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

 ```1 2 3 4 5``` ```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) }) ```

### Example output ```Package 'sm', version 2.2-5.4: type help(sm) for summary information
```

sm documentation built on May 1, 2019, 8:06 p.m.