# lp: Local Polynomial Model Term In locfit: Local Regression, Likelihood and Density Estimation

## Description

`lp` is a local polynomial model term for Locfit models. Usually, it will be the only term on the RHS of the model formula.

Smoothing parameters should be provided as arguments to `lp()`, rather than to `locfit()`.

## Usage

 `1` ```lp(..., nn, h, adpen, deg, acri, scale, style) ```

## Arguments

 `...` Predictor variables for the local regression model. `nn` Nearest neighbor component of the smoothing parameter. Default value is 0.7, unless either `h` or `adpen` are provided, in which case the default is 0. `h` The constant component of the smoothing parameter. Default: 0. `adpen` Penalty parameter for adaptive fitting. `deg` Degree of polynomial to use. `acri` Criterion for adaptive bandwidth selection. `style` Style for special terms (`left`, `ang` e.t.c.). Do not try to set this directly; call `locfit` instead. `scale` A scale to apply to each variable. This is especially important for multivariate fitting, where variables may be measured in non-comparable units. It is also used to specify the frequency for `ang` terms. If `scale=F` (the default) no scaling is performed. If `scale=T`, marginal standard deviations are used. Alternatively, a numeric vector can provide scales for the individual variables.

`locfit`, `locfit.raw`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```data(ethanol, package="locfit") # fit with 50% nearest neighbor bandwidth. fit <- locfit(NOx~lp(E,nn=0.5),data=ethanol) # bivariate fit. fit <- locfit(NOx~lp(E,C,scale=TRUE),data=ethanol) # density estimation data(geyser, package="locfit") fit <- locfit.raw(lp(geyser,nn=0.1,h=0.8)) ```

### Example output

```locfit 1.5-9.1 	 2013-03-22
```

locfit documentation built on March 25, 2020, 5:07 p.m.