# loess.sd: Local Polynomial Regression Fitting with Variability bands In msir: Model-Based Sliced Inverse Regression

## Description

Nonparametric estimation of mean function with variability bands.

## Usage

 ```1 2 3 4``` ```loess.sd(x, y = NULL, nsigma = 1, ...) panel.loess(x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = "red", span = 2/3, degree = 2, nsigma = 1, ...) ```

## Arguments

 `x` a vector of values for the predictor variable x. `y` a vector of values for the response variable y. `nsigma` a multiplier for the standard deviation function. `col, bg, pch, cex` numeric or character codes for the color(s), point type and size of points; see also `par`. `col.smooth` color to be used by `lines` for drawing the smooths. `span` smoothing parameter for `loess`. `degree` the degree of the polynomials to be used, see `loess`. `...` further argument passed to the function `loess`.

## Value

The function `loess.sd` computes the loess smooth for the mean function and the mean plus and minus `k` times the standard deviation function.

The function `panel.loess` can be used to add to a scatterplot matrix panel a smoothing of mean function using loess with variability bands at plus and minus `nsigmas` times the standard deviation.

## Author(s)

Luca Scrucca [email protected]

## References

Weisberg, S. (2005) Applied Linear Regression, 3rd ed., Wiley, New York, pp. 275-278.

`loess`

## Examples

 ```1 2 3 4 5``` ```data(cars) plot(cars, main = "lowess.sd(cars)") lines(l <- loess.sd(cars)) lines(l\$x, l\$upper, lty=2) lines(l\$x, l\$lower, lty=2) ```

### Example output

```Loading required package: mclust
Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
Package 'msir' version 1.3.1
Type 'citation("msir")' for citing this R package in publications.
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE