# sm.sigma2.compare: Comparison across two groups of the error standard deviation... In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

## Description

This function compares across two groups, in a hypothesis test, the error standard deviation in nonparametric regression with two covariates.

## Usage

 `1` ```sm.sigma2.compare(x1, y1, x2, y2) ```

## Arguments

 `x1` a two-column matrix of covariate values for group 1. `y1` a vector of responses for group 1. `x2` a two-column matrix of covariate values for group 2. `y2` a vector of responses for group 2.

## Details

see the reference below.

## Value

a p-value for the test of equality of standard deviations.

none.

## References

Bock, M., Bowman, A.W.\ \& Ismail, B. (2007). Estimation and inference for error variance in bivariate nonparametric regression. Statistics \& Computing, to appear.

`sm.sigma`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## Not run: with(airquality, { x <- cbind(Wind, Temp) y <- Ozone^(1/3) group <- (Solar.R < 200) sig1 <- sm.sigma(x[ group, ], y[ group], ci = TRUE) sig2 <- sm.sigma(x[!group, ], y[!group], ci = TRUE) print(c(sig1\$estimate, sig1\$ci)) print(c(sig2\$estimate, sig2\$ci)) print(sm.sigma(x[ group, ], y[ group], model = "constant", h = c(3, 5))\$p) print(sm.sigma(x[!group, ], y[!group], model = "constant", h = c(3, 5))\$p) print(sm.sigma2.compare(x[group, ], y[group], x[!group, ], y[!group])) }) ## End(Not run) ```