Comparison across two groups of the error standard deviation in nonparametric regression with two covariates.
This function compares across two groups, in a hypothesis test, the error standard deviation in nonparametric regression with two covariates.
sm.sigma2.compare(x1, y1, x2, y2)
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. |
see the reference below.
a p-value for the test of equality of standard deviations.
none.
Bock, M., Bowman, A.W.\ \& Ismail, B. (2007). Estimation and inference for error variance in bivariate nonparametric regression. Statistics \& Computing, to appear.
## 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]))
})
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