nlshc {regtools} | R Documentation |
Heteroscedastic Nonlinear Regression
Description
Extension of nls
to the heteroscedastic case.
Usage
nlshc(nlsout,type='HC')
Arguments
nlsout |
Object of type 'nls'. |
type |
Eickert-White algorithm to use. See documentation for nls. |
Details
Calls nls
but then forms a different estimated covariance
matrix for the estimated regression coefficients, applying the
Eickert-White technique to handle heteroscedasticity. This then
gives valid statistical inference in that setting.
Some users may prefer to use nlsLM
of the package
minpack.lm instead of nls
. This is fine, as both
functions return objects of class 'nls'.
Value
Estimated covariance matrix
Author(s)
Norm Matloff
References
Zeileis A (2006), Object-Oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16, https://www.jstatsoft.org/v16/i09/.
Examples
# simulate data from a setting in which mean Y is
# 1 / (b1 * X1 + b2 * X2)
n <- 250
b <- 1:2
x <- matrix(rexp(2*n),ncol=2)
meany <- 1 / (x %*% b) # reg ftn
y <- meany + (runif(n) - 0.5) * meany # heterosced epsilon
xy <- cbind(x,y)
xy <- data.frame(xy)
# see nls() docs
nlout <- nls(X3 ~ 1 / (b1*X1+b2*X2),
data=xy,start=list(b1 = 1,b2=1))
nlshc(nlout)
[Package regtools version 1.7.0 Index]