nlshc | R Documentation |
Extension of nls
to the heteroscedastic case.
nlshc(nlsout,type='HC')
nlsout |
Object of type 'nls'. |
type |
Eickert-White algorithm to use. See documentation for nls. |
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'.
Estimated covariance matrix
Norm Matloff
Zeileis A (2006), Object-Oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16, https://www.jstatsoft.org/v16/i09/.
# 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)
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