trafoEst {distrMod} | R Documentation |
trafoEst
takes a tau like function (compare
help to trafo-methods and transforms
an existing estimator by means of this transformation
trafoEst(fct, estimator)
fct |
a tau like function, i.e., a function
in the main part theta of the parameter returning a list list(fval, mat)
where fval is the function value tau(theta)
of the transformation, and mat , its derivative matrix at
theta. |
estimator |
an object of class Estimator . |
The disadvantage of this proceeding is that the transformation is not accounted for in determining the estimate (e.g. in a corresponding optimality); it simply transforms an existing estimator, without reapplying it to data. This becomes important in optimally robust estimation.
exactly the argument estimator
, but with modified slots
estimate
, asvar
, and trafo
.
## Gaussian location and scale NS <- NormLocationScaleFamily(mean=2, sd=3) ## generate data out of this situation x <- r(distribution(NS))(30) ## want to estimate mu/sigma, sigma^2 ## -> without new trafo slot: mtrafo <- function(param){ mu <- param["mean"] sd <- param["sd"] fval <- c(mu/sd, sd^2) nfval <- c("mu/sig", "sig^2") names(fval) <- nfval mat <- matrix(c(1/sd,0,-mu/sd^2,2*sd),2,2) dimnames(mat) <- list(nfval,c("mean","sd")) return(list(fval=fval, mat=mat)) } ## Maximum likelihood estimator in the original problem res0 <- MLEstimator(x = x, ParamFamily = NS) ## transformation res <- trafoEst(mtrafo, res0) ## confidence interval confint(res)