Nothing
#* +-------------------------------------------------------------------------------+ *
#* | parInfer: Extend cllasical linear regression parameter inference | *
#* | To distinguish it with generic 'parInfer' of nl.fitt, arguments changed. | *
#* | Input: x, design in nonlinear case is gradient. | *
#* | Input: theta, without sigma. | *
#* | sigma: estimated sigma. | *
#* +-------------------------------------------------------------------------------+ *
pInf <- function(object,confidence=.95){
.expr1 <- attr(object@predictor,"gradient") ## F
.expr2 <- t(.expr1) %*% .expr1 ## F' F
gtginv <- indifinv( .expr2,F )
if(is.Fault(gtginv)) gtginv<-ginv(.expr2)
covmatrix <- object@parameters[["sigma"]] ^ 2 * gtginv ## sgm2 * (F'F)^-1
v2inv <- diag(1/sqrt(diag(covmatrix)))
corrmat <- v2inv %*% covmatrix %*% v2inv
parstdev = sqrt(diag(covmatrix ))
n <- nrow(.expr1)
p <- object$form$p
.expr4 <- sqrt((p+1)*qf(confidence,p+1,n-p-1))
.expr5 <- parstdev * .expr4
cilow <- unlist(object$parameters[names(object$form$par)]) - .expr5
ciupp <- unlist(object$parameters[names(object$form$par)]) + .expr5
result <- list(covmat=covmatrix ,corrmat = corrmat,parstdev=parstdev,CI=cbind(cilow,ciupp))
return(result)
}
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