Nothing
summary.lognlm <-
function(object, ...){
n<-length(object$y)
coef.p <- object$coefficients
p<-length(coef.p)
V<-vcov(object, se=FALSE, ...)
s.err <- sqrt(diag(V[1:p,1:p]))
tvalue <- coef.p/s.err
dn <- c("Estimate", "Std. Error")
df.r<- n-p-1
pvalue <- 2 * pt(-abs(tvalue), df.r) #pnorm
coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
dimnames(coef.table) <- list(names(coef.p), c(dn, "t value", "Pr(>|t|)"))
# ans<-list(coefficients = coef.table, sigma=sqrt(object$s2), cov=V, call=object$call, loglik=object$loglik)
keep <- match(c("call", "terms", "contrasts", "df.residual", "iter", "na.action"), names(object), 0L)
ans <- c(object[keep], list(coefficients = coef.table, sigma=sqrt(object$s2), cov=V, loglik=object$loglik), lik=object$lik)
# ans <- c(object[keep], list(deviance.resid = residuals(object,
# type = "deviance"), coefficients = coef.table, aliased = aliased,
# dispersion = dispersion, df = c(object$rank, df.r, df.f),
# cov.unscaled = covmat.unscaled, cov.scaled = covmat))
#R2
mu<-object$fitted
y<-object$y
if(is.null(object$weights)) {
r2<-1- sum((log(y)-log(mu))^2)/sum((log(y)- mean(log(y)) )^2)
} else {
w<- object$weights
r2<-1- sum(w*(log(y)-log(mu))^2)/sum(w*(log(y)- weighted.mean(log(y),w))^2)
}
ans$r.squared <- r2
if("(Intercept)" %in% names(coef.p)) {
df.num <- n-1
} else {
df.num <- n
}
ans$adj.r.squared <- 1-(1-r2)*df.num/(n-p) #controlla intercetta
class(ans) <- "summary.lognlm"
return(ans)
}
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