Nothing
## Summarize ssanova objects
summary.ssanova <- function(object,diagnostics=FALSE,...)
{
y <- model.response(object$mf,"numeric")
w <- model.weights(object$mf)
offset <- model.offset(object$mf)
if (is.null(offset)) offset <- rep(0,length(y))
## Residuals
mf <- object$mf
if (!is.null(object$random)) mf$random <- object$random$z
res <- y - predict(object,mf)
## Fitted values
fitted <- as.numeric(y-res)
## (estimated) sigma
sigma <- sqrt(object$varht)
## R^2
if (!is.null(w)) {
r.squared <- sum(w*(fitted-sum(w*fitted)/sum(w))^2)
r.squared <- r.squared/sum(w*(y-sum(w*y)/sum(w))^2)
}
else r.squared <- var(fitted)/var(y)
## Residual sum of squares
if (is.null(w)) rss <- sum(res^2)
else rss <- sum(w*res^2)
## Penalty associated with the fit
obj.wk <- object
obj.wk$d[] <- 0
if (!is.null(model.offset(obj.wk$mf))) obj.wk$mf[,"(offset)"] <- 0
penalty <- sum(obj.wk$c*predict(obj.wk,obj.wk$mf[object$id.basis,]))
penalty <- as.vector(10^object$nlambda*penalty)
if (!is.null(object$random)) {
p.ran <- t(object$b)%*%object$random$sigma$fun(object$zeta,
object$random$sigma$env)%*%object$b
penalty <- penalty + p.ran
}
## Calculate the diagnostics
if (is.null(object$partial)) labels.p <- NULL
else labels.p <- labels(object$partial$mt)
if (diagnostics) {
## Obtain retrospective linear model
comp <- NULL
p.dec <- NULL
for (label in c(object$terms$labels,labels.p)) {
if (label=="1") next
if (label=="offset") next
comp <- cbind(comp,predict(object,object$mf,inc=label))
jk <- sum(obj.wk$c*predict(obj.wk,obj.wk$mf[object$id.basis,],inc=label))
p.dec <- c(p.dec,10^object$nlambda*jk)
}
term.label <- object$terms$labels[object$terms$labels!="1"]
term.label <- term.label[term.label!="offset"]
term.label <- c(term.label,labels.p)
if (!is.null(object$random)) {
comp <- cbind(comp,predict(object,mf,inc=NULL))
p.dec <- c(p.dec,p.ran)
term.label <- c(term.label,"random")
}
fitted.off <- fitted-offset
comp <- cbind(comp,yhat=fitted.off,y=fitted.off+res,e=res)
if (any(outer(term.label,c("yhat","y","e"),"==")))
warning("gss warning in summary.ssanova: avoid using yhat, y, or e as variable names")
colnames(comp) <- c(term.label,"yhat","y","e")
## Sweep out constant
if (!is.null(w))
comp <- sqrt(w)*comp - outer(sqrt(w),apply(w*comp,2,sum))/sum(w)
else comp <- sweep(comp,2,apply(comp,2,mean))
## Obtain pi
comp1 <- comp[,c(term.label,"yhat")]
decom <- t(comp1) %*% comp1[,"yhat"]
names(decom) <- c(term.label,"yhat")
decom <- decom[term.label]/decom["yhat"]
## Obtain kappa, norm, and cosines
corr <- t(comp)%*%comp
corr <- t(corr/sqrt(diag(corr)))/sqrt(diag(corr))
norm <- apply(comp,2,function(x){sqrt(sum(x^2))})
cosines <- rbind(corr[c("y","e"),],norm)
rownames(cosines) <- c("cos.y","cos.e","norm")
corr <- corr[term.label,term.label,drop=FALSE]
if (qr(corr)$rank<dim(corr)[2])
kappa <- rep(Inf,len=dim(corr)[2])
else kappa <- as.numeric(sqrt(diag(solve(corr))))
## Obtain decomposition of penalty
rough <- p.dec / penalty
names(kappa) <- names(rough) <- term.label
}
else decom <- kappa <- cosines <- rough <- NULL
## Return the summaries
z <- list(call=object$call,method=object$method,fitted=fitted,residuals=res,
sigma=sigma,r.squared=r.squared,rss=rss,penalty=penalty,
pi=decom,kappa=kappa,cosines=cosines,roughness=rough)
class(z) <- "summary.ssanova"
z
}
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