Nothing
## S3 method
predict9 <- function (object,...) UseMethod("predict9")
## Calculate prediction and Bayesian SE from ssanova objects
predict9.gssanova <- function(object,newdata,ci=FALSE,level=.95,nu=NULL,...)
{
est <- predict(object,newdata,se.fit=ci)
if (object$family=="binomial") {
if (!ci) return(plogis(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,-1,1))
z <- plogis(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family%in%c("poisson","Gamma","inverse.gaussian")) {
if (!ci) return(exp(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,-1,1))
z <- exp(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family=="nbinomial") {
if (!is.vector(model.response(object$mf))) {
if (is.null(nu))
stop("gss error: nu is missing for nbinomial family with 2 column response" )
}
else nu <- object$nu
if (!ci) return(nu/exp(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,1,-1))
z <- nu/exp(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family=="weibull") {
gg <- gamma(1+1/object$nu)
if (!ci) return(gg*exp(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,-1,1))
z <- gg*exp(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family=="lognorm") {
gg <- exp(1/2/object$nu^2)
if (!ci) return(gg*exp(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,-1,1))
z <- gg*exp(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family=="loglogis") {
gg <- pi/object$nu/sin(pi/object$nu)
if (!ci) return(gg*exp(est))
else {
z <- est$fit+outer(est$se.fit*qnorm(1-(1-level)/2),c(0,-1,1))
z <- gg*exp(z)
return(list(fit=z[,1],lcl=z[,2],ucl=z[,3]))
}
}
if (object$family=="polr") {
P <- plogis(outer(est,c(0,cumsum(object$nu)),"+"))
z <- P[,1]
J <- dim(P)[2]
for (i in 2:J) z <- cbind(z,P[,i]-P[,i-1])
z <- cbind(z,1-P[,J])
colnames(z) <- NULL
return(z)
}
}
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