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#' @export
summary.geese <- function(object, ...) {
mean.sum <- data.frame(estimate = object$beta,
# nai.se = sqrt(diag(object$vbeta.naiv)),
san.se = sqrt(diag(object$vbeta)),
ajs.se = sqrt(diag(object$vbeta.ajs)),
j1s.se = sqrt(diag(object$vbeta.j1s)),
fij.se = sqrt(diag(object$vbeta.fij)))
mean.sum$wald <- (mean.sum$estimate / mean.sum$san.se)^2
mean.sum$p <- 1 - pchisq(mean.sum$wald, df=1)
rownames(mean.sum) <- object$xnames
corr.sum <- data.frame(estimate = object$alpha,
# nai.se = sqrt(diag(object$valpha.naiv)),
san.se = sqrt(diag(object$valpha)),
ajs.se = sqrt(diag(object$valpha.ajs)),
j1s.se = sqrt(diag(object$valpha.j1s)),
fij.se = sqrt(diag(object$valpha.fij)))
corr.sum$wald <- (corr.sum$estimate / corr.sum$san.se)^2
corr.sum$p <- 1 - pchisq(corr.sum$wald, df=1)
if (nrow(corr.sum) > 0) rownames(corr.sum) <- object$zcor.names
scale.sum <- data.frame(estimate = object$gamma,
san.se = sqrt(diag(object$vgamma)),
ajs.se = sqrt(diag(object$vgamma.ajs)),
j1s.se = sqrt(diag(object$vgamma.j1s)),
fij.se = sqrt(diag(object$vgamma.fij)))
scale.sum$wald <- (scale.sum$estimate / scale.sum$san.se)^2
scale.sum$p <- 1 - pchisq(scale.sum$wald, df=1)
if (!is.null(object$zsca.names)) rownames(scale.sum) <- object$zsca.names
drop <- ifelse(c(object$control$jack, object$control$j1s, object$control$fij)== 0, TRUE, FALSE)
if (any(drop)) {
drop <- (3:5)[drop]
mean.sum <- mean.sum[,-drop]
corr.sum <- corr.sum[,-drop]
scale.sum <- scale.sum[,-drop]
}
ans <- list(mean=mean.sum, correlation=corr.sum, scale=scale.sum,
call=object$call, model=object$model, control=object$control,
error=object$err, clusz=object$clusz)
class(ans) <- "summary.geese"
ans
}
#' @export
print.geese <- function(x, digits = NULL, quote = FALSE, prefix = "", ...) {
if(is.null(digits)) digits <- options()$digits
else options(digits = digits)
cat("\nCall:\n")
dput(x$call)
cat("\nMean Model:\n")
cat(" Mean Link: ", x$model$mean.link, "\n")
cat(" Variance to Mean Relation:", x$model$variance, "\n")
cat("\n Coefficients:\n")
print(unclass(x$beta), digits = digits)
if (!x$model$scale.fix) {
cat("\nScale Model:\n")
cat(" Scale Link: ", x$model$sca.link, "\n")
cat("\n Estimated Scale Parameters:\n")
print(unclass(x$gamma), digits = digits)
}
else cat("\nScale is fixed.\n")
cat("\nCorrelation Model:\n")
cat(" Correlation Structure: ", x$model$corstr, "\n")
if (pmatch(x$model$corstr, c("independence", "fixed"), 0) == 0) {
cat(" Correlation Link: ", x$model$cor.link, "\n")
cat("\n Estimated Correlation Parameters:\n")
print(unclass(x$alpha), digits = digits)
}
##cat("\nNumber of observations : ", x$nobs, "\n")
##cat("\nMaximum cluster size : ", x$max.id, "\n")
cat("\nReturned Error Value: ")
cat(x$error, "\n")
cat("Number of clusters: ", length(x$clusz), " Maximum cluster size:", max(x$clusz), "\n\n")
invisible(x)
}
#' @export
print.summary.geese <- function(x, digits = NULL,
quote = FALSE, prefix = "", ... ) {
if(is.null(digits)) digits <- options()$digits
else options(digits = digits)
cat("\nCall:\n")
dput(x$call)
cat("\nMean Model:\n")
cat(" Mean Link: ", x$model$mean.link, "\n")
cat(" Variance to Mean Relation:", x$model$variance, "\n")
cat("\n Coefficients:\n")
print(x$mean, digits = digits)
if (x$model$scale.fix == FALSE) {
cat("\nScale Model:\n")
cat(" Scale Link: ", x$model$sca.link, "\n")
cat("\n Estimated Scale Parameters:\n")
print(x$scale, digits = digits)
}
else cat("\nScale is fixed.\n")
cat("\nCorrelation Model:\n")
cat(" Correlation Structure: ", x$model$corstr, "\n")
if (pmatch(x$model$corstr, c("independence", "fixed"), 0) == 0) {
cat(" Correlation Link: ", x$model$cor.link, "\n")
cat("\n Estimated Correlation Parameters:\n")
print(x$corr, digits = digits)
}
##cat("\nNumber of observations : ", x$nobs, "\n")
##cat("\nMaximum cluster size : ", x$max.id, "\n")
cat("\nReturned Error Value: ")
cat(x$error, "\n")
cat("Number of clusters: ", length(x$clusz), " Maximum cluster size:", max(x$clusz), "\n\n")
invisible(x)
}
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