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# QQ-plot for generalised inverse gaussian distribution
qqgig <- function(y, Theta, main = "GIG Q-Q Plot",
xlab = "Theoretical Quantiles",
ylab = "Sample Quantiles",
plot.it = TRUE, line = TRUE, ...){
if (has.na <- any(ina <- is.na(y))) {
yN <- y
y <- y[!ina]
}
if (0 == (n <- length(y)))
stop("y is empty or has only NAs")
x <- qgig(ppoints(n), Theta)[order(order(y))]
if (has.na) {
y <- x
x <- yN
x[!ina] <- y
y <- yN
}
if(plot.it)
plot(x, y, main = main, xlab = xlab, ylab = ylab, ...)
title(sub=paste("Theta = (",
round(Theta[1], 3), "," , round(Theta[2], 3), ",",
round(Theta[3], 3), ")", sep = ""))
if(line) abline(0,1)
invisible(list(x = x, y = y))
} ## End of qqgig()
### PP-plot for generalised inverse gaussian distribution
ppgig <- function(y, Theta, main = "GIG P-P Plot",
xlab = "Uniform Quantiles",
ylab = "Probability-integral-transformed Data",
plot.it = TRUE, line = TRUE, ...){
if (has.na <- any(ina <- is.na(y))) {
yN <- y
y <- y[!ina]
}
if(0 == (n <- length(y)))
stop("data is empty")
yvals <- pgig(y, Theta)
xvals <- ppoints(n, a = 1/2)[order(order(y))]
if (has.na) {
y <- yvals
x <- xvals
yvals <- yN
yvals[!ina] <- y
xvals <- yN
xvals[!ina] <- x
}
if (plot.it)
plot(xvals, yvals, main = main, xlab = xlab, ylab = ylab,
ylim = c(0,1), xlim = c(0,1), ...)
title(sub=paste("Theta = (",
round(Theta[1], 3), ",", round(Theta[2], 3), ",",
round(Theta[3], 3), ")", sep = ""))
if (line) abline(0,1)
invisible(list(x = xvals, y = yvals))
} ## End of ppgig()
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