general.multiple.probplot.sim <-
function (paper.distribution = "normal", data.distribution = "normal",
censor.distribution = NULL, data.parameters = c(0, 1), censor.parameters = c(0,
1), sample.size.vec = c(10, 20, 40), number.simulations = 5,
my.title = NULL, one.size = F, SMRD.point.size = 20, censoring.max = NULL,
ml = T, ...)
{
if (one.size) {
old.par <- par(mfrow = c(sqrt(number.simulations), sqrt(number.simulations)),
oma = c(0, 0, 4, 0), err = -1)
}
else {
old.par <- par(mfrow = c(length(sample.size.vec), number.simulations),
oma = c(0, 0, 4, 0), err = -1)
}
oldOptions <- SMRDOptions(SMRD.point.size = SMRD.point.size)
on.exit({
par(old.par)
par(new = F)
SMRDOptions(oldOptions)
})
mid.plot <- floor(number.simulations/2 + 1)
for (j in 1:length(sample.size.vec)) for (i in 1:number.simulations) {
if (i == mid.plot && !one.size) {
print.samp.size <- T
}
else {
print.samp.size <- F
}
xlim <- data.parameters[1] + c(quant(0.001, data.distribution),
quant(0.999, data.distribution)) * data.parameters[2]
if (is.logdist(data.distribution))
xlim <- exp(xlim)
dist <- general.probplot.sim(paper.distribution = paper.distribution,
data.distribution = data.distribution, censor.distribution = censor.distribution,
data.parameters = data.parameters, censor.parameters = censor.parameters,
sample.size = sample.size.vec[j], print.samp.size = print.samp.size,
cex = 0.7, xlim = xlim, ylim = c(NA, NA),
censoring.max = censoring.max, ml = ml, ...)
}
if (is.null(my.title))
my.title <- paste(paper.distribution, "Probability Plots with Simulated ",
data.distribution, "Data")
mtext(side = 3, line = 0, cex = 1.5, outer = TRUE, my.title)
}
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