uncertMC
or calls
to uncert
with method=MC
.
"plot"(x, which = 1:2, main=paste("Monte Carlo evaluation -",deparse(substitute(x))), ask = prod(par("mfcol")) < length(which) && dev.interactive(), caption = list("Histogram", "Q-Q plot", "Density", "Correlation x-y", "Covariance x-y"), xlab = paste(deparse(substitute(x)), "$y", sep = ""), ..., cex.caption = 1, cex.main = 1.25, lwd.y = 2, col.y = 2, lty.y, col.qqline = NULL, lty.qqline = NULL, lwd.qqline = NULL)
uncertMC
produced by uncertMC()
or
uncert()
with method="MC"
.
which
.cex
in par
.cex.main
in par
.
plot.uncertMC
invisibly returns NULL
.
which=1
x$MC$y
,
with optional line for x$MC$y
. The histogram is produced using
hist.default
which=2
x$MC$y
,
with Q-Q line. The plot uses qqnorm.default
. If datax
is not
present (in sQuote...), it is set to TRUE
.
which=3
x$MC$y
.
The plot calls density.default
to calculate the density and
plot.density
to produce the plot.
which=4
x$y
is present. Any correlation method supported by stats::cor
may
be included in ... (e.g as method="pearson"
.
which=5
x$y
is present. Any correlation method supported by stats::cov
may
be included in ... (e.g as method="pearson"
.Values outside 1:5 are silently ignored.
Parameters in ... are passed to the various plot methods or calculations called. Only those parameters relevant to a given plot are passed to each calculation or plotting function, so ... can include any parameter accepted by any of the functions called.
For the x-y correlation and x-y covariance plot, values in x$cor.xy
are
used if available. If not, stats::cor
or stats::cov
is called on values
in x$MC$y
and x$MC$x
if the latter is available
(i.e. uncertMC
was called with keep.x=TRUE
). If neither
x$cor.xy
nor x$MC$x
is present, or if method
is
unknown, the plot is skipped with a warning.
uncertMC-class
, hist
,
qqnorm
, qqline
,
density
, plot.density
expr <- expression(a/(b-c))
x <- list(a=1, b=3, c=2)
u <- lapply(x, function(x) x/20)
set.seed(403)
u.invexpr<-uncertMC(expr, x, u, distrib=rep("norm", 3), B=999, keep.x=TRUE )
par(mfrow=c(2,2))
plot(u.invexpr, which=1:4, pch=20, method="k")
# method="k" gives Kendall correlation
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