Dave,
qqmath(~val|ind,data=xx
,distribution=function(p) qt(p,df=19)
,ylab="Sample Quatinles"
,xlab="Theoretical Quantiles"
,aspect=1
,prepanel = prepanel.qqmathline
,panel=function(x,y)
{
panel.qqmathline(y, distribution=function(p) qt(p,df=19),col=2)
panel.qqmath(x, y , distribution=function(p)
qt(p,df=19),pch=".",cex=2)
}
)
Adding f.value=fn as argument to qqmath reduces the size of the image,
but neither the axis (absicissae) nor the line added by panel.qqmathline
are right.
Adding f.value=fn as argument to panel.qqmathline and panel.qqmath
generates the right graphic, but the size of the image is again 20 MB.
Any Suggestions?
Eryk
[EMAIL PROTECTED] wrote:
nwew <[EMAIL PROTECTED]> wrote:
Dear R helpers,
I generate a qq plot using the following function call.
...
dim(xx)
[1] 680237 2
How about doing something like this:
fn <- function(n,cut=0.001,m=1000)
{
p <- ppoints(n)
p <- p[pmin(p, 1-p) < cut]
q <- pt(seq(qt(cut,df=19),qt(1-cut,df=19),length=m),df=19)
sort(c(p,q))
}
then adding 'f.value=fn' to your qqmath arguments? This essentially
says, plot the individual data points in the extreme tails of the
distribution (p < 0.001 or p > 0.999), and evaluate the distribution
at a sparse set of points in between, where the density means you
can't discern the individual values anyway.
-- Dave
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