[R] Problem with memory footprint of qq plot generated with lattice

[Witold Eryk Wolski]

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

dhinds at sonic.net wrote:
> nwew <W.E.Wolski at newcastle.ac.uk> 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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