On Tuesday 20 November 2007, Deepayan Sarkar wrote:
> On 11/20/07, Dylan Beaudette <[EMAIL PROTECTED]> wrote:
>
> [...]
>
> > Example:
> > library(lattice)
> >
> > # generate some data:
> > resp <- rnorm(100)
> > pred <- resp*1.5 + rnorm(100)
> > d <- data.frame(resp=resp, pred=pred)
> >
> > # add a grouping factor:
> > d$grp <- gl(4, 25, labels=letters[1:4])
> >
> > # plot: looks ok!
> > main.plot <- xyplot(resp ~ pred | grp, data=d, panel=function(x,y, ...)
> > {panel.xyplot(x, y, ...) ; panel.lmline(x,y, ...) })
> >
> > # however, we have some other information which needs to go in each panel
> > # note that the dimensions (i.e. no of obs) are not the same as the
> > original # data, and the values are different, but on the same scale
> > resp.other <- rnorm(20)
> > pred.other <- resp.other*1.5 + rnorm(20)
> > d.other <- data.frame(resp=resp.other, pred=pred.other)
> > d.other$grp <- gl(4, 5, labels=letters[1:4])
> >
> >
> > The big question:
> > Now that we have the main plot (main.plot) looking ok, how can we add the
> > data from d.other to the frames of main.plot without using subset() for
> > each level of our grouping variable?
Hi,
> I would say you are asking the wrong question. The right question is:
> how do I manipulate my data so that it becomes easy to work with. Try
>
> combined <- make.groups(d, d.other)
excellent! this makes complete sense...
> xyplot(resp ~ pred | grp, data=combined, groups = which,
> panel = panel.superpose,
> panel.groups = function(x,y, ...) {
> panel.xyplot(x, y, ...)
> panel.lmline(x,y, ...)
> })
>
> Is that close to what you want? Does it extend to your real example?
>
> -Deepayan
This is close for the contrived example, but not quite what I need. I would
like to plot the first 'group' of data as lines, and the second as points. I
have tried specifying type=c('l', 'p') but this does not appear to work.
If I plot everything as point, the data plot correctly, but not in the symbols
that I would like.
Cheers,
Dylan
> > # after some messing around, how about this:
> > xyplot(resp ~ pred | grp, data=d, panel=function(x,y, ...)
> > {
> > panel.xyplot(x, y, ...)
> > panel.lmline(x,y, ...)
> > # now the other data:
> > panel.superpose(d.other$pred, d.other$resp, groups=d.other$grp,
> > col=c('red', 'blue', 'green', 'orange')[as.numeric(d.other$grp)], pch=16,
> > subscripts=TRUE)
> > }
> > )
> >
> > #... hmm it doesn't look like the information from 'd.other' is being
> > stratified into the panels setup in panel.xyplot() ...
> >
> > The main point to this rather contrived example is this :
> >
> > 1. i have a data frame with continuous predictions, the response, and the
> > grouping variable -- which plot nicely with the default use of xyplot()
> >
> > 2. I would like to embellish each panel with the original response and
> > predictor values to illustrate the relationship between the original data
> > and the smooth, fitted curve.
> >
> > ideas?
> >
> > thanks!
> >
> > Dylan
> >
> >
> > --
> > Dylan Beaudette
> > Soil Resource Laboratory
> > http://casoilresource.lawr.ucdavis.edu/
> > University of California at Davis
> > 530.754.7341
--
Dylan Beaudette
Soil Resource Laboratory
http://casoilresource.lawr.ucdavis.edu/
University of California at Davis
530.754.7341
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