Thank you so much for your previous help with zoo and lattice.  Following
your guidance, I've found the zoo package very powerful.  The rollapply
demos were also illuminating!

I'm wondering if I've found some inconsistency with zoo documentation r.e.
NA handling.
Both na.omit and na.contiguous are mentioned as methods in the current CRAN
pdf reference documentation.  The hyperlink for na.omit on p18 lead to p1,
and there are no function defs for either in the docs.  Similarly, both
?na.omit and ?na.contiguous lead to function defs.  Are these unimplemented
as generic functions?  I'm a bit confused as to the proper context in which
to use na.omit and na.contiguous.

best,
christian

On 7/8/07, Gabor Grothendieck <ggrothendieck@gmail.com> wrote:
>
> Try this (and see ?xyplot.zoo for more info):
>
> # first run code in your post that creates the experiment object
>
> # create a time series from experiment
> z <- zoo(matrix(experiment$meas, 25, byrow = TRUE))
> colnames(z) <- c("NC", "NT", "SC", "ST")
>
> # add grid to existing plot
> addGrid <- function() for(i in 1:2) {
>   trellis.focus("panel", i, 1); panel.grid(); trellis.unfocus()
> }
>
> zz <- z
> colnames(zz) <- c("N", "NT", "S", "ST")
> xyplot(zz, screens = c(1, 1, 2, 2), col = 1:2, layout = 2:1, type = "b")
> addGrid()
>
> zz <- z
> colnames(zz) <- c("C", "T", "SC", "ST")
> xyplot(zz, screens = 1:2, col = c(1, 1, 2, 2), layout = 2:1, type = "b")
> addGrid()
>
>
> On 7/8/07, icosa atropa <icos.atropa@gmail.com> wrote:
> > > Please provide something reproducible and
> > > show what you want to do without zoo.
> >
> > The following example is contrived, but closely resembles my data.
> >
> > I pull my data from a database using RODBC - it comes in as a data.frame
> > with factors, timestamp, and data all globbed together.  This is very
> > convenient for plotting with lattice, but complicates time series
> analysis.
> > I can't turn the object into a zoo directly due to the mixed
> factor/data.
> > I've looked at fCalendar, whose timeSeries object has an recordtID
> slot...
> >
> >
> > # Everything from here down tested with R 2.5.0
> >
> > experiment.layout = expand.grid( tx=c('control', 'treatment'), rep=c(
> 'N',
> > 'S'), time=1:25)
> > # ^^ block design experiment
> >
> > experiment = data.frame( experiment.layout, meas=rnorm(100) )
> > # ^^the time-indexed dataframe of observations by
> >
> > experiment$meas=experiment$meas + c(1,-1,2,-2)
> > # ^^experimental effects differ by bloco and treatment
> >
> > summary(experiment)
> >
> > require(lattice)
> > #
> > #First split condition on rep, grouping by tx
> > #
> > xyplot(meas ~ time | rep, data=experiment, groups=tx,
> >                 panel=function(x, y, subscripts, groups) {
> >                         panel.grid()
> >                         panel.superpose(x, y, subscripts, groups)
> >                  }
> > )
> >
> > #
> > #Next condition on tx, grouping by experiment
> > #
> > xyplot(meas ~ time | tx, data=experiment, groups=rep,
> >                 panel=function(x, y, subscripts, groups) {
> >                         panel.grid()
> >                         panel.superpose(x, y, subscripts, groups)
> >                  }
> > )
> >
> > thanks again for your time!
> > christian
> >
>



-- 
Far better an approximate answer to the right question, which is often
vague, than the exact answer to the wrong question, which can always be made
precise -- j.w. tukey

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