xyplot() or splom()?: two factors from same data frame

Hi Rich

Without  a dataset I am not sure what you need.

Further down the track you will need to plot what you finally need 
and this may help you decide what you need now

for finals try

library(lattice)
library(latticeExtra)

useOuterStrips(
xyplot(variable1 + variable2 ~ Date|Site, data = Data, groups = Groups, ...)
)

this will give you a matrix plot of the columns being the site and 
the rows being the variables.

but for now if you have a large screen you can do a splom by sites 
you may to subset sites.
use par.settings and levelplot settings to reduce the fontsizes etc 
so that you can get as much information

splom( ...|Site, data= Data,...)

if you want to have 2 plots eg  the x axis as time and another 
independent variable
you can plot them on the page together

eg

plot1 <- xyplot(....)
plot2 <- density.plot(...)

print(p1, position= c(0,0,1,0.5), more = T)
print(p1, position= c(0,0.5,1,1), more = F)

see ?print.trellis

As usual everything depends on the data

HTH

Duncan

Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2351
Email Home: mackay at northnet.com.au

  I'm not seeing how to plot the quantities associated with two values of a
factor by reading the ?xyplot help page and Deepayan's book. Perhaps I need
to generate many different subsets from the data frame, but that would
require _many_ new data frames.

  The structure of the data frame (for a single stream) has sites (factor),
a date (as.Date), parameters (factor), and quantities (numeric). What I need
to do is produce scatter plots of the quantities associated with two
different parameters conditioned by site or by date (separate plots, I'm
sure).

  I have 24 streams and initially need to look at the relationships of 5
different pairs of parameters (e.g., "Cond" and "TDS") for all sites and by
sites. Can I do this from the existing data frame?

  It seems to me that a splom() could be ideal for this, but I still have
the question of how to specify quantities for two parameters to plot against
each other.

Rich

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Without a dataset I am not sure what you need.

Duncan,

   Part of the problems I'm trying to resolve come from changing priorities
from my client and the regulators. I end up stopping one process and
starting on a different one. But, that's life in the real world of
environmental consulting. :-)

   What I need now is to compare TDS (total dissolved solids) with specific
conductivity and the ions that are normally comprise TDS. Before running any
regression models I need to look at these data from three points of view:
all data from all sites collected during the past 30 years; average (or
total) concentrations (not yet decided on what makes the most ecological
sense) within a stream having multiple collection sites; and by site within
certain streams.

   I think that I need to subset the data frame to create distinct analytical
data frames for each comparison, then rm() them until needed again (or I'd
have a very large number of files in the directory). If I have a subset, for
example, of TDS and conductivity regardless of sample date or location I
will have two columns of numbers that will fit the xyplot() formula; e.g.,
xyplot(TDS ~ Cond). This is the broad picture. I can then use the
hydrographic basins (2 of 'em) or streams (24 of 'em) as factors to
condition the analysis. Repeat for other parameter pairs (TDS vs. Ca, TDS
vs, Mg, etc.).

   Another part of the issue, perhaps, is that the data are in a single data
frame:

  str(chemdata)
'data.frame':	47244 obs. of  6 variables:
  $ site    : Factor w/ 143 levels "BC-0.5","BC-1",..: 134 134 134 127 127
  $ sampdate: Date, format: "2006-12-06" "2006-12-06" ...
  $ param   : Factor w/ 66 levels "AGP","ANP","ANP/AGP",..: 58 66 12 24 59 66
  $ quant   : num  1.08e+04 7.95 1.80e-02 2.80e+02 1.90e+01 8.44 1.62e+03
  $ stream  : Factor w/ 24 levels "BCrk","CCrk",..: 4 4 4 21 21 21 4
  $ basin   : Factor w/ 2 levels "BasinEast","BasinWest": 1 1 1 1 1 1 1 1 1 2 ...

while all the data sets used in the books I've read are simpler. What I've
not read is guidance on how complex data sets could (or should) be
partitioned into smaller but still related data sets to facilitate analyses.

   I hope this clarifies my initial request.

Rich