Thanks Andrew, but what you gave me can actually be done simpler like
my_agg$vol.diff <- diff(my_agg$Vol)
or
my_agg$vol.diff <-c(NA, diff(my_agg$Vol)) # for a list with the same
#length as the aggregated mean list
However, what I need is to be able to have this done by groups and then
have a table as an output.
For example if I use by():
my_agg$vol <- with(my_agg, by(Vol, list(Block, Treatment), diff))
but then I am stucked with a by list that can't be coerced and the problem
becomes how to coerce the by list that has a vector for each group?
Cristian Montes
NC State University
> Hi Cristian,
>
> instead of aggregate, how about something like:
>
>
> n <- dim(my_agg)[1]
> my_agg$vol.diff <- my_agg$Vol - c(NA, my_agg$Vol[1:(n-1)]
> my_agg <- my.agg[my.agg$Age > min(my.agg$Age),]
>
>
> (assumes same minimum age for all treatments)
>
> (not checked)
>
> Cheers,
>
> Andrew
>
> On Thu, Nov 01, 2007 at 12:09:34PM -0400, [EMAIL PROTECTED] wrote:
>> Hi everyone
>>
>> I am trying to summarize a table with yield estimates of a forest
>> plantation. For that I have four blocks and four treatments measured
>> over
>> a period of 10 years (every year). In each plot trees are measured
>> (diameters and heights).
>>
>> With aggregate function I can calculate the average diameter or the
>> total
>> volume for each plot at any time with something like this:
>>
>> my_agg <- aggregate(list(Ht = HTO, Dbh = DBH), list(Age = AGE, Block =
>> BLOCK, Treat = TREAT), sum)
>>
>> my_agg <- aggregate(list(VOL = VOLUME), list(Age = AGE, Block = BLOCK,
>> Treat = TREAT), sum)
>>
>> where HTO is the height of the tree
>> Ht average height of trees in each plot at time t
>> DBH is the diameter at 1.3 meter height
>> Dbh is the average diameter of the plot at time t
>> VOL is the [i]th tree volume
>> Vol is the volume of the plot at time t
>>
>> To do actual growth analysis I need to calculate the difference in
>> volume
>> between t and (t-1) for every block/treatment.
>>
>> Now the question
>>
>> How can I use aggregate to calculate the difference of each time series
>> and have an output like aggregate does? I know it is imposible to use
>> diff() within aggregate because it gives me an scalar.
>> In SAS proc means handles such a thing, and diff() in R is fine for a
>> single series, but in this case I want to calculate for all the
>> treatments/blocks at the same time.
>>
>> I can do the whole using by and some more less elegant procedures, but I
>> figure there should be a cleaner way as in PROC MEANS.
>>
>> Any suggestions?
>>
>> Cristian Montes
>> NC State University
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> --
> Andrew Robinson
> Department of Mathematics and Statistics Tel: +61-3-8344-9763
> University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
> http://www.ms.unimelb.edu.au/~andrewpr
> http://blogs.mbs.edu/fishing-in-the-bay/
>
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