Thank you, Uwe, for your help! I have more measurements (m1, m2) and
more parameters (par1, par2). I can calculate the means of m1 and m2
this way:
aggregate(cbind(m1, m2) ~ par1 + par2, dat, mean)
However, I also need to calculate the standard error of the mean, and
the variance for the sample, and I would like to have them output as
extra columns next to the column with means.
Again, I would appreciate any help!
On 17.01.2012 15:09, Uwe Ligges wrote:
On 17.01.2012 12:31, Irek Szczesniak wrote:
Hi,
I have the simulation results of the following structure:
run par measured
1 10 12
2 10 14
1 20 20
2 20 26
Where "run" is the simulation run number, "par" is the parameter of
the simulation, and "measured" is the value measured in the
simulation. This is only a simple example of my results. There are
many values measured and many parameters. But the basic structure
stays the same: there are many runs (identified by the run number) for
the same values of the parameters with various measured values -- they
constitute a sample.
I would like to calculate the mean of the "measured" value for a
sample, and so I would like to obtain the output as follows:
par mean
10 13
20 23
I would appreciate it if someone could write me how to do it.
For you data in a data.frame called dat:
aggregate(measured ~ par, dat, mean)
Uwe Ligges
Thank you,
Irek
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--
Ireneusz (Irek) Szczesniak
http://www.irkos.org
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
