Greg Snow wrote:
I don't know of a single package that is comparable to PASS, but the R system
itself is the most comprehensive tool available for power and sample size
computations.
For the simple cases you already found the pwr package, there are also some
power functions in the stats package and in some other packages and these will
be comparable to the equivalent (or possibly better) than the simple ones in
PASS.
When things get a bit more complicated then there are a few different options
for what to do next:
1. Don't provide anything for the more complicated cases.
2. Provide a minimal set of routines for more complicated cases based on
programmer assumptions rather than information from someone familiar with the
source of the data (assumptions often hidden).
3. Provide many different routines encompassing every alternative set of
assumptions that the programmer can think of forcing the user to sort through
all the options to find the one that is closest (and maybe the same) as what
they want to do.
4 Provide a full programming language so that the people familiar with the
question(s) of interest and the source of the data can explicitly spell out the
desired analysis and assumptions.
5. possible others, but I can't think of any.
It looks like PASS uses option 3, giving many different routines that any one
user in only likely to use a few of.
R is option 4. You can decide what assumptions you want to make about the data
(and later change any of those assumptions), decide how you plan to analyze the
data, then by simulation you can work out the power/sample size/etc. knowing
exactly what assumptions went into the analysis.
As one example of what Greg is talking about see
http://bm2.genes.nig.ac.jp/RGM2/R_current/library/Hmisc/man/spower.html
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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