Ken Higbee <[email protected]>:
> I created a dataset based on the information you provided. I ran
> your -anova- on my 2.4 GHz computer running Linux. It finished
> in just under a minute. I do not know what SPSS and StatView are
> doing and so cannot fully explain the differences in timing.
I need to correct my timing a bit. My PowerBook (apparently) did not feel
very well yesterday. I have run it three times this morning in 3 minutes
29-32 seconds on an iMac G4 800Mhz. That's still however, a 100 times slower
than SPSS.
> When everything is balanced there may be faster ways of getting
> to the same answer. But, Stata's -anova-, using the sweep
> operator, is able to handle designs that are not balanced
> (including having missing cells) and that may have other
> collinearities (from continuous variables included in the model).
> In those cases, the faster ways of getting to the answer may not
> hold.
Yes. That's it. Thank you Ken for making that point. SPSS and StatView only
accepts cases with complete data on all measurements. In this area Stata
outperforms the competition.
The ability to analyze unbalanced designs with missing cells is intriguing
and I can think of many situations where it could be useful. Though, special
care must be taken, when there are lot's of missing data or when the pattern
of missing data is systematic.
Given the enormous speed improvement with (presumably) the alternative way
of calculating ANOVAs, an alternative procedure for anova (for complete
cases data) is high up on my wish list. And I guess also on David Aireys
(did your anova finish at all?) and others who do experimental research.
> David Airey <[email protected]> mentioned several
> alternatives for repeated measures data including Stata's
> -manova- command that was introduced in Stata 8. I personally
> like MANOVA over repeated measures ANOVA. (But there are some
> cases where the MANOVA cannot be done -- too many y variables
> compared to the number of observations -- where the repeated
> measures ANOVA can still be computed.)
MANOVA is an interesting alternative in many situations. I will consider it
when appropriate. If I'm not mistaken though, the present analysis would not
run in MANOVA because it would mean 3*20 dependent variables and only 17
subjects. This is also typical for many of our experiments (and some of our
field studies) so ANOVA would still be our main approach.
David Airey <[email protected]>:
> As for me, the more I use Stata, the more I like it, but the more I
> mess around with statistics, the more tools I wind up exploring (Data
> Desk, Stata, and R, so far).
Agree. Stata is really growing on me. And this is of course part of my
problem. I want Stata to be able to do it all ... I don't want to spend time
in to many programs but I have realized that there are limits even to Stata.
Currently though, I have my hands full with learning Stata and LISREL. And
soon I will take a course in GLLAMM.
> For biologists using statistics, the main weaknesses of Stata are
> currently a lack of a routine like SAS Mixed or R LME/NLME
Mixed modeling is an area that I'm very interested in. I have no practical
experience of it but from what I've read it is the answer to many of my
problems. And that's why I will take some time to learn GLLAMM which as I
understand is the closest to Proc Mixed you can get in Stata.
> Please send me the data set if it's not private, and I will run on my
> Powerbook to compare times. I'm curious about this. I have a 1.25 GHz
> Powerbook.
Check you mail.
Finally, many thanks to Ken Higbee and David Airey your time and knowledge.
Thanks.
Michael Ingre
-----------------
PhD-student
Department of Psychology
Stockholm University &
National Institute for
Psychosocial Medicine
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