[R] LSD, HSD,...

Simon Blomberg s.blomberg1 at uq.edu.au
Mon Jul 16 10:19:30 CEST 2007


If you have a priori planned comparisons, you can just test those using
linear contrasts, with no need to correct for multiple testing. If you
do not, and you are relying on looking at the data and analysis to tell
you which treatment means to compare, and you are considering several
tests, then you should consider correcting for multiple testing. There
is a large literature on the properties of the various tests. (Tukey HSD
usually works pretty well for me.)

<rant> Why do people design experiments with a priori hypotheses in
mind, yet test them using post hoc comparison procedures? It's as if
they are afraid to admit that they had hypotheses to begin with! Far
better to test what you had planned to test using the more powerful
methods for planned comparisons, and leave it at that.
</rant>


On Mon, 2007-07-16 at 09:52 +0200, Adrian J. Montero Calvo wrote:
> Hi,
>     I'm designing a experiment in order to compare the growing of 
> several clones of a tree specie. It will be a complete randomized block 
> design. How can I decide what model of mean comparision to choose? LSD, 
> HSD,TukeyHSD,  Duncan,...?  Thanks in advance
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat. 
Lecturer and Consultant Statistician 
Faculty of Biological and Chemical Sciences 
The University of Queensland 
St. Lucia Queensland 4072 
Australia
Room 320 Goddard Building (8)
T: +61 7 3365 2506 
email: S.Blomberg1_at_uq.edu.au

Policies:
1.  I will NOT analyse your data for you.
2.  Your deadline is your problem.

The combination of some data and an aching desire for 
an answer does not ensure that a reasonable answer can 
be extracted from a given body of data. - John Tukey.



More information about the R-help mailing list