In the model: lm.1 <- lm(variable ~ BLOC + TIL * YEAR , data=selvanera) I found TIL*YEAR interaction significant. Then I am trying to compare means of the different levels of TIL inside every YEAR using: mc.2 <- glht(lm.1, linfct = mcp(TIL*YEAR="Tukey")) summary(mc.2, test = univariate()) but it does not work. There is any way of doing this, like the SLICE option in PROC GLM (SAS)? Thanks a lot, Jorge -- ************************************************** Jorge Lampurlan?s Castel Departament d'Enginyeria Agroforestal Escola T?cnica Superior d'Enginyeria Agr?ria Universitat de Lleida Avinguda Rovira Roure, 191 25198-LLEIDA SPAIN Tl.: +34 973 70 25 37 Fax.:+34 073 70 26 73 e-mail: jlampur at eagrof.udl.es
Yes, it can be done. It is not currently easy because multcomp doesn't have the syntax yet. Making this easy is on Torsten's to-do list for the multcomp package. See the MMC.WoodEnergy example in the HH package. The current version on CRAN is HH_1.17. Please see the discussion of this example in R-help: https://stat.ethz.ch/pipermail/r-help/2007-January/123451.html Rich
Hello,
Data comes from a multiyear field experiment in which 4 levels of a
treatment (2, 3, 4, 6) are compared to see the effect on yield. It is a
randomized complete block design.
The SAS code follows:
options ls=95;
data uno;
infile 'data.csv' delimiter=';' firstobs=2;
input year plot block treat yield;
run;
proc mixed data=uno;
class treat year block;
model yield=block year treat treat*year;
lsmeans year treat /pdiff;
lsmeans treat*year /slice=year pdiff;
ods output diffs=dos;
run;
data tres;
set dos;
if year=_year;
proc print data=tres;
var year _year treat _treat estimate stderr df tvalue probt;
run;
Data are attached as a file: data.csv.
In fact, I do not know if this is the best approach to analyze the data:
- Should block be considered as random? We use the same file and
randomization every year. We are interested in the long term effect of the
treatment.
- Data should be considered as repeated measurements over time (years)?
In multcomp package:
- What is the equivalence between the tests proposed ("Sequen",
"AVE",
"Changepoint", "Williams", "Marcus",
"McDermott") and the tests agronomist
are used to do: LSD (least significant difference), Duncan multiple range
test, Scheffe, S-N-K (Student-Newman-Keuls)?
Thanks a lot for your interest.
Jorge Lampurlan?s
Agronomist
>> Is it possible to do this analysis in R?
>
> Yes, it is possible. The syntax isn't in place yet.
>
> If you send me the complete SAS code and data for an example using slice,
> I will duplicate it for you in the multcomp package in R. I will send
> that
> to the R-help list and to Torsten and it will bring us one step closer
> to the syntax.
>
> The example I showed before was designed to get the same answer as S-Plus
> multicomp using the adjust= argument.
>
> Rich
>
Data is here. I'm sorry.>> Is it possible to do this analysis in R? > > Yes, it is possible. The syntax isn't in place yet. > > If you send me the complete SAS code and data for an example using slice, > I will duplicate it for you in the multcomp package in R. I will send > that > to the R-help list and to Torsten and it will bring us one step closer > to the syntax. > > The example I showed before was designed to get the same answer as S-Plus > multicomp using the adjust= argument. > > Rich >-- ************************************************** Jorge Lampurlan?s Castel Departament d'Enginyeria Agroforestal Escola T?cnica Superior d'Enginyeria Agr?ria Universitat de Lleida Avinguda Rovira Roure, 191 25198-LLEIDA SPAIN Tl.: +34 973 70 25 37 Fax.:+34 073 70 26 73 e-mail: jlampur at eagrof.udl.es **************************************************