Dear Alejandro,
If I understand correctly what you want (that is, pairwise multivariate
tests among the groups), you could do the following tests (some output
suppressed):
> linear.hypothesis(diversitylm.ok, c("(Intercept)=CategoryM"),
+ idata=idata.df, idesign=~Season, iterms="Season")
Response transformation matrix:
Season1 Season2
dLluvias 1 0
dNortes 0 1
dSecas -1 -1
Sum of squares and products for the hypothesis:
Season1 Season2
Season1 0.03447365 0.04897023
Season2 0.04897023 0.06956279
Sum of squares and products for error:
Season1 Season2
Season1 0.07481798 0.1463547
Season2 0.14635470 0.3945622
Multivariate Tests:
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.3557184 1.3802911 2 5 0.33319
Wilks 1 0.6442816 1.3802911 2 5 0.33319
Hotelling-Lawley 1 0.5521164 1.3802911 2 5 0.33319
Roy 1 0.5521164 1.3802911 2 5 0.33319
> linear.hypothesis(diversitylm.ok, c("(Intercept)=CategoryI"),
+ idata=idata.df, idesign=~Season, iterms="Season")
. . .
Multivariate Tests:
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.712033 6.181560 2 5 0.044500 *
. . .
> linear.hypothesis(diversitylm.ok, c("CategoryI=CategoryM"),
+ idata=idata.df, idesign=~Season, iterms="Season")
. . .
Multivariate Tests:
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.716616 6.321937 2 5 0.04275 *
. . .
The Bonferroni adjustment is to multiply each p-value by 3 (the number of
tests); it should be conservative in this context, and so there should be
better approaches for multivariate pairwise comparisons.
Regards,
John
--------------------------------
John Fox, Professor
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------
> -----Original Message-----
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of "Alejandro
> Luis Collantes Chávez-Costa"
> Sent: Thursday, September 27, 2007 1:45 PM
> To: r-help@r-project.org
> Subject: [R] Post-hoc after Anova() car package using
> linear.hypothesis() in a Repeated Measure Analysis
>
> R masters:
>
> I need your help to figure out how can I perform Post-hoc
> test after Anova() car package using
> linear.hypothesis() in a Repeated Measure Analysis.
>
> I performed a Repeated Measures Analysis to test the effect
> of Category, Season and their Interaction on some ecological
> properties using Anova() from car package.
> I find some significant effect and now I would like to know
> where the differences are. In order to perform these Within,
> Between and within-between post hocs Professor John Fox
> recommend me to use linear.hypothesis() and a Bonferonni
> adjustment of the p-values.
>
> After a couple of weeks of work, I can not figure out how to
> do that. I am very sorry, I did my best but I am not
> statistician and I can not find examples to understand how
> can I use linear.hypothesis() in post hoc test.
>
> There are someone who can help me?
>
> P.D. (I am very thankful to Professor Richard Heiberger who
> give me advices about the use of glht.mmc() with the
> calpha argument).
>
>
> For those who can help me, I am posting some extra
> information about my case and my attempts:
>
> The experiment design was as follow: 1 between subjects
> (fixed factor with 3 levels) and 1 within subjects (fixed
> factor with 3 levels). Between subjects are nine plots
> grouped into 3 age category (tree plot for each age category
> T, I, M), and Within category are 3 season of the year
> "llu, nor, sec" (equidistant in the time scale). The data set is:
>
> lludiversity nordiversity secdiversity Plot
> Category Season
> 1.96601 2.10215 2.17984 07A T llu
> 1.73697 1.96866 1.99766 10B T llu
> 1.87122 1.92848 2.2673 10C T llu
> 2.06851 1.98455 2.43838 15B I llu
> 2.17905 2.49451 2.25759 15C I llu
> 2.2572 2.16882 2.58295 17A I llu
> 1.99913 2.43767 2.29582 60A M llu
> 2.12738 2.64161 2.5385 60B M llu
> 2.22421 2.42401 2.5385 60C M llu
>
> To test the effect of Category, Season and their Interaction
> on some ecological properties I use the following code:
>
> rm(list=ls(all=TRUE))
> library(lattice);
> library(Matrix);
> library(car);
> setwd(c:/R help/);
> diversity.tbl <- read.table("diversity.txt", header=TRUE);
> Season <- factor(c("Lluvias","Nortes","Secas"),
> levels=c("Lluvias","Nortes","Secas"));
> idata.df <- data.frame(Season) # Within plot; Plot <-
> diversity.tbl[,4]; Category <- factor(diversity.tbl[,5],
> levels=c("T", "I", "M")) #Between plot; dLluvias <-
> diversity.tbl[,1]; dNortes <- diversity.tbl[,2]; dSecas <-
> diversity.tbl[,3]; datalm.df <- data.frame(Plot, Category,
> dLluvias, dNortes, dSecas); diversitylm.ok <-
> lm(cbind(dLluvias, dNortes, dSecas) ~ Category,
> data=datalm.df); diversityav.ok <- Anova(diversitylm.ok,
> idata=idata.df, type="II", idesign=~Season);
> summary(diversityav.ok, multivariate=FALSE); diversityav.ok
>
> To try perform post hoc test I did:
> linear.hypothesis(diversitylm.ok, c("(Intercept)=CategoryM"),
> idata=idata.df, idesign=~Season, iterms="Season"); #To
> contrast Intercept (Category T) and Category M
>
>
> Alejandro Collantes Chávez-Costa
> Universidad de Quintana Roo, Unidad Cozumel
>
> http://www.cozumel.uqroo.mx
>
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