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 hoc’s 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
______________________________________________
R-help@r-project.org 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.
