Hello,
I used the gls function from the nlme package to run a generalized least
squares model. One of the predictor variables is a factor with 3 levels. Here
is a reproducible example:
library(nlme)
response <- c(rnorm(5,1,3), rnorm(5,6,1), rnorm(5,10,5))
foo <- data.frame(response = response,
X=rep(letters[1:3], each=5),
Y=rep(LETTERS[1:3], each=5))
m1 <- gls(response ~ X, weights = varIdent(form= ~1|Y), data=foo)
The anova command indicates that the factor X is significant:
anova(m1)
The summary command compares the mean of each level of X to the reference
level, which is 'a' in this case:
summary(m1)
Based on the summary command, I will report that levels 'b' and 'c' are greater
than 'a' at the p < 0.05 level. My question is, what test should I cite for
these post hoc comparisons? Are these contrasts a version of Tukeys, Scheffe,
Fisher LSD, or something similar.
This reproducible example can also be viewed at:
http://rpubs.com/jbeaulie/12839
==================================
Jake J. Beaulieu, PhD
US Environmental Protection Agency
National Risk Management Research Lab
26 W. Martin Luther King Drive
Cincinnati, OH 45268
USA
513-569-7842 (desk)
513-487-2511 (fax)
beaulieu.j...@epa.gov
[[alternative HTML version deleted]]
______________________________________________
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.
