ancova in HH is a wrapper for aov that displays a set of lattice plots. the problem you are seeing is probably that glht ignores covariates (with an appropriate message) unless you specify an optional argument. I will reply in more detail when i am at my computer.
in the meantime, look at ?glht in the multcomp package and at ?mmc in the HH package for examples. Sent from my iPhone On Feb 12, 2012, at 13:28, peter dalgaard <pda...@gmail.com> wrote: > Inline below > > On Feb 12, 2012, at 13:39 , Evagelopoulos Thanasis wrote: > [...] >> >> Because there exist significantly different regression slopes, I did a post >> hoc test with glht() to find out between which samplings: >> >>> summary(glht(mod, linfct=mcp(sampling="Tukey"))) >> > > I believe this compares the intercepts, not slopes. Slope differences are in > the sampling:dist interaction terms. > >> The results seem to say that there are no significantly different slopes for >> any of the pair-wise comparisons of factor levels: >> >> Simultaneous Tests for General Linear Hypotheses >> >> Multiple Comparisons of Means: Tukey Contrasts >> >> >> Fit: aov(formula = h ~ sampling * dist, data = data) >> >> Linear Hypotheses: >> Estimate Std. Error z value Pr(>|z|) >> sp - au == 0 0.06696 0.04562 1.468 0.457 >> su - au == 0 -0.02238 0.04562 -0.491 0.961 >> wi - au == 0 0.01203 0.04562 0.264 0.994 >> su - sp == 0 -0.08934 0.04562 -1.958 0.204 >> wi - sp == 0 -0.05493 0.04562 -1.204 0.624 >> wi - su == 0 0.03441 0.04562 0.754 0.875 >> (Adjusted p values reported -- single-step method) >> > > We don't have coefficients for your model, so it is a bit hard to tell what > the parameter functions are, but I would expect those NOT to be the slope > differences. > >> Warning message: >> In mcp2matrix(model, linfct = linfct) : >> covariate interactions found -- default contrast might be inappropriate >> >> >> >> My questions are: >> >> - Did I make a mistake somewhere? (I probably did!) > > You need to figure out how to get glht to look at the appropriate linear > hypothesis (and mcp(sampling=...) is not right). I'd do a straight lm() > analysis so that I'd know exactly what the parameters mean -- aov() can be a > little too good at hiding technical details from the user! > >> - Could I do pairwise ANCOVAs and thus have just two factor levels (=two >> regression slopes) to compare each time? > > Possibly, but you'd lose the multiple comparison features of glht. > >> What does the warning message "covariate interactions found -- default >> contrast might be inappropriate" mean? >> > > That you likely don't want to look at intercepts (or whatever the "sampling" > parameters represent --- I'm not familiar with that ancova() function) in the > presence of interactions... > >> Thank you! >> Athanasios Evagelopoulos >> ______________________________________________ >> 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. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > ______________________________________________ > 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. ______________________________________________ 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.
