[R] ANCOVA post-hoc test

[Rmh]

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 <pdalgd at 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
>> ______________________________________________
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>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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> 
> -- 
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com
> 
> ______________________________________________
> R-help at 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.