Dear Yulia,
When you have an interaction between a continuous and a categorical variable,
then the multiple comparison on the categorical variabel makes only sense
conditional that the continuous variable is zero. Hence the warning.
Best regards,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx at inbo.be
www.inbo.be
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asking him to perform a post-mortem examination: he may be able to say what the
experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure
that a reasonable answer can be extracted from a given body of data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Yuliia Aloshycheva
Verzonden: woensdag 7 november 2012 21:52
Aan: r-help at r-project.org
Onderwerp: [R] A warning message in glht
Dear all,
I was wondering if you could give me any suggestions/help on the following
issue. So I carried out the analysis of my data using generalized linear model
(glm). After that, to check for multiple comparisons, I applied the glht
function from the multcomp package in R. The output, however, gave me a warning
(please see below). So my question is whether this warning is smth that I should
ignore or not. And if not, what I should do about it (I kind of know how to deal
with this problem for categorical factors, however, in my data, one of the
factors (AveScore) is continuous).
Thanks a lot!
Yuliia
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: glm(formula = EW1 ~ AveScore + Speaker + File + factor(Bplace) +
factor(Sex) + AveScore:File, family = binomial(link = "logit"),
data = data.0)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
In - A == 0 -0.9055 0.3573 -2.534 0.0302 *
O - A == 0 -2.5638 0.3917 -6.545 <0.001 ***
O - In == 0 -1.6584 0.3766 -4.404 <0.001 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 (Adjusted p
values reported -- single-step method)
*Warning message: In mcp2matrix(model, linfct = linfct) : covariate
interactions found -- default contrast might be inappropriate*
--
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