Hi everyone
I'm using Tukey HSD as post-hoc test following a lme analysis. I'm
measuring hemicelluloses in different species treated with three
different CO2 concentrations (l=low, m=medium, h=high). The whole
experiment is a split-plot design and the Tukey-function from the
package multcomp is suitable for lme-analysis with random factors.
The analysis works fine but I get a non significant lme-result and if I
do a Tukey afterwards (I know that one usually does a Tukey following a
significant anova-result) I get highly significant p-values for two
multiple comparisons.
How can this happen? How can the p-values from the Tukey become
significant when the lme-model wasn't?
the data are: d
Species Block CO2 hemicell
Ps a l 9.027363
Ps b l 9.647537
Ps a m 10.051916
Ps b m 10.112294
Ps a h 10.342162
Ps b h 10.303091
my lme model:
anova(hc<-lme(asin(sqrt(0.01*hemicell))~CO2,random=~1|Block/CO2,data=d))
numDF denDF F-value p-value
(Intercept) 1 2 30403.248 <.0001
CO2 2 2 8.051 0.1105
Tukey with the lme-object:
summary(glht(hc, linfct=mcp(CO2="Tukey")))
yielding:
Linear Hypotheses:
Estimate Std. Error z value p value
m - l == 0 0.012616 0.004317 2.922 0.00963 **
h - l == 0 0.016590 0.004317 3.843 < 0.001 ***
h - m == 0 0.003973 0.004317 0.920 0.62738
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)
Thank you very much for your help
Christina
--
Christina Schädel
Institute of Botany
University of Basel
Schönbeinstrasse 6
CH-4056 Basel
ph. +41 61 267 35 06
fax +41 61 267 29 80
E-Mail [EMAIL PROTECTED]
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