[R-sig-eco] lme post-hoc help

[Nicholas Lewin-Koh]

Hi Nathan,
I don't use SPSS, so I can't comment on what it is doing,
but if you look at the bottom of the output from multcomp
it says:(Adjusted p values reported -- single-step method)
What that means is that multcomp is adjusting for the fact 
that you are doing six comparisons. So a quick and dirty
explanation is that worst case (Bonferonni), if you were
rejecting at 0.05, you would have to reject at 0.05/6=0.0083
and the p-values are adjusted accordingly. The adjustment
multcomp uses as default is not as severe as that.

Might I ask how many days you have? If you only have a small
number of days, you may not be able to estimate the variance
of the random effects very accurately, and you are better off
putting it in as a fixed effect.

lastly, your effect sizes aren't all that big, you may
need to look at your measurement instrument, are you using
% cover? or some other measure.

Best
Nicholas 

> Message: 2
> Date: Wed, 3 Mar 2010 13:04:54 -0600
> From: Nathan Lemoine <lemoine.nathan at gmail.com>
> To: r-sig-ecology at r-project.org
> Subject: [R-sig-eco] lme post-hoc help
> Message-ID: <DE063D70-0155-4DE7-8DE6-569E6AFB9121 at gmail.com>
> Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes
> 
> Hi all,
> 
> I'm attempting to analyze some data on log-transformed algae grazing  
> rates that I've collected in different habitats. I when collecting the  
> data, I blocked for day to account for temporal variation in grazing  
> intensity, and I'm considering DAY as a random factor in my model. As  
> such, I've used the lme model to construct the mixed-effects model as  
> follows:
> 
>  > al_lme <- lme(grazing~habitat, random = ~1|day, data=algae)
> 
> The ANOVA summary shows a significant result:
> 
>  > anova(al_lme)
> 
>             numDF   denDF   F-value        p-value
> (Intercept)     1    32        174.97322    <.0001
> HAB               3    32          3.31776       0.0321
> 
> Yet, when I do a post-hoc comparison, none of the pairwise tests are  
> significant:
> 
>  > pairs <- glht(al_lme, linfct = mcp("habitat"="Tukey")
>  > summary(pairs)
> 
>                                        Estimate Std. Error  z value  
> Pr(>|z|)
> Fake - Complex == 0    0.2125     0.5390   0.394    0.978
> Far - Complex == 0       1.1937     0.5390   2.215    0.114
> Near - Complex == 0   0.7758     0.3623   2.142    0.134
> Far - Fake == 0              0.9813     0.4437   2.211    0.115
> Near - Fake == 0           0.5633     0.5390   1.045    0.715
> Near - Far == 0             -0.4179     0.5390  -0.775    0.861
> (Adjusted p values reported -- single-step method)
> 
> How is this possible? In visually inspecting the data, it is apparent  
> that at least the Far-Complex ought to be significant. To be sure, I  
> double checked my statistics using SPSS, which is where I'm getting  
> more confused.
> 
> In SPSS, I built a blocked, general linear model with Loss as the  
> dependent, Habitat as the fixed factor, and Day as a random factor. I  
> used the default Type III SS because the design was not balanced. SPSS  
> also returns a significant effect:
> 
> Habitat F = 4.741, denom df = 33, p = 0.015
> 
> and the Tukey's HSD post-hoc test returns a significant difference  
> between the Far-Complex habitats, like expected. My questions are:  
> First, how can I receive a significant result in R and have no  
> significant pairwise effects? Second, what are the differences between  
> SPSS and R, that SPSS uses a different denominator df to calculate the  
> F-statistic? This is probably the reason that the p-value for SPSS is  
> lower, but I'm not sure that this is part of the reason for the  
> different post-hoc results.
> 
> Thanks for any help,
> 
> Nate Lemoine
> 
> 
> 
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> End of R-sig-ecology Digest, Vol 24, Issue 2
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