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

[Nathan Lemoine]

Thanks for the responses,

So my design is an unbalanced incomplete randomized block design,  
actually. There are 4 replicates per day over the course of three days  
for the Simple and Complex habitats, and 4 replicates per day over the  
course of 2 separate days for the Fake and Far habitats.

So it looks like this:
Habitat		Day
Near		1
Near		2
Near		3
Complex	1
Complex	2
Complex	3
Far			4
Far			5
Fake		4
Fake		5

With four replicates in each of those categories. The metric I used  
was the hourly % loss of a tethered set of algae. Basically, I set the  
tether out, came back an hour later, and quantified the percent loss  
in terms of mass.



On Mar 4, 2010, at 10:32 AM, Nicholas Lewin-Koh wrote:

> 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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