[R-sig-ME] Multi-level qualitative (fixed-effects) factors

[Andrew Dolman]

I don't get it. How can you fit the model with just 1 of three levels
of factor "habitat" and have the same number of observations as when
you run the model with all three? (It must have at least 2 levels to
fit anyway) Also, in the first example you have 4 levels of habitat.

Are they different levels of habitat resolution? e.g.

Aquatic - non aquatic
Aquatic - Terrestrial - Epiphytic
Aquatic - Terrestrial - Epiphytic - Up elephant's noses


Please read the posting guide and include proper examples of what you
are doing and what the data look like.


andydolman at gmail.com



On 3 August 2010 08:48, Peter Francis <peterfrancis at me.com> wrote:
> Hi David and Ben, thanks for your help -
>
> I was worried this would make little sense!
>
> I have set out my candidate models
>
> A+B+C+D
> B+C+D
> A+C+D
> etc etc
>
> And am running through them in lmer. Factor A for instance is Habit, which takes 3 forms - aquatic, terrestrial or epiphyte.
>
> When i run the model with A as a factor i get the breakdown of the individual levels habitat 1, habitat 2 and habitat 3 and a corresponding AIC score. However if i just run it with habitat 3 - aquatic - i get a lower AIC score, therefore the model fits the data better?
>
> I am unsure how to, without splitting my factors into their constituent levels at the beginning - A1+A2+A3 + B1 + B2 etc, arrive at the model with the lowest AIC?
>
> Thanks
>
> Peter
>
> On 3 Aug 2010, at 00:16, David Duffy wrote:
>
> On Mon, 2 Aug 2010, Peter Francis wrote:
>
>> I have many multi level factors i.e habit - aquatic, terrestrial, epiphyte etc
>>
>> I ran the model with habit as a factor
>>
>>> model111 <-lmer(threatornot~1+(1|a/b) + habit, family=binomial)
>>
>>> Generalized linear mixed model fit by the Laplace approximation
>>> Formula: threatornot ~ 1 + (1 | order/family) + habit
>>>  AIC  BIC logLik deviance
>>> 1406 1436 -696.9     1394
>>> Random effects:
>>> Groups       Name        Variance   Std.Dev.
>>> family:order (Intercept) 6.9892e-01 8.3602e-01
>>> order        (Intercept) 4.2292e-14 2.0565e-07
>>> Number of obs: 1116, groups: family:order, 43; order, 9
>>>
>>> Fixed effects:
>>>            Estimate Std. Error z value Pr(>|z|)
>>> (Intercept) -0.04803    0.19174  -0.250  0.80219
>>> habit2       1.10627    0.41607   2.659  0.00784 **
>>> habit3       0.92578    0.78141   1.185  0.23611
>>> habit4       0.14383    0.38477   0.374  0.70856
>>
>> ---
>> Which had a AIC of 1406
>>
>> I then re-ran the model with only aquatic and got a lower AIC value - which i guess is to be expected as aquatic is highly significant and aquatic species are more prone to threat ( my response).
>>
>>
>>>> model112 <-lmer(threatornot~1+(1|a/b) + aquatic, family=binomial)
>>>> model112
>>> Generalized linear mixed model fit by the Laplace approximation
>>> Formula: threatornot ~ 1 + (1 | order/family) + aquatic
>>>  AIC  BIC logLik deviance
>>> 1395 1415 -693.4     1387
>>> Random effects:
>>> Groups       Name        Variance Std.Dev.
>>> family:order (Intercept) 0.60007  0.77464
>>> order        (Intercept) 0.00000  0.00000
>>> Number of obs: 1116, groups: family:order, 43; order, 9
>>>
>>> Fixed effects:
>>>            Estimate Std. Error z value Pr(>|z|)
>>> (Intercept)   0.1572     0.1827   0.860 0.389613
>>> aquatic      -0.6683     0.1737  -3.847 0.000119 ***
>>
>> My question is - when i developed the candidate models i thought using multilevel factors would be OK and i would be able to tease out the individual levels. If i split the factors into levels from the beginning then i am left with a huge amount of candidate models? This would not be a problem in stepwise regression as i could just remove the habit with the least significant P Value.
>>
>> If i remove habits i "feel" are unimportant from the beginning i feel i would be limiting the model too much.
>>
>> I hope this makes sense!
>
> I don't understand at all, I'm afraid.  Is aquatic the same as habit=2, or something?  If so, there is something funny about the model fits.
>
> If family and order are "nuisance" variables, then a stepwise approach is quite reasonable (if you are someone who thinks stepwise regression is reasonable, of course ;)).
>
> Just 2c, David Duffy.
>
> --
> | David Duffy (MBBS PhD)                                         ,-_|\
> | email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
> | Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
> | 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v
>
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