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

[David Duffy]

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