[R-sig-ME] Using nlme package in an information-theoretic framework, AIC

Ben Bolker bbolker at gmail.com
Tue Jan 4 02:34:26 CET 2011

On 11-01-03 06:51 PM, Corey VanStratt wrote:
> Dear Listers,
> I am studying the foraging effort of surf scoters (a sea duck) during
> the winter period @ a site in southeast Alaska. This particular
> species only dives for its food, and therefore the response of
> interest is both minutes underwater per hour and minutes underwater
> per day.  These 2 metrics provide a reliable estimate of how often
> these birds are feeding during the winter, and they can be compared
> to estimates collected in the same manner at different winter sites.
> To collect these data, we attached VHF transmitters to 110
> individuals; we monitored individuals during 1-hour foraging
> observations; we knew a bird was diving when a signal was lost, as
> the VHF radio signal does not transmit through water.  For each
> observation, we calculated total minutes underwater/hour and
> extrapolated to minutes underwater/day (these ducks are diurnal
> foragers, so based daily rates on amount of daylight hours).  Of the
> 110 individuals marked, we were able to get at least 2 observations
> on 65 individuals. Over 2 seasons, there were a total of 959 1-hour
> observations on these 65 individuals.
> The data set was unbalanced and we observed the same individuals over
> time (repeated measures, therefore requiring mixed models); the
> number of observation periods was not equal among all individuals and
> time between observations within and among individuals was not equal.
> Therefore, we required a mixed modeling procedure to account for
> these repeated measures of individuals.  We used the nlme package in
> R to specify individual as a random effect.
> Foraging effort variation at all sites was evaluated using an
> information-theoretic approach.  Therefore, rather than 'model
> building', as is described in much of the nlme documentation and
> resources, I am testing several pre-determined, biologically
> plausible models. However, I have read in prior posts that AIC values
> may not be reliable from this package, and I have been using these
> values to compare the models. So, I have three main questions:
> 1.) Does the process I describe below to analyze these data make
> sense?
> 2.) In the case of this data set and the candidate model set, is it
> appropriate to use the given AIC values from the nlme output for
> determining which model is most parsimonious?
> 3.) If it is not appropriate to use these AIC values, what is the
> best alternative (e.g. how could I obtain AIC values that are
> reliable)?
> So, moving onto the process:
> We evaluated the variation in response of both minutes underwater per
> hr and per day in relation to several covariates.  We considered 16
> biologically plausible a priori candidate models (see below).  We
> explored date as an explanatory variable, and we included it both as
> a date and date squared term (referred to as DATE), to allow
> variation in foraging effort to vary non-linearly over time; we
> adjusted all date values in both years to correspond to 2 December =
> day 1.  We always considered sex (male or female) and age [hatch year
> (HY) or after hatch year (AHY)] together as COHORT, as these four
> groups often behave differently.  We included a YEAR term (2008/09 or
> 2009/10) to account for variation between years.  We considered the
> following environmental factors (ENVIRON) in each model except for
> the true null: tide height (m), temperature (°C), time of day of
> observation, and wind speed (categorical with three levels—low, mid,
> high).  We did this to both limit the number of models in our set,
> and because we merely wanted to account for environmental effects.
> Finally, we included the interaction effect terms COHORT*DATE and
> COHORT*YEAR, because we believed it quite feasible that the different
> cohorts would respond to environmental pressures differently both
> across time within a winter season and between the winter seasons.

fmN	Null
fm1	Environ
fm2	Date  + environ
fm3	Cohort  + environ
fm4	Year  + environ
fm5	Date + cohort + environ
fm6	Date + year + environ
fm7	Cohort + year + environ
fm8     Date + cohort + year + environ
fm9	Date + environ + cohort x date
fm10	Year + environ + cohort x date
fm11	Date + cohort + environ + cohort x date
fm12	Date + cohort + year + environ + cohort x date
fm13	Cohort + environ + cohort x year
fm14	Date + cohort + environ + cohort x year
fm15	Date + cohort + year + environ + cohort x date + cohort x year

> For each response variable, we first ran the most global model (fm15)
> in our set using a model with no random effects, a model with a
> random intercept (allowed intercept to vary among individuals), and a
> model with a random intercept and slope (allowed both intercept to
> vary among individuals and slope to vary within each individual over
> DATE).  'ID' in the random effects refers to each of the 65 uniquely
> identified ducks.  Using an AIC approach, we found that the random
> intercept and slope model was the most parsimonious.  Proceeding with
> this global model and continuing with an AIC information-theoretic
> approach, we ran the global model (inc. the random intercept/slope)
> against several different feasible covariance structures, and found
> that for both response variables (minutes underwater hr-1 and day-1),
> an exponential covariance structure was the best-fitting based on AIC
> values.  Therefore, we applied this covariance structure to all of
> the models in the candidate model set and proceeded with the overall
> best-fitting model selection process. That final most parameterized
> model specification as written for R is as follows:
> fm15 <-
> lme(fixed=MINh~((Date+DateSq)*(Cohort))+((Cohort)*Year)+Tide+
> Temp+factor(Sea)+Time.start,data=data1,
> random=~Date+DateSq|ID,corr=exp2,method="ML")
>  We calculated Akaike’s Information Criterion (AICc), adjusted for
> small sample size, for all of the models in the candidate model set
> from the AIC values given in the lme output.  We then examined
> relative support for each model using Δ AICc and AICc weights (wi).
> So, again, being relatively new to both R and mixed modeling, I would
> be much appreciative if those with a greater understanding of the
> process could inform me if the process I have used indeed is
> appropriate/makes sense (as I have not used a model building exercise
> that is often described when using this package), and if the AIC
> values can in fact be used with relative confidence.
> Thanks so much for any insight, Corey VanStratt
Example of output from fm15:

Linear mixed-effects model fit by maximum likelihood
 Data: data1
       AIC      BIC    logLik
  5723.309 5835.224 -2838.654

Random effects:
 Formula: ~DateB + DateSq | ID
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev      Corr
(Intercept) 3.500360472 (Intr) DateB
DateB       0.175541462 -0.680
DateSq      0.001982993  0.576 -0.967
Residual    4.185115132

Correlation Structure: Exponential spatial correlation
 Formula: ~DateB | ID
 Parameter estimate(s):
Fixed effects: MINh ~ DateB + DateSq + Cohort + (Cohort):(Season) + Tide
+ Temp +      factor(Sea) + Time.start
                       Value Std.Error  DF   t-value p-value
(Intercept)         9.972296  2.624219 887  3.800100  0.0002
DateB               0.025724  0.031821 887  0.808423  0.4191
DateSq              0.000128  0.000336 887  0.379635  0.7043
CohortAHY M        -7.337342  3.060351  57 -2.397549  0.0198
CohortHY F          4.634536  4.220465  57  1.098110  0.2768
CohortHY M         -7.870449  3.346530  57 -2.351824  0.0222
Tide                0.157180  0.088498 887  1.776074  0.0761
Temp               -0.009083  0.039280 887 -0.231239  0.8172
factor(Sea)6        0.111870  2.194239 887  0.050983  0.9594
factor(Sea)7-8      3.294262  1.961388 887  1.679556  0.0934
Time.start          0.087318  0.068847 887  1.268298  0.2050
CohortAHY F:Season -1.925349  1.412226  57 -1.363344  0.1781
CohortAHY M:Season  1.804896  1.235439  57  1.460935  0.1495
CohortHY F:Season  -4.522203  1.914241  57 -2.362400  0.0216
CohortHY M:Season   2.323518  1.469479  57  1.581185  0.1194

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max
-2.5466867 -0.6254431 -0.1261256  0.5150277  3.4233514


  As far as I can tell, this all looks pretty reasonable.

  General questions/comments:

- what are your concerns about unreliable AIC values?  Can you quote a
specific post to this list?  There are various concerns with AICs in the
mixed model context, but I don't know that any of them seems
particularly awful in the context you describe:
  - there is a general issue when testing against null hypotheses on the
edge of their feasible space (e.g. testing whether variances are equal
to zero or not), which also affects AICs (because the approximations on
which AICs are based rely on expanding *around* the model)
   - it can be hard to count 'degrees of freedom' for random effects,
both for comparing models with different random effects (here Vaida and
Blanchard's 'conditional AIC' is useful, but I think you're interested
in population-level predictions, so the relevant number of parameters
would be approx. 1) and for calculating residual degrees of freedom for
finite-size corrections such as AICc.  Your spatial correlation term
also affects this consideration, because your data points are not
entirely independent (and hence you have fewer 'effective' residual
degrees of freedom).
   - What kind of inference are you making *after* you go through the
model selection procedure?  Are you planning to use multi-model
inference?  The biggest mistake that you might be making (can't tell
from your description) would be to take the selected model and calculate
confidence intervals for it, disregarding the model selection uncertainty.
  Or are you using the AICc values to (effectively) test hypotheses
about which variables are 'important'?  I have a bit of a problem with
this approach (I think if you're going to test hypotheses you should do
it explicitly, not use an approach that is based on maximizing expected
predictive accuracy), but I'm quite in the minority in that opinion.

  Bottom line: I would strongly encourage you to supplement what you've
done by exploring the data, and the predictions/residuals from the model
graphically, and I am curious what kind of inferences you plan to make
from these data, but overall your approach seems to make sense -- no big
red flags going up for me.

  Others may see something I missed, or have different opinions.

  Ben Bolker

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