Hi Jarrod, 

 

You got it. My complete dataset has 165 observations but 35 NAs for
HatchingOrder (so we have 130 observations for 55 nests). There are no NAs
for predictor Year. 

 

The GLMM estimates the parameters considering only those eggs with known
HatchingOrder (thus, omitting those eggs with NAs for HatchinOrder). That
said, it makes sense that the raw data (which considers egg volume from all
eggs regardless of hatching order [then, no NAs]) tells a different story
about the differences among different egg types than the GLMM itself (which
includes HatchOrder and quite a few NAs as predictor). 

 

Am I on the right path here?

 

Best, 

 

Luciano     

    

  _____  

De: Jarrod Hadfield [mailto:j.hadfield@ed.ac.uk] 
Enviado el: Wednesday, December 29, 2010 7:15 PM
Para: Luciano La Sala
CC: r-sig-mixed-models@r-project.org
Asunto: RE: [R-sig-ME] Coefficients interpretation and plot

 

Hi Luciano,


Quoting Luciano La Sala <lucianolasala@yahoo.com.ar>:

> Hi Jarrod,
>
> Thank you for the speedy reply. My issue seems to be the opposite: raw
data
> indicates that A-eggs are a little smaller than B-eggs in 2006, while the
> GLMM (with Nest IDs as random intercepts) shows that A-eggs are a little
> larger than B-eggs.

I think this is what I meant too (if call the first to hatch as A-eggs).


I wonder if this difference comes from having included
> Nests as a random intercepts. Very far from being a statistician myself,
the
> issue at hand baffles me.
>
> By the way, I only have three-egg clutches, and first, second, and third
> hatching chicks within each nest. Any ideas as to where this difference
> comes from?

In your output you have 130 observations from 55 nests: which is about 
2.46 eggs per nest rather than three. Is it possible there are NA's 
for some of the predictors?

Cheers,

Jarrod




>
> Best,
> Luciano
>
>
> ________________________________________
> De: Jarrod Hadfield [mailto:j.hadfield@ed.ac.uk]
> Enviado el: Wednesday, December 29, 2010 6:05 PM
> Para: Luciano La Sala
> CC: r-sig-mixed-models@r-project.org
> Asunto: Re: [R-sig-ME] Coefficients interpretation and plot
>
> Hi Luciano,
>
> If I understand you correctly, your issue is that the prediction for 
> the first egg in the year that is NOT (?) 2007 is greater than second 
> eggs in that year, yet the raw data indicate the opposite?
>
> I notice that you have less than 3 eggs for each nest. If there is a 
> (positive) relationship between clutch size and egg volume you could 
> get such a discrepancy. You could try putting clutch size in the 
> model. That being said, the offending coefficient is small with a 
> large p-value (0.35) so the discrepancy may not be that surprising.
>
> Also, I'm not sure what the state of play with pvals.func is. mcmcsamp 
> used to behave oddly, and from your output the fixed effects look OK, 
> but the 95% MCMC CI's for the variance components do not seem to 
> overlap the REML estimates. Its possible there on a different scale, 
> but I would check.
>
> Cheers,
>
> Jarrod
>
>
>
>
> Quoting Luciano La Sala <lucianolasala@yahoo.com.ar>:
>
>> Hello everyone,
>>
>> Since I'm not entirely sure this is THE list I should be referring too,
> feel
>> free to blow me off and refer me to another mailing list if necessary.
>> I am analyzing a small dataset using lmer from lme4 package. My model has
>> "egg volume" as dependent variable and "hatching order" and "year" as
>> dependent variables. The best fit model has these two variables plus
their
>> interaction (hatching order*year). I included Nest_ID as random
> intercepts.
>> Output follows:
>>
>>> best <- lmer(EggVolume~HatchOrder+Year+HatchOrder*Year+(1|NestID), data
=
>> Data)
>>> summary(best)
>>
>> Linear mixed model fit by REML
>>
>> Formula: EggVolume ~ HatchOrder + Year + HatchOrder * Year + (1 | NestID)
>>     Data: Data
>>     AIC BIC logLik deviance REMLdev
>>   736.1 759 -360.1    729.1   720.1
>>
>> Random effects:
>>   Groups   Name        Variance Std.Dev.
>>   NestID   (Intercept) 26.2931  5.1277
>>   Residual              6.2175  2.4935
>>
>> Number of obs: 130, groups: NestID, 55
>>
>> Fixed effects:
>>                        Estimate Std. Error t value
>> (Intercept)           79.7261     1.1350   70.24
>> HatchSecond           -0.7227     0.7758   -0.93
>> HatchThird            -4.8455     0.9112   -5.32
>> Year2007               3.5548     1.5750    2.26
>> HatchSecond:Year2007  -2.6914     1.0752   -2.50
>> HatchThird:Year2007   -2.7999     1.2294   -2.28
>>
>> Correlation of Fixed Effects:
>>              (Intr) HtchOS HtchOT Yr2007 HOS:Y2
>> HtchOrdrScn -0.277
>> HtchOrdrThr -0.229  0.388
>> Year2007    -0.721  0.199  0.165
>> HtcOS:Y2007  0.200 -0.722 -0.280 -0.299
>> HtcOT:Y2007  0.170 -0.287 -0.741 -0.301  0.415
>>
>> I used the "pvals.fnc" function in the "coda" package to estimate
> p-values.
>> Output follows:
>>
>>> pvals.fnc(best, nsim = 10000, ndigits = 4, withMCMC = FALSE,
>> addPlot=FALSE)
>>
>> $fixed
>>                    Estimate MCMCmean HPD95lower HPD95upper  pMCMC
Pr(>|t|)
>> (Intercept)       79.7261  79.5990     77.687    81.5521 0.0001   0.0000
>> HatchSecond       -0.7227   0.1239     -2.630     2.8256 0.9468   0.3534
>> HatchThird        -4.8455  -4.3177     -7.391    -1.0711 0.0086   0.0000
>> Year2007           3.5548   3.9605      1.090     6.8664 0.0080   0.0258
>> HatchSecond:2007  -2.6914  -3.4393     -7.235     0.3782 0.0772   0.0136
>> HatchThird:2007   -2.7999  -3.6649     -7.768     0.5046 0.0830   0.0245
>>
>> $random
>>      Groups        Name Std.Dev. MCMCmedian MCMCmean HPD95lower
HPD95upper
>> 1   NestID (Intercept)   5.1277     2.3265   2.3166     1.5388     3.1619
>> 2 Residual               2.4935     4.5507   4.5744     3.8179     5.4108
>>
>> I understand that, even in mixed models, one should not interpret main
>> effects' coefficients by themselves when a significant interaction is
>> present. Instead, coefficients of the main effect and the interaction
term
>> should be added. In my example:
>>
>> # Coefficient for HatchSecond:Year2007: -0.7227 + (-2.6914) = -3.4141
>>
>> Then, in 2007 the volume of HatchSecond eggs was 3.41 units lower than
> that
>> of HathFirst eggs.
>>
>> # HatchThird:Year2007: -4.8455 + (-2.7999) = -7.6454
>>
>> Then, in 2007 the volumen of HatchThird eggs was 7.64 units lower than
> that
>> of HathFirst eggs.
>>
>> 1. Are these interpretations correct in the contest of mixed modeling?
>>
>> 2. When I plot my raw data (means of egg volume for each year and
> stratified
>> by hatching order), the plot looks good for 2007 (decreasing egg volumes
>> along the hatching sequence). However, HatchSecond eggs have a mean
volume
>> slightly larger than that of HatchFirst eggs (80,02 vs. 79,47) which
> doesn't
>> reconcile with my GLMM: HatchSecond eggs from 2007 are 3.41 units lower
> than
>> that of HathFirst eggs.
>>
>> That said, I was wondering if this differences is due to the fact that
the
>> GLMM includes a random effect (Nest) while plotting raw data ignores it.
>>
>> Thank you very much in advance!
>>
>> LFLS
>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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