Re: [R] lme: how to compare random effects in two subsets of data

From: Laurent Fanchon <lfanchon_at_vet-alfort.fr>
Date: Thu 04 May 2006 - 03:22:16 EST

I thank you for your answer. It was really helpful. I purchased Pinheiro and Bates last year for the reasons you mentionned.

I checked Sec. 5.2 and think I might use the following : model.var <- update(model2,weights=varIdent(form=~1|Limb)) which gives :

Variance function:
 Structure: Different standard deviations per stratum  Formula: ~1 | Limb
 Parameter estimates:
  Left Right
1.000000 1.039030

and test the model
anova (model.var,model2)
which shows no differences between the two models.

Is this the right procedure?
Does it means there is no statistical differences between variances of the two limbs (regardless of the day)?

If I am right, I guess I should compare two other models with varIdent(form=~1|Day) and varIdent(form=~1|Day*Limb) to test if the difference of variance between the two limb is significant for between-day variation. Is this right?

thank you for your help,

Laurent Fanchon
Ecole Nationale Vétérinaire d'Alfort
National Vet School of Alfort (France)

Spencer Graves a écrit :

> (comments in line)
>
> Laurent Fanchon wrote:
>
>> Dear R-gurus,
>>
>> I have an interpretation problem regarding lme models.
>>
>> I am currently working on dog locomotion, particularly on some
>> variation factors.
>> I try to figure out which limb out of 2 generated more dispersed data.
>>
>> I record a value called Peak, around 20 times for each limb with a
>> record.
>> I repeat the records during a single day, and on several days.
>>
>> I tried to build two models, one for each limb :
>> Dog.Left <- lme (fixed=Peak~1, data=Loco,
>> subset=Limb=="Left",random=~1|Dog/Day/Record)
>> Dog.Right <- lme (fixed=Peak~1, data=Loco,
>> subset=Limb=="Right",random=~1|Dog/Day/Record)
>>
>> This allows to determine the variance attributable to each factor.
>> Record represents the within-day variation, Day represents the
>> between-day variation.
>>
>> This gives the following results :
>> VarCorr (Dog.Left)
>> Variance StdDev Dog = pdLogChol(1)
>> (Intercept) 564.55587 23.760384
>> Day = pdLogChol(1) (Intercept) 54.63027 7.391229
>> Record = pdLogChol(1) (Intercept) 23.29377 4.826362
>> Residual 27.46464 5.240672
>>
>> VarCorr(Dog.Right)
>> Variance StdDev Dog = pdLogChol(1)
>> (Intercept) 552.11246 23.497074
>> Day = pdLogChol(1) (Intercept) 70.72088 8.409571
>> Record = pdLogChol(1) (Intercept) 21.94594 4.684649
>> Residual 29.68476 5.448373
>>
>> This shows that the variance might be different for each limb.
>> For example, the variance attributable to Day might be higher for the
>> Right limb.
>>
>> This is the first part of my interpretation, and I hope to be right.
>> What do you think??
>>
>> Then, the question is : are these differences statistically significant.
>> I am not sure of how to investigate this question.
>>
>> I tried to compare several models :
>> model1<- lme (fixed=Peak~Limb, data=Loco,
>> random=list(Dog=~Limb,Day=~Limb,Record=~Limb)) this is the more
>> complicated model
>> model2<-lme (fixed=Peak~Limb, data=Loco,
>> random=list(Dog=~Limb,Day=~Limb,Record=~1))
>> anova (model1,model2) showed no difference
>> model3<-lme (fixed=Peak~Limb, data=Loco,
>> random=list(Dog=~Limb,Day=~1,Record=~1))
>> anova (model2,model3) showed a significant difference <0.0001
>>
>> model2 seems to be the best model.
>> Does it means the difference of variance between the two limb is
>> significant for between-day variation and is unsignificant for
>> within-day variation??
>
>
> NO, it does NOT mean that the variance between the two limbs is
> significantly different between days. The model includes additive
> errors for each day as
>
> e[day]+e[day & limb],
>
> and the contribution from e[day & limb] is statistically significant.
>
>>
>> Finally VarCorr (model2) gives :
>> Variance StdDev Corr Dog =
>> pdLogChol(Limb) (Intercept) 567.553021 23.823371
>> (Intr)
>> LimbRight 7.249064 2.692409 -0.166
>> Day = pdLogChol(Limb) (Intercept) 53.888346
>> 7.340868 (Intr)
>> LimbRight 4.863394 2.205310 0.363
>
>
> This estimates var(e[day]) = 53.9 and var(e[day & limb]) = 4.9.
>
> To test a difference in variance between limbs, please see Sec.
> 5.2 in Pinheiro and Bates (2000) Mixed-Effects Models in S and S-PLUS
> (Springer). I couldn't find the electronic catalog on the web site
> for the Bibliothèque de l'École nationale vétérinaire d’Alfort, so I
> couldn't check to see if they have a copy. If they don't, please feel
> free to advise them for me that you've heard that this is the best
> book on this subject available today, and it should find many users
> among researchers such as yourself. In my opinion, Doug Bates is the
> leading expert on variance components questions in the world today,
> and an institution such as the École nationale vétérinaire d’Alfort
> should have a copy of this book in their library, and their
> researchers who routinely must work with these kinds of data should
> make routine use of this book, if they don't already.
>
> hope this helps,
> spencer graves
>
>> Record = pdLogChol(1) (Intercept)
>> 22.418031 4.734768 Residual 28.740451 5.361012
>> I am not sure to understand this issue.
>> The global variance attributable to Day is roughly 53.88 (random
>> effect on the intercept). And the differences between the two limbs
>> might be increased according to a variance of 4.86 (random effect on
>> the slope).
>> Is that right?
>> But does this also make it possible to determine which limb had the
>> highest variance? I guess if I change the order of the Limb factor
>> (Right<Left) I will get the same results with LimbLeft. How to
>> determine the limb with the highest variance?
>> Do I have to refer to the first two models (Variance attributable to
>> Day was a little higher for the Right) ?
>>
>> Any help will be very appreciated,
>>
>> Laurent Fanchon
>> Ecole Nationale Vétérinaire d'Alfort
>> National Vet School of Alfort (France)
>>
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>
>



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