[R-sig-ME] Interpreting a MCMCglmm model with a bivariate response variable
Jarrod Hadfield
j.hadfield at ed.ac.uk
Wed Sep 23 16:38:47 CEST 2015
Hi,
a) Section 8 of the course notes details the use of 'triat'.
b) DIC is not implemented for multivariate threshold models. DIC, as
its currently `focussed' is not useful anyway, I think.
Cheers,
Jarrod
The muklQuoting Iker Vaquero Alba <karraspito at yahoo.es> on Wed, 23 Sep
2015 14:32:00 +0000 (UTC):
>
> Hi, Jarrod and everyone else.
> Thank you very much for your advise. Actually, I just realized
> after sending the email, I was going to send the correct one. The
> model:
> testmodel1<-MCMCglmm(cbind(natapshort,nataplong)~gender+age+religion+sexor+selfattr+partnerattr+gender:age+gender:religion+gender:sexor+gender:selfattr+gender:partnerattr+age:religion+age:sexor+age:selfattr+age:partnerattr+religion:sexor+religion:selfattr+religion:partnerattr+sexor:selfattr+sexor:partnerattr+selfattr:partnerattr,random=NULL,rcov=~corg(trait):units,family=c("threshold","threshold"),data=extphen,nitt=100000,singular.ok=TRUE)
> And the summary outcome:
> summary(testmodel1)
>
> Iterations = 3001:99991
> Thinning interval = 10
> Sample size = 9700
>
> DIC:
>
> R-structure: ~corg(trait):units
>
> post.mean l-95% CI u-95% CI eff.samp
> natapshort:natapshort.units 1.0000 1.0000 1.0000 0
> nataplong:natapshort.units 0.3469 0.2243 0.4744 7885
> natapshort:nataplong.units 0.3469 0.2243 0.4744 7885
> nataplong:nataplong.units 1.0000 1.0000 1.0000 0
>
> Location effects: cbind(natapshort, nataplong) ~ gender + age +
> religion + sexor + selfattr + partnerattr + gender:age +
> gender:religion + gender:sexor + gender:selfattr +
> gender:partnerattr + age:religion + age:sexor + age:selfattr +
> age:partnerattr + religion:sexor + religion:selfattr +
> religion:partnerattr + sexor:selfattr + sexor:partnerattr +
> selfattr:partnerattr
>
> post.mean l-95% CI u-95% CI eff.samp pMCMC
> (Intercept) 3.170e+00 -1.940e+00 8.301e+00 8794 0.21897
> genderM -1.048e-01 -2.006e+00 1.915e+00 9408 0.90948
> genderO -5.316e+02 -1.827e+05 1.854e+05 9864 0.99052
> age -1.006e+00 -3.414e+00 1.266e+00 8847 0.39959
> religionY -2.823e+00 -5.121e+00 -5.111e-01 8322 0.01691 *
> sexorHOM 1.338e+03 -1.663e+05 1.738e+05 9700 0.99608
> sexorOT 8.310e+02 -1.580e+05 1.586e+05 9700 0.99732
> selfattr -8.544e-01 -2.236e+00 5.512e-01 9092 0.22454
> partnerattr 4.296e-01 -9.066e-01 1.784e+00 8818 0.52598
> genderM:age 3.254e-02 -4.556e-01 5.027e-01 9700 0.88557
> genderO:age 2.113e+02 -7.761e+04 7.336e+04 9700 0.99918
> genderM:religionY -4.660e-01 -1.141e+00 2.360e-01 9503 0.17258
> genderO:religionY 3.408e+02 -1.551e+05 1.567e+05 9700 0.99485
> genderM:sexorHOM -7.522e-01 -1.687e+00 1.528e-01 9700 0.11155
> genderO:sexorHOM 8.643e+01 -1.468e+05 1.512e+05 9700 0.99258
> genderM:sexorOT -5.950e-01 -1.661e+00 4.325e-01 9326 0.26742
> genderO:sexorOT 6.955e+02 -1.589e+05 1.664e+05 9700 0.99340
> genderM:selfattr 3.038e-01 -1.054e-01 6.976e-01 9334 0.13526
> genderO:selfattr -1.968e+02 -7.999e+04 7.118e+04 9341 0.99340
> genderM:partnerattr -2.686e-01 -6.974e-01 1.595e-01 9345 0.20866
> genderO:partnerattr 1.064e+00 -1.365e+00 3.685e+00 8894 0.41155
> age:religionY 3.473e-01 -3.822e-01 1.100e+00 9215 0.34763
> age:sexorHOM -6.704e+02 -8.690e+04 8.316e+04 9700 0.99608
> age:sexorOT -4.160e+02 -7.930e+04 7.899e+04 9700 0.99732
> age:selfattr 3.904e-01 -2.322e-01 9.908e-01 9257 0.20351
> age:partnerattr -5.805e-02 -6.213e-01 5.200e-01 8974 0.85155
> religionY:sexorHOM 3.131e-01 -8.014e-01 1.381e+00 9700 0.57505
> religionY:sexorOT -3.484e-03 -1.612e+00 1.651e+00 7850 0.99278
> religionY:selfattr 1.074e-01 -3.688e-01 5.790e-01 8506 0.65649
> religionY:partnerattr 5.330e-01 1.455e-01 8.974e-01 8817 0.00495 **
> sexorHOM:selfattr 3.787e-01 -3.850e-01 1.134e+00 9101 0.31588
> sexorOT:selfattr 3.805e-01 -2.086e-01 9.754e-01 9163 0.20866
> sexorHOM:partnerattr 4.977e-01 -2.861e-01 1.323e+00 8472 0.22639
> sexorOT:partnerattr -7.482e-02 -6.965e-01 5.284e-01 9023 0.80000
> selfattr:partnerattr 5.051e-02 -1.438e-01 2.540e-01 8700 0.62041
> ---
> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>
> Cutpoints:
> post.mean l-95% CI u-95% CI eff.samp
> cutpoint.traitnatapshort.1 1.024 0.7313 1.335 872.3
> cutpoint.traitnatapshort.2 2.362 2.0725 2.698 756.2
> cutpoint.traitnatapshort.3 4.002 3.6299 4.396 884.4
> cutpoint.traitnataplong.1 1.187 0.9010 1.489 828.4
> cutpoint.traitnataplong.2 2.496 2.1718 2.812 727.6
> cutpoint.traitnataplong.3 3.959 3.5890 4.350 872.9
> Some additional questions, if that's fine:
> 1. Do I have to actually include the word 'trait' in the model,
> something like 'cbind(natapshort, nataplong) ~ trait + gender +
> age..."? What is the reason for that? Also, do I have to include
> interactions between 'trait' and other variables?
> 2. Why the summary of my model does not give me the DIC value? It
> appears empty.
>
> Thank you very much.
> Iker
>
>
> __________________________________________________________________
>
> Iker Vaquero-Alba
> Visiting Postdoctoral Research Associate
> Laboratory of Evolutionary Ecology of Adaptations
> Joseph Banks Laboratories
> School of Life Sciences
> University of Lincoln Brayford Campus, Lincoln
> LN6 7DL
> United Kingdom
>
> https://eric.exeter.ac.uk/repository/handle/10036/3381
>
>
> De: Jarrod Hadfield <j.hadfield at ed.ac.uk>
> Para: Iker Vaquero Alba <karraspito at yahoo.es>
> CC: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org>
> Enviado: Miércoles 23 de septiembre de 2015 15:25
> Asunto: Re: [R-sig-ME] Interpreting a MCMCglmm model with a
> bivariate response variable
>
> Hi Iker,
>
> You need to follow the advice given to your previous post. With
> unconstrained residual variance the model is largely generating
> nonsense. Use `corg' instead of `us'. Also, depending on what the
> outcomes are you almost certainly need to have `trait' in the fixed
> effect specification.
>
> Jarrod
>
>
>
>
> Quoting Iker Vaquero Alba <karraspito at yahoo.es> on Wed, 23 Sep 2015
> 13:28:09 +0000 (UTC):
>
>
>
>>
>> Hello all,
>> I am implementing a model with a multiple (bivariate) response
>> variable using MCMCglmm. Both response variables and all the
>> explanatory variables are categorical variables, with between 2 and
>> 6 levels. The model is as follows:
>>
>> testmodel1<-MCMCglmm(cbind(natapshort,nataplong)~gender+age+religion+sexor+selfattr+partnerattr+gender:age+gender:religion+gender:sexor+gender:selfattr+gender:partnerattr+age:religion+age:sexor+age:selfattr+age:partnerattr+religion:sexor+religion:selfattr+religion:partnerattr+sexor:selfattr+sexor:partnerattr+selfattr:partnerattr,random=NULL,rcov=~us(trait):units,family=c("threshold","threshold"),data=extphen,nitt=100000,singular.ok=TRUE)
>> And this is the summary of the model after all the iterations:
>>
>> summary(testmodel1) Iterations =3001:99991 Thinninginterval =
>> 10 Sample size = 9700 DIC: R-structure:
>> ~us(trait):units post.mean l-95% CI u-95%
>> CI eff.sampnatapshort:natapshort.units 114108 992.1 213000
>> 7.105nataplong:natapshort.units 33245 310.8 66300
>> 4.656natapshort:nataplong.units 33245 310.8 66300
>> 4.656nataplong:nataplong.units 82964 671.5 155869
>> 2.218 Location effects:cbind(natapshort, nataplong) ~ gender + age
>> + religion + sexor + selfattr +partnerattr + gender:age +
>> gender:religion + gender:sexor + gender:selfattr +gender:partnerattr
>> + age:religion + age:sexor + age:selfattr + age:partnerattr+
>> religion:sexor + religion:selfattr + religion:partnerattr +
>> sexor:selfattr +sexor:partnerattr + selfattr:partnerattr
>> post.mean l-95% CI u-95% CI eff.samp
>> pMCMC (Intercept) 8.934e+02 -6.995e+02 2.596e+03
>> 275.73 0.22660 genderM -8.936e+01 -7.437e+0
>> 2 5.066e+02 4236.67 0.75794 genderO -1.292e+03
>> -1.934e+05 1.765e+05 9700.00 0.99052
>> age -3.493e+02 -1.170e+03 3.615e+02 505.92
>> 0.31918 religionY -7.361e+02 -1.598e+03 1.481e+01
>> 33.71 0.03402 * sexorHOM -1.235e+03
>> -1.808e+05 1.679e+05 9700.00 0.99814 sexorOT
>> 2.193e+03 -1.589e+05 1.687e+05 10583.09 0.97814
>> selfattr -2.367e+02 -7.424e+02 1.706e+02 314.82
>> 0.24948 partnerattr 1.391e+02 -2.667e+02 6.147e+02
>> 966.40 0.49546 genderM:age 2.696e+01 -1.313e+02
>> 1.748e+02 3786.10 0.69670 genderO:age -1.055e+04
>> -8.325e+04 7.163e+04 1722.63 0.78474
>> genderM:religionY -1.295e+02 -3.725e+02 8.194e+01 200.08
>> 0.20495 genderO:religionY -1.016e+04 -1.589e+05 1.505e+05
>> 8731.86 0.89052 genderM:sexorHOM -2.245e+02
>> -5.713e+02 4.443e+01 105.67 0.10495
>> genderO:sexorHOM -8.104e+03 -1.620e+05 1.385e+05 5318.22
>> 0.90474 genderM:sexorOT -1.52
>> 0e+02 -5.124e+02 1.856e+02 423.76 0.33402
>> genderO:sexorOT 2.628e+03 -1.654e+05 1.658e+05 9700.00
>> 0.97670 genderM:selfattr 9.029e+01 -3.152e+01 2.334e+02
>> 119.78 0.12907 genderO:selfattr 6.281e+03
>> -6.511e+04 8.524e+04 3504.67 0.88412
>> genderM:partnerattr -7.284e+01 -2.160e+02 6.729e+01 263.29
>> 0.25052 genderO:partnerattr 2.536e+02 -5.113e+02 1.121e+03
>> 1291.76 0.49113 age:religionY 8.732e+01
>> -1.283e+02 3.457e+02 727.46 0.42289
>> age:sexorHOM 2.809e+02 -8.592e+04 8.847e+04 9700.00
>> 0.99711 age:sexorOT -1.246e+03 -8.447e+04 7.941e+04
>> 9370.57 0.97526 age:selfattr 1.195e+02
>> -6.636e+01 3.452e+02 212.35 0.19567
>> age:partnerattr -8.598e+00 -1.963e+02 1.714e+02 9700.00
>> 0.92227 religionY:sexorHOM 8.506e+01 -2.392e+02 4.612e+02
>> 2059.92 0.59959 religionY:sexorOT 1.420e+01
>> -5.170e+02 5.464e+02 9700.00 0.96268
>> religionY:selfattr 2.782e+01 -1.198e+02 1.833e+02 3520.80
>> 0.68701 religionY:part
>> nerattr 1.407e+02 1.423e+01 2.886e+02 22.99 0.00928
>> **sexorHOM:selfattr 1.160e+02 -1.141e+02 3.707e+02 394.74
>> 0.28495 sexorOT:selfattr 1.006e+02 -8.528e+01 3.050e+02
>> 305.50 0.24577 sexorHOM:partnerattr 1.231e+02
>> -1.246e+02 3.990e+02 415.43 0.31072
>> sexorOT:partnerattr -1.401e+00 -2.007e+02 1.956e+02 9700.00
>> 0.99237 selfattr:partnerattr 5.483e+00 -6.017e+01 7.207e+01
>> 3007.45 0.85464 ---Signif. codes: 0?***? 0.001 ?**? 0.01 ?*? 0.05
>> ?.? 0.1 ? ? 1 Cutpoints: post.mean l-95%
>> CI u-95% CI eff.sampcutpoint.traitnatapshort.1 235.2 62.34
>> 376.2 8.822cutpoint.traitnatapshort.2 633.4 202.16
>> 944.0 3.578cutpoint.traitnatapshort.3 1139.5 364.35
>> 1683.7 5.832cutpoint.traitnataplong.1 293.4 54.85
>> 433.7 5.203cutpoint.traitnataplong.2 651.8 223.70
>> 961.6 2.604cutpoint.traitnataplong.3 1023.1 344.82
>> 1483.0 2.353
>>
>> So, my question is: in that summary, where are the effect sizes, are
>> they the "post. mean" column? And have they been transformed in some
>> way? Because obviously, for response variables that can only take
>> values 1,2,3,4 or 5, I would expect to see those as the effect size.
>> Also, is there any way of knowing to what extent are those results
>> due to each specific response variable, and the degree of covariance
>> between both? Is it possible to get all that information from that
>> summary output I have copied above?
>> Thank you very much. Iker
>>
>> __________________________________________________________________
>>
>> Iker Vaquero-Alba
>> Visiting Postdoctoral Research Associate
>> Laboratory of Evolutionary Ecology of Adaptations
>> Joseph Banks Laboratories
>> School of Life Sciences
>> University of Lincoln Brayford Campus, Lincoln
>> LN6 7DL
>> United Kingdom
>>
>> https://eric.exeter.ac.uk/repository/handle/10036/3381
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
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