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Heritability of ordinal data in MCMCglmm and estimating fixed effects
3 messages · Samantha Patrick, David Duffy
1 day later
On Tue, 30 Oct 2012, Samantha Patrick wrote:
Hi I am estimating the heritability of an ordinal trait using MCMCglmm and have come across two problems: one regarding heritability and one specific to ordinal data sets. _My data_ Trait1 = ordinal score from 0 - 4 Colony = factor with 2 options ID = repeated measures per individual (818 individuals) So the heritability: /posterior.heritability1.1 <- model2.1$VCV[, "animal"]/(model2.1$VCV[, "animal"]+ model2.1$VCV[, "ID"] + model2.1$VCV[, "units"]+1)/ /posterior.mode(posterior.heritability1.1)/ /h^2 = 0.21 (0.05- 0.45)/
Why are there repeated measures? Is the between-occasion variation of interest, or a nuisance? That is, is animal/(animal+units) a better measure of h2?
/model2.2<-MCMCglmm(Trait1~ Colony , random =~animal + BYEAR + MOTHER + ID, pedigree = Ped3, data = Data, prior = prior2.1, family='ordinal',burnin = 20000, nitt = 500000, thin = 200, pr=TRUE)/
doesn't work. How many levels of BYEAR, how many obs per year, do you want a random regression on BYEAR (ie do you expect a linear relationship?)
My second question is specific to ordinal analyses I need to extract one score per individual and I wondered if anyone knows if there is any methods for doing this? I can fit ID as a random effect but I am not sure this changes anything, and is associated with the curse of BLUPS.
BLUPs are what you want, curse them ;)
| 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
Hi David Le 31/10/2012 05:49, David Duffy a ?crit :
On Tue, 30 Oct 2012, Samantha Patrick wrote:
Hi I am estimating the heritability of an ordinal trait using MCMCglmm and have come across two problems: one regarding heritability and one specific to ordinal data sets. _My data_ Trait1 = ordinal score from 0 - 4 Colony = factor with 2 options ID = repeated measures per individual (818 individuals) So the heritability: /posterior.heritability1.1 <- model2.1$VCV[, "animal"]/(model2.1$VCV[, "animal"]+ model2.1$VCV[, "ID"] + model2.1$VCV[, "units"]+1)/ /posterior.mode(posterior.heritability1.1)/ /h^2 = 0.21 (0.05- 0.45)/
Why are there repeated measures? Is the between-occasion variation of interest, or a nuisance? That is, is animal/(animal+units) a better measure of h2?
>> There are between 1-4 measures of Trait 1 per individual. I have run the model using only the first measure per individual (as in h2 = animal/(animal+units)). It has little effect on the heritability. I have kept in the repeated measures as they allow as to calculate consistent environmentally induced differences ( see http://www.wildanimalmodels.org/tiki-index.php?page=repeated%20measures), and as I understand, it is better to use the full data set and control for any non independence.
/model2.2<-MCMCglmm(Trait1~ Colony , random =~animal + BYEAR + MOTHER + ID, pedigree = Ped3, data = Data, prior = prior2.1, family='ordinal',burnin = 20000, nitt = 500000, thin = 200, pr=TRUE)/
doesn't work.
>>> In what way? Sorry I don't quite understand this...
How many levels of BYEAR, how many obs per year, do you want a random regression on BYEAR (ie do you expect a linear relationship?)
>>> BYEAR is a factor with 27 levels; a quick summary observations per level: 0-5 obs = 3 levels 5-10 obs = 2 levels 20-50 obs = 10 levels 50-85 obs = 12 levels There is no reason to suppose it would be a linear relationship; instead it is likely to represent cohort effects as a result of similarities between birds born in the same year so I have not tried to fit it as a random regression.
My second question is specific to ordinal analyses I need to extract one score per individual and I wondered if anyone knows if there is any methods for doing this? I can fit ID as a random effect but I am not sure this changes anything, and is associated with the curse of BLUPS.
BLUPs are what you want, curse them ;)
>>> The problem is for Gaussian data, Individual would be fitted as a fixed effect in the model, such that in its simplest form the model would be: Trait1~ Colony + ID and then the parameter estimates are extracted for ID and these are used as the individual measures. This does not involve taking residuals from the model so seems to be statistically more sound than using BLUPs. While the two are normally highly correlated, there are differences and it seems unwise to go back to a method that has received much criticism. Many thanks for your comments Sam
Dr Samantha Patrick Post Doctoral Fellow Centre d'Etudes Biologiques de Chiz? - CNRS 79360 Villiers-en-Bois France T:+33 5 49 09 78 46 M:+33 6 75 06 34 51 Skype: sammy_patrick http://www.cebc.cnrs.fr/Fidentite/patrick/patrick.htm http://www.researchgate.net/profile/Samantha_Patrick/