This week I’m facing my—and many other lecturers’—least favorite part of teaching: grading exams. In a supreme act of procrastination I will continue the previous post, and the antepenultimate one, showing the code for a bivariate analysis of a randomized complete block design.

Just to recap, the results from the REML multivariate analysis (that used ASReml-R) was the following:

library(asreml)

m4 = asreml(cbind(bden, veloc) ~ trait,
            random = ~ us(trait):Block +  us(trait):Family, data = a,
            rcov = ~ units:us(trait))

summary(m4)$varcomp

#                                      gamma    component    std.error
#trait:Block!trait.bden:bden    1.628812e+02 1.628812e+02 7.854123e+01
#trait:Block!trait.veloc:bden   1.960789e-01 1.960789e-01 2.273473e-01
#trait:Block!trait.veloc:veloc  2.185595e-03 2.185595e-03 1.205128e-03
#trait:Family!trait.bden:bden   8.248391e+01 8.248391e+01 2.932427e+01
#trait:Family!trait.veloc:bden  1.594152e-01 1.594152e-01 1.138992e-01
#trait:Family!trait.veloc:veloc 2.264225e-03 2.264225e-03 8.188618e-04
#R!variance                     1.000000e+00 1.000000e+00           NA
#R!trait.bden:bden              5.460010e+02 5.460010e+02 3.712833e+01
#R!trait.veloc:bden             6.028132e-01 6.028132e-01 1.387624e-01
#R!trait.veloc:veloc            1.710482e-02 1.710482e-02 9.820673e-04
#                                  z.ratio constraint
#trait:Block!trait.bden:bden     2.0738303   Positive
#trait:Block!trait.veloc:bden    0.8624639   Positive
#trait:Block!trait.veloc:veloc   1.8135789   Positive
#trait:Family!trait.bden:bden    2.8128203   Positive
#trait:Family!trait.veloc:bden   1.3996166   Positive
#trait:Family!trait.veloc:veloc  2.7650886   Positive
#R!variance                             NA      Fixed
#R!trait.bden:bden              14.7057812   Positive
#R!trait.veloc:bden              4.3442117   Positive
#R!trait.veloc:veloc            17.4171524   Positive

The corresponding MCMCglmm code is not that different from ASReml-R, after which it is modeled anyway. Following the recommendations of the MCMCglmm Course Notes (included with the package), the priors have been expanded to diagonal matrices with degree of belief equal to the number of traits. The general intercept is dropped (-1) so the trait keyword represents trait means. We are fitting unstructured (us(trait)) covariance matrices for both Block and Family, as well as an unstructured covariance matrix for the residuals. Finally, both traits follow a gaussian distribution:

library(MCMCglmm)
bp = list(R = list(V = diag(c(0.007, 260)), n = 2),
          G = list(G1 = list(V = diag(c(0.007, 260)), n = 2),
                   G2 = list(V = diag(c(0.007, 260)), n = 2)))

bmod = MCMCglmm(cbind(veloc, bden) ~ trait - 1,
                random = ~ us(trait):Block + us(trait):Family,
                rcov = ~ us(trait):units,
                family = c('gaussian', 'gaussian'),
                data = a,
                prior = bp,
                verbose = FALSE,
                pr = TRUE,
                burnin = 10000,
                nitt = 20000,
                thin = 10)

Further manipulation of the posterior distributions requires having an idea of the names used to store the results. Following that, we can build an estimate of the genetic correlation between the traits (Family covariance between traits divided by the square root of the product of the Family variances). Incidentally, it wouldn’t be a bad idea to run a much longer chain for this model, so the plot of the posterior for the correlation looks better, but I’m short of time:

dimnames(bmod$VCV)

rg = bmod$VCV[,'veloc:bden.Family']/sqrt(bmod$VCV[,'veloc:veloc.Family'] *
                                  bmod$VCV[,'bden:bden.Family'])

plot(rg)

posterior.mode(rg)
#     var1
#0.2221953 

HPDinterval(rg, prob = 0.95)
#         lower     upper
#var1 -0.132996 0.5764006
#attr(,"Probability")
#[1] 0.95

And that’s it! Time to congratulate Jarrod Hadfield for developing this package.