Hi Jarrod, thanks for your reply.
I understand about the interval calculation now. However, I'm sorry
that I still don't see how to get the random effects myself. I see I
can get the posterior mean for fixed effects by mean(m2a.7$Sol[,1])
and HPDinterval(m2a.7$Sol[,1],prob=0.95) etc, I see there are data in
m2a.7$Sol in columns after the fixed effects in columns 5 through 96
for each of the days, but how do I reproduce this
G-structure: ~day
post.mean l-95% CI u-95% CI
day 0.09326 0.06076 0.1313
from m2a.7$Sol[,5:96]) ? I would like to do
mean(something)
and
HPDinterval(something, prob = 0.95)
So what is the "something" ?
Thanks again
Robert Long
Postgraduate student
University of Leeds / UK
On Mon, Aug 6, 2012 at 11:11 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
Hi,
specifying pr=TRUE in the call to MCMCglmm saves the posterior distribution
of all location effects (fixed and random). They appear in Sol.
summary uses HPDinterval not quantile. HPDinterval (with prob=0.95) finds
the shortest interval which contains 95% of the posterior samples, which may
be different from quantile which just finds the lowest and highest 2.5%.
Cheers,
Jarrod
Quoting Robert Long <longrob604 at gmail.com> on Mon, 6 Aug 2012 10:56:53
+0100:
Hello
I would like to extract the data for the posterior distribution for a
random effect in MCMCglmm. Using the example in the tutorial:
data(Traffic)
prior <- list(R = list(V = 1, nu = 0.002), G = list(G1 = list(V = 1,
nu = 0.002)))
m2a.7 <- MCMCglmm(y ~ year + limit + as.numeric(day), random = ~day,
family = "poisson", data = Traffic, prior = prior, verbose = FALSE, pr=T)
summary(m2a.7)
This gives:
G-structure: ~day
post.mean l-95% CI u-95% CI eff.samp
day 0.09326 0.06076 0.1313 266.8
How can I extract the data that gives this mean and 95% BCI ?
I can see that I can obtain the results for the fixed effects by such as:
mean(m2a.7$Sol[,1]) which gives the posterior mean for the first fixed
effect. But how can I do that for the random effects ? I can see that
there are data in m2a.7$Sol[,5:96] but these don't seem to be
variances as many are negative.
A related question is: quantile(m2a.7$Sol[,1],c(0.025,0.975),type = 1)
does not give precisely the same interval as in summary(m2a.7) - I
wonder why there is a difference ?
Thanks !
Robert Long
Postgraduate student
University of Leeds / UK