Dear list members,
I am evaluating subject choices between three possible responses in
three conditions.
Each of 10 subject made the choice in six variations in the three
conditions. I therefore want to include subject-offsets in my model,
as well as variation-offsets (variations are repeated between
conditions). I also factor out condition-specific subject
preferences, even though this has little effect on the model outcomes.
I have had some success modeling the outcome with MCMCglmm, but, to
my surprise, the outcome of the MCMCglmm model depends on the
ordering of the outcome factor (unordered in my data frame). below
are the summaries of two (converged) MCMCglmm models that I expected
to give me the same results: note that the calls are the same, as
are the data frames.
Any insights why this happens, if there are more appropriate
packages for my purposes, or how to better interpret my results
would be greatly appreciated.
Thank you, list!
Christoph
ps: since I am new to the list, i do not know if I can attach code
or data frame files? any advice there, too, would be appreciated!
pps: these are my data, script, and summaries:
data frame summary:
'data.frame': 154 obs. of 4 variables:
$ subject : Factor w/ 10 levels "CC","ChDe","CiDe",..: 1 1 1 1 1 1
1 1 1 1 ...
$ theme : Factor w/ 12 levels "sonnenaufgang",..: 12 12 12 11 11
11 10 10 10 9 ...
$ condition: Factor w/ 3 levels "base","test1",..: 2 1 3 2 1 3 2 1 3 2 ...
$ choice : Factor w/ 3 levels "a","b","c": 1 3 1 2 2 1 2 1 1 2 ?
subject theme condition choice
5 CC bauernhof test1 a
9 CC bauernhof base c
14 CC bauernhof test2 a
19 CC buffet test1 b
with(christerwi.data, table(condition, choice))
choice
condition a b c
base 24 16 13
test1 30 10 6
test2 26 24 5
# for prior specification
k <- 3
I <- diag(k-1)
J <- matrix(rep(1, (k-1)^2), c(k-1, k-1))
# here is the model specification:
mcmcglmm.christerwi<-MCMCglmm(fixed=choice~condition, random =
~idh(condition):subject+subject+theme,data = christerwi.data,rcov =
~us(trait):units,
family = "categorical",nitt =
200000,burnin = 5000,thin=150,singular.ok=TRUE, verbose=FALSE,
prior=list(R = list(fix=1,V = (1/3)*(I+J)),
G = list(G1 = list(V =
diag(3)*0.2,nu = 1),
G2 = list(V = 0.2, nu = 1),
G3 = list(V = 0.2,
nu = 1))))
autocorrelation(mcmcglmm.christerwi$Sol) shows that the
autocorrelation between successive draws with this combination of
'nitt', 'burnin', and 'thin' is below 0.1. :
, , (Intercept)
(Intercept) conditiontest1 conditiontest2
Lag 0 1.000000000 -0.610678249 -0.65585856
Lag 100 0.030298125 -0.031276267 -0.01295414 (truncated)
# the same is true of all VCV draws.
# and now the summary indicates that the distribution differences
are not significant between the various levels:
summary(mcmcglmm.christerwi)
?
Location effects: choice ~ condition
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) -0.57857 -1.67346 0.43472 1527 0.2290
conditiontest1 -1.03532 -2.33849 0.02690 1456 0.0634 .
conditiontest2 -0.05335 -1.08068 1.10419 1333 0.8841
# NOW BUILD MODEL ON REORDERED OUTCOME VARIABLE:
christerwi.data$choice<-relevel(christerwi.data$choice, ref='c')
with(christerwi.data, table(condition, choice))
choice
condition c a b
base 13 24 16
test1 6 30 10
test2 5 26 24
# now running the same model as above on this data:
mcmcglmm.christerwi.c<-MCMCglmm(fixed=choice~condition, random =
~idh(condition):subject+subject+theme,data = christerwi.data,rcov =
~us(trait):units,
family = "categorical",nitt =
300000,burnin = 10000,thin=200,singular.ok=TRUE, verbose=FALSE,
prior=list(R = list(fix=1,V = (1/3)*(I+J)),
G = list(G1 = list(V =
diag(3)*0.2,nu = 1),
G2 = list(V =
0.2, nu = 1),
G3 = list(V =
0.2, nu = 1))))
# after again verifying the low autocorrelation between successive
draws, I check the summary and find this:
summary(mcmcglmm.christerwi.c)
Location effects: choice ~ condition
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 1.03562 -0.56164 2.78721 1047.5 0.183
conditiontest1 1.47854 -1.01625 3.61806 696.1 0.139
conditiontest2 1.75178 -0.08553 3.60983 1322.5 0.051 .