[R-sig-ME] MCMCglmm with missing values
Monica Anderson Berdal
m@@nder@on@berd@| @end|ng |rom gm@||@com
Mon Mar 16 11:24:03 CET 2020
I’m trying to run a MCMCglmm where two of the response variables and three
of the fixed effects are sex specific. Each line represents one individual,
and the data looks like this:
Sex
Fitness_M
Fitness_F
Age_M
Age_F
Mate_mass
Behaviour
Mass
Temperature
Line
M
743
NA
140
NA
NA
69
229
21
X
M
515
NA
188
NA
NA
50
220
19
X
M
390
NA
231
NA
NA
50
109
17
Y
M
266
NA
960
NA
NA
39
113
19
Y
M
323
NA
105
NA
NA
55
171
25
Y
F
NA
112
NA
74
205
67
247
20
Y
F
NA
107
NA
74
163
60
139
26
Z
F
NA
8
NA
53
193
71
118
24
Z
F
NA
207
NA
55
74
37
219
21
Z
F
NA
300
NA
56
160
68
261
18
Z
All individuals have a behaviour and mass recorded, and only the sex
specific variables have NAs. I’ve been using the at.level function to
control for these missing values, but still get the error: Missing values
in the fixed predictors
cricketMCMC <- MCMCglmm(cbind(Fitness_F, Fitness_M, Behaviour, Mass)~
at.level(trait, 1):(Age_F + Mate_mass) +
at.level(trait, 2):(Age_M) +
at.level(trait, 3):(Sex + Temperature) +
at.level(trait, 4):(Sex + Temperature),
random=~us(trait):Line,rcov=~us(trait):units,
family=rep("gaussian",4), nitt=260000, thin=200,
burnin=60000,
verbose=FALSE, prior=prior.4t, pr=TRUE, data=Data)
To avoid the issues of missing values I added mean values for Age_F and
Mate_mass for the males and Age_M for the females, while still using the
at.level function. The idea was to overcome the problem with the missing
values while also ignoring the mean values for the sex specific traits.
This model ran without problems, and to see if the model ran properly, I
compared it to a second model where I added a random value instead of mean
values. I used set.seed() before running both models, but the outputs are
not the same, which means that the added values are still affecting the
results.
How can I avoid the problems of NAs when running an MCMCglmm on a data set
with this structure?
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list