[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?

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