Trying to fit a GLMM that incorporates random effects for isofemale and population, and includes fixed effects and interactions that test:
I also need to use an 'individual level random effect' which is included in my code:
datheat$replicateID = factor(1:nrow(datheat))
mod = glmer(Survival ~ sex + region + treatment + treatment*region +
treatment*sex + sex*region + (1|isofemale) + (1|population) +
(1|replicateID), data = datheat, family =
binomial)
summary(mod)
However, I keep getting this error and I'm not sure how to fix it:
Error in eval(family$initialize, rho) : y values must be 0 <= y <= 1
Here is a sample of my data (datheat):
note abbreviated column names (pop
=population
, iso
= isofemale
, S*
=Survival
, X
=X..<something>
)
region pop treatment iso sex rep n S* X proportion
1 Southwest CAJ hardening D1 Females 1 10 2 20 0.2
2 Southwest CAJ hardening D1 Females 2 10 1 10 0.1
3 Southwest CAJ hardening D1 Females 3 10 5 50 0.5
32 Southwest REG hardening R4 Females 1 10 3 30 0.3
33 Southwest REG hardening R4 Females 2 10 1 10 0.1
34 Southwest REG hardening R4 Females 3 10 3 30 0.3
60 Southwest REG hardening Southwest2 Females 1 10 5 50 0.5
61 Southwest REG hardening Southwest2 Females 2 10 3 30 0.3
62 Southwest REG hardening Southwest2 Females 3 10 0 0 0
74 Southwest PAV hardening Pa1 Females 1 10 2 20 0.2
75 Southwest PAV hardening Pa1 Females 2 10 3 30 0.3
76 Southwest PAV hardening Pa1 Females 3 10 4 40 0.4
136 Southwest CAN hardening C2 Females 1 10 0 0 0
137 Southwest CAN hardening C2 Females 2 10 1 10 0.1
138 Southwest CAN hardening C2 Females 3 10 0 0 0
Thanks!
The ?binomial
help page (in base R) explains how to specify binomial responses. Assuming that the n
column in your data set is the number of individuals in each trial, you either need to specify the number of 'successes' and 'failures' as the columns of a two-column matrix:
glmer(cbind(Survival, n-Survival) ~ ..., ...)
or specify the proportion surviving and give the denominator (total number exposed) as the weights
argument:
glmer(proportion ~ ..., ..., weights = n)
While the former is more common in R examples, I prefer the latter (but the answers should be identical).