Here's the model setup:
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data1.proc <- process.data(data1, model="POPAN", begin.time = 2000, groups = c("Var3", "Var2", "Var1"))
data1.ddl <- make.design.data(data1.proc)
data1.analysis=function(){
Phi.Var1 = list(formula= ~ -1 + Var1,link="sin")
pent.1 = list(formula = ~1)
p.Var3 = list(formula= ~ Var3)
N.Var2.x.Var3 = list(formula = ~ Var2*Var3)
cml=create.model.list("POPAN")
mark.wrapper(cml,data=data1.proc, ddl=data1.ddl)}
data1.results <- data1.analysis()
To look at results, I did this, which gave me a total of 12 values. The majority of phis was unique to Var1. However, a single level of Var1 had two different values of phi produced.
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temp <- model.average(data1.results, "Phi", vcv=TRUE)$estimates
temp <- unique(select(temp, estimate, lcl, ucl, Var1))
temp <- unique(temp)
dim(temp)
This did not happen if I simply looked at
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data1.results[[1]]$results$real
I wanted to use model.average, since my original code includes many models. What could be the problem here?
Thank you!