When returning a GeneralizedLinearMixedModel result using julia_eval I get estimates in the subsequently saved .rds that are out of order with the labels in julia.
The data I am running is quite large and not easily shared. So I apologies for an incomplete example. The code:
julia_assign("m0form", formula(case ~ 0 + Analysisclass + (1|cropyear/individual_local_identifier)))
julia_command(sprintf('Jmodel = fit(GeneralizedLinearMixedModel, m0form, data, Bernoulli(), wts=float.(data.weights), contrasts= Dict(:Analysisclass => DummyCoding(base="aRice_Wet_day")))'))
produces:
But when I save it into R using:
juliamodel<- julia_eval("robject(:glmerMod, Tuple([Jmodel, data]));",need_return ="R")
I get:
That is a pretty serious discrepancy! Any recommendation on how to prevent this or identify the correct parameter order from the rds alone?
I ran this code in a loop across different datasets and saving only the .rds and not retaining a log to see the original Julia output.
When returning a GeneralizedLinearMixedModel result using julia_eval I get estimates in the subsequently saved .rds that are out of order with the labels in julia.
The data I am running is quite large and not easily shared. So I apologies for an incomplete example. The code:
produces:
But when I save it into R using:
I get:
That is a pretty serious discrepancy! Any recommendation on how to prevent this or identify the correct parameter order from the rds alone?
I ran this code in a loop across different datasets and saving only the .rds and not retaining a log to see the original Julia output.