Comments (7)
Hi @cperk: thanks for the report. If you share a reproducible example (with data and code) that demonstrates the issue I can investigate. The reprex R package may come in handy. -I
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Thanks!
Here is a reproducible example:
df1 <- data.frame(production=c(15,12,10,9,6,8,9,5,3,3,2,1,0,0,0,0), Treatment_Num=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4), Treatment=c("Fresh","Fresh","Fresh","Fresh","low","low","low","low","med","med","med","med","high","high","high","high"),Genotype=c(1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2), Source_Salinity=c("Fresh","Fresh","Brackish","Brackish","Fresh","Fresh","Brackish","Brackish","Fresh","Fresh","Brackish","Brackish","Fresh","Fresh","Brackish","Brackish"), Days_to_death <- c(500, 500, 500, 500, 400, 350, 300, 500, 200, 202, 260, 280, 150,150,160,140), censored <- c(1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0))
salmonella_fm <- production ~ Treatment
N_T_6_2020_nbn_toy <- brglm2::brnb(salmonella_fm,
data=df1,link = "log",transformation = "inverse", type = "AS_mean")
I figured out that it only happens when the fixed effect is categorical; when I use a numeric version of Treatment, the error disappears:
salmonella_fm <- production ~ Treatment_Num
N_T_6_2020_nbn_toy <- brglm2::brnb(salmonella_fm,
data=df1,link = "log",transformation = "inverse", type = "AS_mean")
In my real data it wouldn't be appropriate to convert Treatment to numeric, so I would really appreciate it if you could find a solution for using a categorical fixed effect!
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Any update on this?
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We are investigating the issue you opened. I understand that you may need a resolution quickly, but please note that we cannot guarantee the timeframe of response/resolution.
from brglm2.
Hi! I totally understand that you couldn't resolve this issue right away; are you any closer?
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any update?
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I am having the same issue.
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