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I am evaluating how much the method B for data collection, predicts the method A.

It’s important to SAY that METHOD A has HIERARCHY data (on a scale ranging from 0 to 3) and METHOD B has data with CONTINUOUS VALUES.

Would anyone know how to use more than one random variable in? Because I would like to do a double nesting using as random variable both, landscape and informant.

Thanks

data_model1<-read.csv("test2.csv",h=T,sep=";")
str(baseline) 
library(ordinal)
baseline$method_A=factor(baseline$method_A, levels=0:3, ordered = T)
mod = clmm(method_A~method_B*species+(1|landscape/Informant), data=baseline, Hess=T, nAGQ=17)
summary(mod)
Informant laandscape species method_A method_B
Namea AA Cheracebus_lucifer 2 0.000
Namea AA Cuniculus_paca 3 0.000
Namea AA Dasyprocta_fuliginosa 1 0.000
Namea AA Dasypus_novemcinctus 2 0.000
Namea AA Didelphis_marsupialis 3 0.000
Namea AA Eira_barbara 2 0.552
Namea AA Hydrochoerus_hydrochaeris 1 0.004
Namea AA Lagothrix_lagotricha 2 0.000
Namea AA Leontocebus_nigricollis 2 0.035
Namea AA Leopardus_pardalis 2 0.000
Namea AA Lontra_longicaudis 2 0.000
Nameb AA Cheracebus_lucifer 1 0.004
Nameb AA Cuniculus_paca 1 0.000
Nameb AA Dasyprocta_fuliginosa 3 0.313
Nameb AA Dasypus_novemcinctus 1 1.030
Nameb AA Didelphis_marsupialis 0 0.000
Nameb AA Eira_barbara 0 0.000
Nameb AA Hydrochoerus_hydrochaeris 0 0.000
Nameb AA Lagothrix_lagotricha 3 0.004
Nameb AA Leontocebus_nigricollis 3 0.000
Nameb AA Leopardus_pardalis 2 0.000
Nameb AA Lontra_longicaudis 2 0.000
Namec AA Cheracebus_lucifer 2 0.000
Namec AA Cuniculus_paca 1 0.000
Namec AA Dasyprocta_fuliginosa 1 0.552
Namec AA Dasypus_novemcinctus 1 0.004
Namec AA Didelphis_marsupialis 2 0.000
Namec AA Eira_barbara 2 0.035
Namec AA Hydrochoerus_hydrochaeris 2 0.000
Namec AA Lagothrix_lagotricha 3 0.000
Namec AA Leontocebus_nigricollis 3 0.004
Namec AA Leopardus_pardalis 0 0.000
Namec AA Lontra_longicaudis 0 0.313
Named BB Cheracebus_lucifer 0 1.030
Named BB Cuniculus_paca 0 0.000
Named BB Dasyprocta_fuliginosa 3 0.000
Named BB Dasypus_novemcinctus 3 0.000
Named BB Didelphis_marsupialis 2 0.004
Named BB Eira_barbara 2 0.000
Named BB Hydrochoerus_hydrochaeris 2 0.552
Named BB Lagothrix_lagotricha 0 0.004
Named BB Leontocebus_nigricollis 0 0.000
Namee BB Cheracebus_lucifer 0 0.035
Namee BB Cuniculus_paca 0 0.000
Namee BB Dasyprocta_fuliginosa 3 0.000
Namee BB Dasypus_novemcinctus 3 0.004
Namee BB Didelphis_marsupialis 2 0.000
Namee BB Eira_barbara 2 0.313
Namee BB Hydrochoerus_hydrochaeris 2 1.030
Namee BB Lagothrix_lagotricha 2 0.000
Namee BB Leontocebus_nigricollis 2 0.000
Namef BB Cheracebus_lucifer 2 0.000
Namef BB Cuniculus_paca 2 0.004
Namef BB Dasyprocta_fuliginosa 2 0.000
Namef BB Dasypus_novemcinctus 2 0.000
Namef BB Didelphis_marsupialis 1 0.000
Namef BB Eira_barbara 1 0.552
Namef BB Hydrochoerus_hydrochaeris 1 0.004
Namef BB Lagothrix_lagotricha 1 0.000
Namef BB Leontocebus_nigricollis 3 0.035
Nameg CC Cheracebus_lucifer 0 0.000
Nameg CC Cuniculus_paca 3 0.000
Nameg CC Dasyprocta_fuliginosa 3 0.004
Nameg CC Dasypus_novemcinctus 3 0.000
Nameg CC Didelphis_marsupialis 3 0.313
Nameg CC Eira_barbara 3 1.030
Nameg CC Hydrochoerus_hydrochaeris 3 0.000
Nameg CC Lagothrix_lagotricha 2 0.000
Nameg CC Leontocebus_nigricollis 2 0.000
Nameh CC Cheracebus_lucifer 2 0.004
Nameh CC Cuniculus_paca 2 0.000
Nameh CC Dasyprocta_fuliginosa 2 0.552
Nameh CC Dasypus_novemcinctus 2 0.004
Nameh CC Didelphis_marsupialis 2 0.000
Nameh CC Eira_barbara 2 0.035
Nameh CC Hydrochoerus_hydrochaeris 1 0.000
Nameh CC Lagothrix_lagotricha 1 0.000
Nameh CC Leontocebus_nigricollis 1 0.004
Namei CC Cheracebus_lucifer 1 0.000
Namei CC Cuniculus_paca 2 0.313
Namei CC Dasyprocta_fuliginosa 2 1.030
Namei CC Dasypus_novemcinctus 3 0.000
Namei CC Didelphis_marsupialis 3 0.000
Namei CC Eira_barbara 3 0.000
Namei CC Hydrochoerus_hydrochaeris 3 0.004
Namei CC Lagothrix_lagotricha 3 0.000
Namei CC Leontocebus_nigricollis 2 0.313
Namej DD Dasypus_novemcinctus 2 1.030
Namej DD Didelphis_marsupialis 2 0.000
Namej DD Eira_barbara 2 0.000
Namej DD Hydrochoerus_hydrochaeris 2 0.000
Fran Braga
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  • If I recall correctly, the `ordinal` package can't fit more than one random effect. I believe it is possible in other packages such as `brms` and `mgcv` – sjp May 27 '20 at 22:02
  • Thanks.. I will try these other packages :) – Fran Braga Jun 04 '20 at 14:15
  • The new problem now is that when I got a warning message in the model summary and many summary statistics are missing. Why is that? Estimate Std. Error z value 0|1 -2.7654 NA NA 1|2 -0.6467 NA NA 2|3 1.0517 NA NA – Fran Braga Jul 06 '20 at 12:45

0 Answers0