data <- read.csv("NND_NNID_ALL_DATA.csv", header=TRUE, sep=",")

library(blme)
library(lme4)
library(glmmTMB)
library(lmerTest)
library(effects)

#Mean NND All
model1<- blmer(Mean_NND_All_Neighbours~Treatment*Age+(1|Group/Focal.ID), data=data) 
summary(model1)

#Age Interaction Model Comparison 
nullNND <- blmer(Mean_NND_All_Neighbours~Treatment+Age+(1|Group/Focal.ID), data=data)
anova(model1, nullNND)

#Age Only Model Comparison
nullNND2 <- blmer(Mean_NND_All_Neighbours~Treatment+(1|Group/Focal.ID), data=data)
anova(model1, nullNND2)

#Treatment Only Model Comparison
nullNND3 <- blmer(Mean_NND_All_Neighbours~Age+(1|Group/Focal.ID), data=data)
anova(model1, nullNND3)
summary(model1)

#Mean NND Adults Only 
adultmodel <- lmer(Mean_NND_Adult_Neighbours~Treatment*Age +(1|Group/Focal.ID), data=data)
summary(adultmodel)

#NNID Proportion Adult Dominant 
#Adjust data to remove exact zeroes and ones 
data$Proportion_Adult_Dominant[which(data$Proportion_Adult_Dominant == "0")] <- 0.00001
data$Proportion_Adult_Dominant[which(data$Proportion_Adult_Dominant == "1")] <- 0.99999
adult_dom_model <- glmmTMB(Proportion_Adult_Dominant~Treatment*Age+(1|Group/Focal.ID), family= beta_family(link = "logit"), data=data) 
summary(adult_dom_model)

#NNID Proportion Adult
#Adjust data to remove exact zeroes and ones 
data$Proportion_Adult[which(data$Proportion_Adult == "0")] <- 0.00001
data$Proportion_Adult[which(data$Proportion_Adult == "1")] <- 0.99999
NNID_adult_model <- glmmTMB(Proportion_Adult~Treatment*Age+(1|Group/Focal.ID), family= beta_family(link = "logit"), data=data) 
summary(NNID_adult_model)

#NNID Proportion Adult Female
#Adjust data to remove exact zeroes and ones 
data$Proportion_Adult_Female[which(data$Proportion_Adult_Female == "0")] <- 0.00001
data$Proportion_Adult_Female[which(data$Proportion_Adult_Female == "1")] <- 0.99999
adult_female_model <- glmmTMB(Proportion_Adult_Female~Treatment*Age+(1|Group/Focal.ID), family= beta_family(link = "logit"), data=data) 
summary(adult_female_model)