## R-Script of manuscript titled "Does novelty influence foraging decision of a scavenger?" by Bhattacharjee et al. 2023

# set directory 
setwd()

# Packages required
library(lme4)
library(lmerTest)
library(performance)
library(DHARMa)
library(lmtest)

## See Raw data_Bhattacharjee et al. 2023 to extract the data for analyses.
#-----------------------------------------------------------------------------------------------------------------------
### Object Choice
#-----------------------------------------------------------------------------------------------------------------------

## GB-BP Condition

# Binomial test to check object choice (Including both age classes)
# 41 dogs chose GB out of 63.
binom.test(41, 63)

# GLM to check influence of age class (and sex) on object choice
gbbpinspect <- read.csv("gbbpinspect.csv")
lrfit01 <- glm(inspect ~ age + sex, data = gbbpinspect, family = binomial(link = "logit"))
summary(lrfit01)

# Model diagnostics
DHARMa::testResiduals(lrfit01)

#-----------------------------------------------------------------------------------------------------------------------
## GB-TP Condition

# Binomial test to check object choice (Including both age classes)
# 45 dogs chose GB out of 60.
binom.test(42, 60)

# GLM to check influence of age class (and sex) on object choice
gbtpinspect <- read.csv("gbtpinspect.csv")
lrfit02 <- glm(inspect ~ age + sex, data = gbtpinspect, family = binomial(link = "logit"))
summary(lrfit02)

# Model diagnostics
DHARMa::testResiduals(lrfit02)

#-----------------------------------------------------------------------------------------------------------------------

## BP-TP Condition

# Binomial test to check object choice (Including both age classes)
# 34 dogs chose BP out of 59.
binom.test(34, 59)

# GLM to check influence of age class (and sex) on object choice
bptpinspect <- read.csv("bptpinspect.csv")
lrfit03 <- glm(inspect ~ age + sex, data = bptpinspect, family = binomial(link = "logit"))
summary(lrfit03)

# Model diagnostics
DHARMa::testResiduals(lrfit03)

#-----------------------------------------------------------------------------------------------------------------------

## GB-GBB Condition

# Binomial test to check object choice (Including both age classes)
# 43 dogs chose GB out of 79.
binom.test(43, 79)

# GLM to check influence of age class (and sex) on object choice
gbgbbinspect <- read.csv("gbgbbinspect.csv")
lrfit04 <- glm(inspect ~ age + sex, data = gbgbbinspect, family = binomial(link = "logit"))
summary(lrfit04)

# Model diagnostics
DHARMa::testResiduals(lrfit04)

#-----------------------------------------------------------------------------------------------------------------------
### Latency
#-----------------------------------------------------------------------------------------------------------------------

## GB-BP Condition
# LM to check influence of age class (and sex) on latency of object choice
gbbplat <- read.csv("gbbplat.csv")

#Log-transform of response variable
gbbplat$latency <- log10(gbbplat$latency)

#Linear model (LM) analysis
lrfit05 <- lm(latency ~ age + sex + object, data = gbbplat)
summary(lrfit05)

# Model diagnostics
DHARMa::testResiduals(lrfit05)

#-----------------------------------------------------------------------------------------------------------------------

## GB-TP Condition
# LM to check influence of age class (and sex) on latency of object choice
gbtplat <- read.csv("gbtplat.csv")

#Log-transform of response variable
gbtplat$latency <- log10(gbtplat$latency)

#Linear model (LM) analysis
lrfit06 <- lm(latency ~ age + sex + object, data = gbtplat)
summary(lrfit06)

# Model diagnostics
DHARMa::testResiduals(lrfit06)

#-----------------------------------------------------------------------------------------------------------------------

## BP-TP Condition
# LM to check influence of age class (and sex) on latency of object choice
bptplat <- read.csv("bptplat.csv")

#Log-transform of response variable
bptplat$latency <- log10(bptplat$latency)

#Linear model (LM) analysis
lrfit07 <- lm(latency ~ age + sex + object, data = bptplat)
summary(lrfit07)

# Model diagnostics
DHARMa::testResiduals(lrfit07)

#-----------------------------------------------------------------------------------------------------------------------

## GB-GBB Condition
# LM to check influence of age class (and sex) on latency of object choice
gbgbblat <- read.csv("gbgbblat.csv")

#Log-transform of response variable
gbgbblat$latency <- log10(gbgbblat$latency)

#Linear model (LM) analysis
lrfit08 <- lm(latency ~ age + sex + object, data = gbgbblat)
summary(lrfit08)

# Model diagnostics
DHARMa::testResiduals(lrfit08)
# Residual deviation detected. Model Without log-transformation used. 

gbgbblat <- read.csv("gbgbblat.csv")
lrfit09 <- lm(latency ~ age + sex + object, data = gbgbblat)
summary(lrfit09)

# Model diagnostics
DHARMa::testResiduals(lrfit09)

#-----------------------------------------------------------------------------------------------------------------------