##### Each Bystander Related Behaviour (BRB) #####

data <- read.table("Data_R2.txt",header=T,sep="\t")
str(data)
data$Hypothesis <- as.factor(data$Hypothesis)


### Eavesdropping

eavesHyp <- table(data$Eavesdropping, data$Hypothesis)
dimnames(eavesHyp)<-list(Eavesdropping=c("no-evidence","evidence"),
                         Hypothesis=c("competition","other"))
eavesChiSqResult <- chisq.test(eavesHyp, simulate.p.value=TRUE)

eavesChiSqResult$expected
#                       Hypothesis
# Eavesdropping   competition    other
#   no-evidence   15.774648 40.22535
#   evidence       4.225352 10.77465

##  the assumption that the expected frequencies are at least 5 
## in no less than 80% of the cells in the contingency table is not fulfilled

# Therefore, we ran Fisher's exact test

(eavesFisherResult <- fisher.test(eavesHyp))
# p-value = 0.00383
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.0396541 0.6549499
# sample estimates:
# odds ratio 
# 0.1683376 


eavesHyp
#                     Hypothesis
# Eavesdropping   competition other
#   no-evidence          11    45
#   evidence              9     6


## Odds ratio

(9/20)/(6/51)
#  3.825
# it was 3.8 times more likely to find evidence of eavesdropping
# for species related to the intrasexual competition hypothesis
# than for those linked to other hypotheses 




### Exploitation

expHyp <- table(data$Exploitation, data$Hypothesis)
dimnames(expHyp)<-list(Exploitation=c("no-evidence","evidence"),
                       Hypothesis=c("competition","other"))
expChiSqResult <- chisq.test(expHyp, simulate.p.value=TRUE)
expChiSqResult$expected
#                       Hypothesis
# Exploitation     competition     other
#   no-evidence     17.464789    44.535211
#   evidence         2.535211    6.464789

(expFisherResult <- fisher.test(expHyp))
# p-value = 0.1051
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
#  0.04548752 1.38497379
# sample estimates:
#   odds ratio 
# 0.2613004 



### Audience effect ('audience effect' test)

audHyp <- table(data$Audience, data$Hypothesis)
dimnames(audHyp)<-list(Audience=c("no-evidence","evidence"),
                       Hypothesis=c("competition","other"))
audChiSqResult <- chisq.test(audHyp, simulate.p.value=TRUE)
audChiSqResult$expected
#                       Hypothesis
# Audience        competition     other
# no-evidence      17.183099    43.816901
# evidence         2.816901     7.183099

(audFisherResult <- fisher.test(audHyp))
# p-value = 0.1314
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
#   0.06609034 1.65608514
# sample estimates:
#  odds ratio 
# 0.3321457  





### Removing 10 species for which at least another hypothesis was also suggested ###
row_names_df_to_remove<-c("Oreochromis mossambicus","Cygnus atratus","Ptilonorhynchus violaceus","Aphidius ervi","Byrsotria fumigata","Cotesia rubecula","Euphydryas editha","Eupoecilia ambiguella","Capra hircus","Thamnophis sirtalis")
data2 <- data[!(data$Species %in% row_names_df_to_remove),]



### Eavesdropping

eavesHyp2 <- table(data2$Eavesdropping, data2$Hypothesis)
dimnames(eavesHyp2)<-list(Eavesdropping=c("no evidence","evidence"),
                          Hypothesis=c("competition","other"))
eavesChiSqResult2 <- chisq.test(eavesHyp2, simulate.p.value=TRUE)

eavesChiSqResult2$expected

(eavesFisherResult2 <- fisher.test(eavesHyp2))
# p-value = 0.01211
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.02330775 0.79568593
# sample estimates:
# odds ratio 
# 0.1400554



### Exploitation

expHyp2 <- table(data2$Exploitation, data2$Hypothesis)
dimnames(expHyp2)<-list(Exploitation=c("no evidence","evidence"),Hypothesis=c("competition","other"))
expChiSqResult2 <- chisq.test(expHyp2, simulate.p.value=TRUE)

expChiSqResult2$expected

(expFisherResult2 <- fisher.test(expHyp2))
# p-value = 0.2526
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.04111985 4.44921221
# sample estimates:
# odds ratio 
# 0.3483708 



### Audience effect

audHyp2 <- table(data2$Audience, data2$Hypothesis)
dimnames(audHyp2)<-list(Audience=c("no evidence","evidence"),Hypothesis=c("competition","other"))
audChiSqResult2 <- chisq.test(audHyp2, simulate.p.value=TRUE)

audChiSqResult2$expected
(audFisherResult2 <- fisher.test(audHyp2, simulate.p.value=TRUE))

# p-value = 0.1154
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
#  0.03917116 2.05207579
# sample estimates:
#  odds ratio 
# 0.2615068  






##### Combined BRB ##### 


data$BRBcombined <- data$Eavesdropping + data$Exploitation + data$Audience

BRBHyp <- table(data$BRBcombined, data$Hypothesis)

dimnames(BRBHyp)<-list(BRBcombined=c("none","one","two","three"),
                       Hypothesis=c("competition","other"))

BRBHyp
#                     Hypothesis
# BRBcombined competition   other
#   none          11         41
#   one           1           6
#   two           6           3
#   three         2           1

BRBChiSqResult <- chisq.test(BRBHyp, simulate.p.value=TRUE)

BRBChiSqResult$expected
#                       Hypothesis
# BRBcombined  competition     other
#   none        14.6478873   37.352113
#   one         1.9718310    5.028169
#   two         2.5352113    6.464789
#   three       0.8450704    2.154930

##  the assumption that the expected frequencies are at least 5 
## in no less than 80% of the cells in the contingency table is not fulfilled

# Therefore, we ran Fisher's exact test


(BRBFisherResult<- fisher.test(BRBHyp))
# p-value = 0.01035
# alternative hypothesis: two.sided3.



### Odds ratio

(2/11)/(1/41)
#  7.454545
# it was 7.5 times more likely to find all 3 bystander-related behaviours combined than no BRB
# for species related to the intrasexual competition hypothesis than the other hypotheses 




### Removing 10 species for which at least another hypothesis was also suggested ###

data2$BRBcombined <- data2$Eavesdropping + data2$Exploitation + data2$Audience

BRBHyp2 <- table(data2$BRBcombined, data2$Hypothesis)

dimnames(BRBHyp2)<-list(BRBcombined=c("none","one","two","three"),
                        Hypothesis=c("competition","other"))

BRBHyp2
#                     Hypothesis
# BRBcombined  competition   other
#   none            5        41
#   one             1         6
#   two             3         3
#   three           1         1

BRBChiSqResult2 <- chisq.test(BRBHyp2, simulate.p.value=TRUE)
BRBChiSqResult2$expected
#                       Hypothesis
# BRBcombined  competition     other
#   none        7.5409836    38.459016
#   one         1.1475410     5.852459
#   two         0.9836066     5.016393
#   three       0.3278689     1.672131


(BRBFisherResult2<- fisher.test(BRBHyp2))
# p-value = 0.04924
# alternative hypothesis: two.sided3.