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Commit 80811c86 authored by Loic-Lenof's avatar Loic-Lenof
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Update READMES

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CSV_data/README-Stats.md → CSV_data/README.md
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kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
numb_stats_b %>%
ggplot(aes(x=number, y=mean, group=1)) +
geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
color="red", width=.1, show.legend = FALSE)+
geom_point() + geom_line() +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Number of BBPs per min")+
xlab("Number of dolphins in group")
# Clicks
#KW test
kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
numb_stats_c <- computeStats(acoustic.dta, number, number_of_clicks)
numb_stats_c[is.na(numb_stats_c)] <- 0
numb_stats_c$number <- as.factor(numb_stats_c$number)
numb_stats_c %>%
ggplot(aes(x=number, y=mean, group=1)) +
geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
color="red", width=.1)+
geom_point() + geom_line() +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Mean number of clicks per min")+
xlab("Number of echolocation clicks in group")
#### Nets plots + KW analysis ####
# Whistles
#KW test
......@@ -16,9 +28,9 @@ kruskalmc(acoustic.dta$whistling_time_per_dolphin, acoustic.dta$net)
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("a","ad","bd","cd","a"))
barPlot(computeStats(acoustic.dta, net, whistling_time_per_dolphin/375),
myletters_df,
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=.6,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
......@@ -26,14 +38,14 @@ theme(axis.text.x=element_text(size=8.5))
#KW test
kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$net)
# p<0.05 so post-hoc
kruskalmc(acoustic.dta$BBPs_per_dolphin, acoustic.dta$net,)
kruskalmc(acoustic.dta$BBPs_per_dolphin, acoustic.dta$net)
# DIY : letters
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("a","a","a","a","a"))
barPlot(computeStats(acoustic.dta, net, BBPs_per_dolphin),
myletters_df,
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=.8,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
......@@ -46,14 +58,113 @@ kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("ae","ad","bd","cd","e"))
barPlot(computeStats(acoustic.dta, net, clicks_per_dolphin),
myletters_df,
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=120,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
# p<0.05 so post-hoc
kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon,
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
beacon
computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
names(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375))
factor(acoustic.dta$beacon)
unique(acoustic.dta$beacon)
computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
#### : Beacon plots + KW analysis (letters not shown for readability) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = names, new_names = names,
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
names = list(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"])
names
names
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
names
unlist(names)
#### Beacon plots + KW analysis (letters not shown for readability) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = unlist(names), new_names = unlist(names),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
#### Beacon plots + KW analysis (letters not shown for readability) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = unlist(names), new_names = unlist(names),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
# BBPs
#KW test
kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
NULL,
beacon, old_names = unlist(names), new_names = unlist(names),
xname="Signals from bio-inspired beacon", height=0.5, size=3,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
# Clicks
#KW test
kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
NULL,
beacon, old_names = unlist(names), unlist(names),
xname="Signals from bio-inspired beacon", height=150, size=3,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
########################################################################
# STATISTICS
# Author : Loic LEHNHOFF
......@@ -71,46 +182,32 @@ library(stringr) # "gsub" function
library(rcompanion) # "fullPTable" function
library(multcompView) # "multcompLetters" function
library(ggplot2)
library(pgirmess)
#library(tidyquant) # geom_ma() if rolling average needed
################# DATASET IMPORTS #####################################
folder <- './../'
whistles.dta <-read.table(file=paste0(folder,
'Whistles/Evaluation/whistles_durations.csv'),
sep = ',', header=TRUE)
whistles.dta <- whistles.dta[order(whistles.dta$audio_names),]
bbp.dta <-read.table(file=paste0(folder,
'BBPs/Results/16-06-22_14h00_number_of_BBP.csv'),
sep = ',', header=TRUE)
bbp.dta <- bbp.dta[order(bbp.dta$audio_names),]
clicks.dta <-read.table(file=paste0(folder,
'Clicks/Results/projection_updated_number_of_clicks_02052022.csv'), #number_of_clicks_02052022.csv
sep = ',', header=TRUE)
clicks.dta <- clicks.dta[order(clicks.dta$audio_names),]
# Merge files into 1 dataset
acoustic.dta <- clicks.dta
acoustic.dta$number_of_bbp <- bbp.dta$number_of_BBP
acoustic.dta$total_whistles_duration <- whistles.dta$total_whistles_duration
rm(whistles.dta, bbp.dta, clicks.dta)
# suppress "T" acoustic data (other groups not tested on our variables)
acoustic.dta <- acoustic.dta[acoustic.dta$acoustic!="T",]
bbp.dta <- bbp.dta[bbp.dta$acoustic!="T",]
# shuffle dataframe
acoustic.dta <- acoustic.dta[sample(1:nrow(acoustic.dta)), ]
acoustic.dta$acoustic <- factor(acoustic.dta$acoustic)
bbp.dta <- bbp.dta[sample(1:nrow(bbp.dta)), ]
bbp.dta$acoustic <- factor(bbp.dta$acoustic)
#################### DATA INSPECTION #################################
# Data description
names(acoustic.dta)
names(bbp.dta)
# self explenatory except acoustic : correspond to the activation sequence.
# Look for obvious correlations
plot(acoustic.dta) # nothing that we can see
plot(bbp.dta) # nothing that we can see
# Look for zero-inflation
100*sum(acoustic.dta$number_of_clicks == 0)/nrow(acoustic.dta)
100*sum(acoustic.dta$number_of_bbp == 0)/nrow(acoustic.dta)
100*sum(acoustic.dta$total_whistles_duration == 0)/nrow(acoustic.dta)
# 3.6%, 53.7% & 24.7% of our data are zeros. Will have to be dealt with.
100*sum(bbp.dta$number_of_BBP == 0)/nrow(bbp.dta)
100*sum(bbp.dta$Buzz == 0)/nrow(bbp.dta)
100*sum(bbp.dta$Burst.pulse == 0)/nrow(bbp.dta)
# 53.7%, 60.1% & 73.6% of data are zeroes
# QUESTION: This study is aimed at understanding if dolphin's acoustic activity
# is influenced bytheir behavior, the emission of a pinger or a fishing net.
# Dependent variables (Y): number_of_clicks, number_of_bbp, total_whistles_duration.
# Dependent variables (Y): number_of_BBP, Buzz & Burst.pulse
# Explanatory variables (X): acoustic, fishing_net, behavior, beacon, net, number.
# What are the H0/ H1 hypotheses ?
# H0 : No influence of any of the explanatory variables on a dependant one.
......@@ -118,47 +215,47 @@ plot(acoustic.dta) # nothing that we can see
##################### DATA EXPLORATION ################################
# Y Outlier detection
par(mfrow=c(2,3))
boxplot(acoustic.dta$total_whistles_duration, col='red',
ylab='total_whistles_duration')
boxplot(acoustic.dta$number_of_bbp, col='red',
ylab='number_of_bbp')
boxplot(acoustic.dta$number_of_clicks, col='red',
ylab='number_of_clicks')
dotchart(acoustic.dta$total_whistles_duration, pch=16,
xlab='total_whistles_duration', col='red')
dotchart(acoustic.dta$number_of_bbp, pch=16,
xlab='number_of_bbp', col='red')
dotchart(acoustic.dta$number_of_clicks, pch=16,
xlab='number_of_clicks', col='red')
boxplot(bbp.dta$number_of_BBP, col='red',
ylab='number_of_BBP')
boxplot(bbp.dta$Buzz, col='red',
ylab='Buzz')
boxplot(bbp.dta$Burst.pulse, col='red',
ylab='Burst.pulse')
dotchart(bbp.dta$number_of_BBP, pch=16,
xlab='number_of_BBP', col='red')
dotchart(bbp.dta$Buzz, pch=16,
xlab='Buzz', col='red')
dotchart(bbp.dta$Burst.pulse, pch=16,
xlab='Burst.pulse', col='red')
# Y distribution
par(mfrow=c(2,3))
hist(acoustic.dta$total_whistles_duration, col='red', breaks=8,
xlab='total_whistles_duration', ylab='number')
hist(acoustic.dta$number_of_bbp, col='red', breaks=8,
xlab='number_of_bbp', ylab='number')
hist(acoustic.dta$number_of_clicks, col='red', breaks=8,
xlab='number_of_clicks', ylab='number')
qqnorm(acoustic.dta$total_whistles_duration, col='red', pch=16)
qqline(acoustic.dta$total_whistles_duration)
qqnorm(acoustic.dta$number_of_bbp, col='red', pch=16)
qqline(acoustic.dta$number_of_bbp)
qqnorm(acoustic.dta$number_of_clicks, col='red', pch=16)
qqline(acoustic.dta$number_of_clicks)
shapiro.test(acoustic.dta$total_whistles_duration)
shapiro.test(acoustic.dta$number_of_bbp)
shapiro.test(acoustic.dta$number_of_clicks)
hist(bbp.dta$number_of_BBP, col='red', breaks=8,
xlab='number_of_BBP', ylab='number')
hist(bbp.dta$Buzz, col='red', breaks=8,
xlab='Buzz', ylab='number')
hist(bbp.dta$Burst.pulse, col='red', breaks=8,
xlab='Burst.pulse', ylab='number')
qqnorm(bbp.dta$number_of_BBP, col='red', pch=16)
qqline(bbp.dta$number_of_BBP)
qqnorm(bbp.dta$Buzz, col='red', pch=16)
qqline(bbp.dta$Buzz)
qqnorm(bbp.dta$Burst.pulse, col='red', pch=16)
qqline(bbp.dta$Burst.pulse)
shapiro.test(bbp.dta$number_of_BBP)
shapiro.test(bbp.dta$Buzz)
shapiro.test(bbp.dta$Burst.pulse)
# p-values are significant => they do not follow normal distributions
# will need a transformation or the use of a glm model
# we will need transformations or the use of glm models
# X Number of individuals per level
summary(factor(acoustic.dta$acoustic))
summary(factor(acoustic.dta$fishing_net))
summary(factor(acoustic.dta$behavior))
summary(factor(acoustic.dta$beacon))
summary(factor(acoustic.dta$net))
table(factor(acoustic.dta$acoustic),factor(acoustic.dta$fishing_net))
table(factor(acoustic.dta$acoustic),factor(acoustic.dta$behavior))
table(factor(acoustic.dta$behavior),factor(acoustic.dta$acoustic))
ftable(factor(acoustic.dta$fishing_net), factor(acoustic.dta$behavior), factor(acoustic.dta$acoustic))
summary(factor(bbp.dta$acoustic))
summary(factor(bbp.dta$fishing_net))
summary(factor(bbp.dta$behavior))
summary(factor(bbp.dta$beacon))
summary(factor(bbp.dta$net))
table(factor(bbp.dta$acoustic),factor(bbp.dta$fishing_net))
table(factor(bbp.dta$acoustic),factor(bbp.dta$behavior))
table(factor(bbp.dta$behavior),factor(bbp.dta$acoustic))
ftable(factor(bbp.dta$fishing_net), factor(bbp.dta$behavior), factor(bbp.dta$acoustic))
# => unbalanced, no big deal but will need more work (no orthogonality):
# Effects can depend on the order of the variables
# => Beacon and net have modalities with <10 individuals => analysis impossible
......@@ -176,54 +273,45 @@ ftable(factor(acoustic.dta$fishing_net), factor(acoustic.dta$behavior), factor(a
# (using kruskall-Wallis non-parametric test)
# fishing_net, behavior and acoustic where tested with their interactions.
# If a variable is it in a model, it is because it had no significant effect.
par(mfrow=c(1,1))
### Model for whistles
# Residual hypotheses not verified for LM
# Overdipsersion when using GLM (negative binomial)
# Using ZINB:
zero.whi <- zeroinfl(total_whistles_duration ~
acoustic + fishing_net + behavior + offset(log(number)),
data=acoustic.dta, dist='negbin')
nb.whi <- glm.nb(total_whistles_duration ~
acoustic + fishing_net + behavior + offset(log(number)),
data=acoustic.dta)
# comparison ZINB VS NB model
vuong(zero.whi, nb.whi) #(if p-value<0.05 then first model in comparison is better)
mod.whi <- zero.whi # => zeroinflated model is indeed better suited
car::Anova(mod.whi, type=3)
shapiro.test(residuals(mod.whi)) # H0 : normality -> not rejected if p>0.05
dwtest(mod.whi) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.whi) # H0 -> homoscedasticity if p<0.05
# No normality but we do not need it
### Model for BBP
# No normality of residuals for LM
# overdispersion with GLM quasipoisson
#try with glm NB:
mod.bbp <- glm.nb(number_of_bbp ~ acoustic + fishing_net + behavior
mod.bbp <- glm.nb(number_of_BBP ~ acoustic + fishing_net + behavior
+ offset(log(number)),
data=acoustic.dta)
data=bbp.dta)
car::Anova(mod.bbp, type=3)
dwtest(mod.bbp) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.bbp) # H0 -> homoscedasticity if p<0.05
# Normality not needed in GLM, hypotheses verified !
mod.bbp$deviance/mod.bbp$df.residual
# slight underdispersion, not improved with ZINB so we keep this
### Model for clicks
# Using NB model:
mod.cli <- glm.nb(number_of_clicks ~ acoustic + fishing_net + acoustic:fishing_net + offset(log(number)),
data=acoustic.dta)
car::Anova(mod.cli, type=3)
shapiro.test(residuals(mod.cli)) # H0 : normality -> cannot be rejected if p > 0.05
dwtest(mod.cli) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.cli) # H0 -> homoscedasticity if p<0.05
# Normality not needed in GLM, hypotheses verified !
mod.cli$deviance/mod.cli$df.residual
# slight overdispersion. (ZINB does not clearly improve results so we keep this)
# FYI1: Comparison of combination of explanatory variables between models
# were compared based on BIC criterion.
# The model with the lowest BIC was kept (and is the one shown)
# FYI2: log(number of dolphin per group) does have an effect on data but we have
# no interest in investigating it, that is why we use it as an offset.
# slight underdispersion
### Model for Buzzes
# No normality of residuals for LM
# overdispersion with GLM quasipoisson
# underdispersion with glm NB
# Try with ZINB:
mod.buzz <- glm.nb(Buzz ~ behavior + fishing_net + acoustic
+ offset(log(number)),
data=bbp.dta)
car::Anova(mod.buzz, type=3)
dwtest(mod.buzz) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.buzz) # H0 -> homoscedasticity if p<0.05
mod.buzz$df.null/mod.buzz$df.residual
# No overdispersion
### Model for Burst-pulses
# No normality of residuals for LM
# overdispersion with quasipoisson
# underdispersion with NB
# ZINB is working :
mod.burst.pulse <- zeroinfl(Burst.pulse ~ fishing_net + acoustic + behavior
+ offset(log(number)), dist="negbin",
data=bbp.dta)
car::Anova(mod.burst.pulse, type=3)
dwtest(mod.burst.pulse) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.burst.pulse) # H0 -> homoscedasticity if p<0.05
mod.burst.pulse$df.null/mod.burst.pulse$df.residual # -> Overdispersion of != 1
# no overdispersion
##################### Boxplots and comparisons #####################
### Functions to compute stats
computeLetters <- function(temp, category) {
......@@ -251,10 +339,10 @@ height, xname="", colours="black", legend_title="", legend_labs="",ytitle=""){
if (!is.null(signif)){colnames(signif)[1] <- "use"}
dta %>%
mutate(use=fct_relevel({{category}}, old_names)) %>%
ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}})) +
ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}}, na.rm = TRUE)) +
{if(length(colours)==1)geom_point(color=colours, position=position_dodge(.5))}+
{if(length(colours)==2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
{if(length(colours)==2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
{if(length(colours)>=2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
{if(length(colours)>=2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
scale_x_discrete(breaks=old_names,
labels=new_names)+
ylab(ytitle)+
......@@ -266,247 +354,159 @@ geom_errorbar(aes(x=use, ymin=mean-ic, ymax=mean+ic), position=position_dodge(.5
}
####Introducing variables averaged per dolphins ####
# since we introduced an offset, variables can be divided by the number of dolphins
acoustic.dta$whistling_time_per_dolphin <- acoustic.dta$total_whistles_duration/acoustic.dta$number
acoustic.dta$BBPs_per_dolphin <- acoustic.dta$number_of_bbp/acoustic.dta$number
acoustic.dta$clicks_per_dolphin <- acoustic.dta$number_of_clicks/acoustic.dta$number
#### Fishing net ####
# whistles
table <- cld(emmeans(mod.whi, pairwise~fishing_net, adjust="tukey"), Letters = letters)
bbp.dta$BBPs_per_dolphin <- bbp.dta$number_of_BBP/bbp.dta$number
bbp.dta$Buzz_per_dolphin <- bbp.dta$Buzz/bbp.dta$number
bbp.dta$Burst.pulse_per_dolphin <- bbp.dta$Burst.pulse/bbp.dta$number
#### Fishing nets plots ####
par(mfrow=c(3, 1))
# BBPs
table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(fishing_net=table$fishing_net,
letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375), # 375 bins = 1 sec
barPlot(computeStats(bbp.dta, fishing_net, BBPs_per_dolphin),
myletters_df, fishing_net,
old_names = c("SSF","F"), new_names = c("Absent", "Present"),
xname="Presence/Asence of fishing net", height=.5,
ytitle="Mean whistling time per dolphin per min")
# BBP
table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
xname="Presence/Asence of fishing net", height=.6,
ytitle="Mean number of BBP per dolphin per min")
# Buzz
table <- cld(emmeans(mod.buzz, pairwise~fishing_net, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(fishing_net=table$fishing_net,
letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin),
barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin),
myletters_df, fishing_net,
ytitle="Mean number of Buzzes per dolphin per min",
old_names = c("SSF","F"), new_names = c("Absent", "Present"),
xname="Presence/Asence of fishing net", height=.6,
ytitle="Mean number of BBPs per dolphin per min")
# Clicks
table <- cld(emmeans(mod.cli, pairwise~fishing_net, adjust="tukey"), Letters = letters)
xname="Presence/Asence of fishing net", height=.45)
# Burst-pulse
table <- cld(emmeans(mod.burst.pulse, pairwise~fishing_net, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(fishing_net=table$fishing_net,
letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin),
barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin),
myletters_df, fishing_net,
ytitle="Mean number of Burst-pulses per dolphin per min",
old_names = c("SSF","F"), new_names = c("Absent", "Present"),
xname="Presence/Asence of fishing net", height=100,
ytitle="Mean number of clicks per dolphin per min")
xname="Presence/Asence of fishing net", height=.18, )
#### Acoustic plots ####
# Whistles
table <- cld(emmeans(mod.whi, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, acoustic, whistling_time_per_dolphin/375),
myletters_df, acoustic, height=0.65, ytitle="Mean whistling time per dolphin per min",
# BBPs
table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,
letter = trimws(table$.group))
barPlot(computeStats(bbp.dta, acoustic, BBPs_per_dolphin),
myletters_df, acoustic, height=.9, ytitle="Mean number of BBPs per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"),
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence")
# BBPs
table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, acoustic, BBPs_per_dolphin),
myletters_df, acoustic, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
# Buzz
table <- cld(emmeans(mod.buzz, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,
letter = trimws(table$.group))myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"), letter = c("a","a","a","a","a"))
#error, no acoustic in model:
myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"),
letter = c("a","a","a","a","a"))
barPlot(computeStats(bbp.dta, acoustic, Buzz_per_dolphin),
myletters_df, acoustic, height=0.45, ytitle="Mean number of Buzzes per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"),
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence")
# Clicks
table <- cld(emmeans(mod.cli, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, acoustic, clicks_per_dolphin),
myletters_df, acoustic, height=155, ytitle="Mean number of clicks per dolphin per min",
# Burst-pulse
table <- cld(emmeans(mod.burst.pulse, pairwise~acoustic, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$acoustic,
letter = trimws(table$.group))
barPlot(computeStats(bbp.dta, acoustic, Burst.pulse_per_dolphin),
myletters_df, acoustic, height=0.5, ytitle="Mean number of Burst-pulses per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"),
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence")
#### Interaction fishing_net:acoustic plots ####
# Whistles
letters_df <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
#### Behaviour plots ####
# BBPs
table <- cld(emmeans(mod.bbp, pairwise~behavior, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
barPlot(computeStats(bbp.dta, behavior, BBPs_per_dolphin),
myletters_df, behavior, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins")
# Buzz
table <- cld(emmeans(mod.buzz, pairwise~behavior, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
barPlot(computeStats(bbp.dta, behavior, Buzz_per_dolphin),
myletters_df, behavior, height=1, ytitle="Mean number of Buzzes per dolphin per min",
old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins")
# Burst-pulse
table <- cld(emmeans(mod.burst.pulse, pairwise~behavior, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
barPlot(computeStats(bbp.dta, behavior, Burst.pulse_per_dolphin),
myletters_df, behavior, height=0.4, ytitle="Mean number of Burst-pulses per dolphin per min",
old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins")
#### Interaction : acoustic:fishing_net plots ####
# BBP
letters_df <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
"acoustic")$acoustic
letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
letters_df$letter <- gsub(" ", "", letters_df$letter)
barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375, two=acoustic),
barPlot(computeStats(bbp.dta, fishing_net, BBPs_per_dolphin, two=acoustic),
NULL, acoustic, fill=fishing_net,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean whistling time per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence", height=c(.95,.95,.95,1,.95,1,.95,1,1,1),
xname="Activation sequence", height=c(1.6),
colours=c("#E69F00","#999999"), size=5,
legend_title="Fishing net", legend_labs=c("Present", "Absent"))
# BBPs
letters_df <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
# Buzz
letters_df <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
letters_df$acoustic <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
"acoustic")$acoustic
letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
letters_df$letter <- gsub(" ", "", letters_df$letter)
barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin, two=acoustic),
barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin, two=acoustic),
NULL, acoustic, fill=fishing_net,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Buzzes per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence", height=c(1.65,1.65,1.72,1.65,1.72,1.65,1.65,1.72,1.72,1.72),
xname="Activation sequence", height=c(0.77,0.77,0.8,0.77,0.77,0.8,0.77,0.8,0.8,0.8),
colours=c("#E69F00","#999999"), size=5,
legend_title="Fishing net", legend_labs=c("Present", "Absent"))
# Clicks
letters_df <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
# Burst-pulse
letters_df <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
letters_df$acoustic <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
"acoustic")$acoustic
letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
letters_df$letter <- gsub(" ", "", letters_df$letter)
barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin, two=acoustic),
barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin, two=acoustic),
NULL, acoustic, fill=fishing_net,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of clicks per dolphin per min",
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Burst-pulses per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence", height=c(180,180,187,187,180,187,180,180,187,187),
xname="Activation sequence", height=c(0.9,0.85,0.9,0.9,0.85,0.9,0.85,0.9,0.85,0.85),
colours=c("#E69F00","#999999"), size=5,
legend_title="Fishing net", legend_labs=c("Present", "Absent"))
#### Behaviour plots ####
# Whistles
table <- cld(emmeans(mod.whi, pairwise~behavior, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(behavior=table$behavior,letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, behavior, whistling_time_per_dolphin/375),
myletters_df, behavior, height=0.75, ytitle="Mean whistling time per dolphin per min",
old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins")
# BBPs
# real effect measured in model
table <- cld(emmeans(mod.bbp, pairwise~behavior, adjust="tukey"), Letters = letters)
myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, behavior, BBPs_per_dolphin),
myletters_df, behavior, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins")
# Clicks
# no significant effect in click statistical model so all the same letters
myletters_df <- data.frame(behavior=unique(acoustic.dta$behavior),
letter = rep("a",length(unique(acoustic.dta$behavior))))
barPlot(computeStats(acoustic.dta, behavior, clicks_per_dolphin),
myletters_df,
behavior, old_names = c("CHAS", "DEPL", "SOCI"),
new_names = c("Foraging", "Travelling", "Socialising"),
xname="Behaviours of dolphins", height=150,
ytitle="Mean number of clicks per dolphin per min")
#### Nets plots + KW analysis ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$net)
# p<0.05 so post-hoc
kruskalmc(acoustic.dta$whistling_time_per_dolphin, acoustic.dta$net)
# DIY : letters
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("a","ad","bd","cd","a"))
barPlot(computeStats(acoustic.dta, net, whistling_time_per_dolphin/375),
myletters_df,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
xname="Fishing nets", height=.6,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
# BBPs
#KW test
kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$net)
# p<0.05 so post-hoc
kruskalmc(acoustic.dta$BBPs_per_dolphin, acoustic.dta$net,)
# DIY : letters
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("a","a","a","a","a"))
barPlot(computeStats(acoustic.dta, net, BBPs_per_dolphin),
myletters_df,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
xname="Fishing nets", height=.8,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
# Clicks
#KW test
kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$net)
# p<0.05 so post-hoc
kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
# DIY : letters
myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
letter = c("ae","ad","bd","cd","e"))
barPlot(computeStats(acoustic.dta, net, clicks_per_dolphin),
myletters_df,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
xname="Fishing nets", height=120,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
# BBPs
#KW test
kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
xname="Signals from bio-inspired beacon", height=0.5, size=3,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
# Clicks
#KW test
kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
xname="Signals from bio-inspired beacon", height=150, size=3,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
scale_x_discrete(guide=guide_axis(n.dodge = 2))
# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
#### WHY NOT: Number plots ####
# Whistles
numb_stats_w <- computeStats(acoustic.dta, number, total_whistles_duration/375)
numb_stats_w[is.na(numb_stats_w)] <- 0
numb_stats_w$number <- as.factor(numb_stats_w$number)
numb_stats_w %>%
ggplot(aes(x=number, y=mean, group=1)) +
geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
color="red", width=.1, show.legend = FALSE)+
geom_point() + geom_line() +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Mean whistling time per min")+
xlab("Number of dolphins in group")
# BBPs
numb_stats_b <- computeStats(acoustic.dta, number, number_of_bbp)
numb_stats_b[is.na(numb_stats_b)] <- 0
numb_stats_b$number <- as.factor(numb_stats_b$number)
numb_stats_b %>%
ggplot(aes(x=number, y=mean, group=1)) +
geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
color="red", width=.1, show.legend = FALSE)+
geom_point() + geom_line() +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Number of BBPs per min")+
xlab("Number of dolphins in group")
# Clicks
numb_stats_c <- computeStats(acoustic.dta, number, number_of_clicks)
numb_stats_c[is.na(numb_stats_c)] <- 0
numb_stats_c$number <- as.factor(numb_stats_c$number)
numb_stats_c %>%
ggplot(aes(x=number, y=mean, group=1)) +
geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
color="red", width=.1)+
geom_point() + geom_line() +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Mean number of clicks per min")+
xlab("Number of echolocation clicks in group")
#### Interaction : acoustic:behavior plots ####
# BBP
barPlot(computeStats(bbp.dta, behavior, BBPs_per_dolphin, two=acoustic),
NULL, acoustic, fill=behavior,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence",
colours=c("#E69F00","#55c041", "#FF3814"), size=5,
legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
# Buzz
barPlot(computeStats(bbp.dta, behavior, Buzz_per_dolphin, two=acoustic),
NULL, acoustic, fill=behavior,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Buzzes per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence",
colours=c("#E69F00","#55c041", "#FF3814"), size=5,
legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
# Burst-pulse
barPlot(computeStats(bbp.dta, behavior, Burst.pulse_per_dolphin, two=acoustic),
NULL, acoustic, fill=behavior,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Burst-pulses per dolphin per min",
new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
xname="Activation sequence",
colours=c("#E69F00","#55c041", "#FF3814"), size=5,
legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
Stats/BBP-click-whistles_3models.R
+ 10
− 7
View file @ 80811c86
......@@ -399,7 +399,7 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
barPlot(computeStats(acoustic.dta, net, whistling_time_per_dolphin/375),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=.6,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
......@@ -415,7 +415,7 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
barPlot(computeStats(acoustic.dta, net, BBPs_per_dolphin),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=.8,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
......@@ -431,19 +431,20 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
barPlot(computeStats(acoustic.dta, net, clicks_per_dolphin),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
xname="Fishing nets", height=120,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8.5))
#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
#### Beacon plots + KW analysis (letters not shown for readability) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
beacon, old_names = unlist(names), new_names = unlist(names),
xname="Signals from bio-inspired beacon", height=0.9, size=3,
ytitle="Mean whistling time per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
......@@ -453,9 +454,10 @@ barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
# BBPs
#KW test
kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
beacon, old_names = unlist(names), new_names = unlist(names),
xname="Signals from bio-inspired beacon", height=0.5, size=3,
ytitle="Mean number of BBPs per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
......@@ -465,9 +467,10 @@ barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
# Clicks
#KW test
kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
NULL,
beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
beacon, old_names = unlist(names), unlist(names),
xname="Signals from bio-inspired beacon", height=150, size=3,
ytitle="Mean number of clicks per dolphin per min")+
theme(axis.text.x=element_text(size=8))+
......
Stats/README-Stats.md → Stats/README.md
+ 39
− 0
View file @ 80811c86
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