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__init__.py
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Luc Giffon authored
First commit: implementation with tensorflow of deepfriedconvnet with non-adaptative fastfood and no fht - only one stack of fastfood - mnist dataset
Luc Giffon authoredFirst commit: implementation with tensorflow of deepfriedconvnet with non-adaptative fastfood and no fht - only one stack of fastfood - mnist dataset
.Rhistory 24.19 KiB
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$acoustic))
# => unbalanced, no big deal but will need more work (no orthogonality):
# Effects can depend on the order of the variables
# => Beacon and net have modalities with <10 individuals => analysis impossible
# => They will be treated apart from the rest as they are likely to be biased
##################### STATISTICAL MODELLING ###########################
### Model tested
# GLM: General linear model (residual hypothesis: normality, homoscedasticity, independant)
# GLIM: Generalized linear model (residual hypothesis: uncorrelated residuals)
# NB : Negative Binomial model (residual hypothesis: independantM)
# ZINB: Zero inflated negative binomial model (residual hypothesis: independant)
# We are using number as an offset (more dolphins => more signals)
# beacon and net explanatory variables could not be tested in models
# as they contain information already present in "fishing_net" which is more
# interesting to keep for our study. They will be treated after
# (using kruskall-Wallis non-parametric test)
# fishing_net, behavior and acoustic where tested with their interactions.
# If a variable is it in a model, it is because it had no significant effect.
par(mfrow=c(1,1))
### Model for whistles
# Residual hypotheses not verified for GLM
# Overdipsersion when using GLIM (negative binomial)
# Using ZINB:
zero.whi <- zeroinfl(total_whistles_duration ~
acoustic + fishing_net + behavior + offset(log(number)),
data=acoustic.dta, dist='negbin')
nb.whi <- glm.nb(total_whistles_duration ~
acoustic + fishing_net + behavior + offset(log(number)),
data=acoustic.dta)
# comparison ZINB VS NB model
vuong(zero.whi, nb.whi) #(if p-value<0.05 then first model in comparison is better)
mod.whi <- zero.whi # => zeroinflated model is indeed better suited
car::Anova(mod.whi, type=3)
dwtest(mod.whi) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.whi) # H0 -> homoscedasticity if p<0.05
mod.whi$df.null/mod.whi$df.residual
# no dispersion, perfect
### Model for BBP
# No normality of residuals for GLM
# overdispersion with GLIM quasipoisson
#try with glim NB:
mod.bbp <- glm.nb(number_of_bbp ~ acoustic + fishing_net + behavior
+ offset(log(number)),
data=acoustic.dta)
car::Anova(mod.bbp, type=3)
dwtest(mod.bbp) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.bbp) # H0 -> homoscedasticity if p<0.05
mod.bbp$deviance/mod.bbp$df.residual
# slight underdispersion, not improved with ZINB so we keep this
### Model for clicks
# Using NB model:
mod.cli <- glm.nb(number_of_clicks ~ acoustic + fishing_net + acoustic:fishing_net + offset(log(number)),
data=acoustic.dta)
car::Anova(mod.cli, type=3)
dwtest(mod.cli) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
bptest(mod.cli) # H0 -> homoscedasticity if p<0.05
mod.cli$deviance/mod.cli$df.residual
# slight overdispersion. (ZINB does not clearly improve results so we keep this)
# FYI1: Comparison of combination of explanatory variables between models
# were compared based on BIC criterion.
# The model with the lowest BIC was kept (and is the one shown)
# FYI2: log(number of dolphin per group) does have an effect on data but we have
# no interest in investigating it, that is why we use it as an offset.
##################### Boxplots and comparisons #####################
### Functions to compute stats
computeLetters <- function(temp, category) {
test <- multcomp::cld(object = temp$emmeans,
Letters = letters)myletters_df <- data.frame(category=test[,category],
letter=trimws(test$.group))
colnames(myletters_df)[1] <- category
return(myletters_df)
}
computeStats <- function(data, category, values, two=NULL, three=NULL) {
my_sum <- data %>%
group_by({{category}}, {{two}}, {{three}}) %>%
summarise(
n=n(),
mean=mean({{values}}),
sd=sd({{values}})
) %>%
mutate( se=sd/sqrt(n)) %>%
mutate( ic=se * qt((1-0.05)/2 + .5, n-1))
return(my_sum)
}
barPlot <- function(dta, signif, category, old_names, new_names, fill=NULL, size=5,
height, xname="", colours="black", legend_title="", legend_labs="",ytitle=""){
if (!is.null(signif)){colnames(signif)[1] <- "use"}
dta %>%
mutate(use=fct_relevel({{category}}, old_names)) %>%
ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}})) +
{if(length(colours)==1)geom_point(color=colours, position=position_dodge(.5))}+
{if(length(colours)==2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
{if(length(colours)==2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
scale_x_discrete(breaks=old_names,
labels=new_names)+
ylab(ytitle)+
xlab(xname)+
theme_classic()+ theme(text=element_text(size=12))+
{if(!is.null(signif))geom_text(data=signif, aes(label=letter, y=height), size=size,
colour="black", position=position_dodge(.5))}+
geom_errorbar(aes(x=use, ymin=mean-ic, ymax=mean+ic), position=position_dodge(.5), width=.1, show.legend = FALSE)
}
####Introducing variables averaged per dolphins ####
# since we introduced an offset, variables can be divided by the number of dolphins
acoustic.dta$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)
myletters_df <- data.frame(fishing_net=table$fishing_net,
letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/n_bins),
myletters_df, fishing_net,
old_names = c("SSF","F"), new_names = c("Absent", "Present"),
xname="Presence/Asence of fishing net", height=1,
ytitle="Mean whistling time per dolphin per min (in sec)")
# BBP
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, BBPs_per_dolphin),
myletters_df, fishing_net,
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)
myletters_df <- data.frame(fishing_net=table$fishing_net,
letter = trimws(table$.group))
barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin),
myletters_df, fishing_net,
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")
#### Acoustic plots ####
# Whistlestable <- 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/n_bins),
myletters_df, acoustic, height=1.3, ytitle="Mean whistling time per dolphin per min (in sec)",
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",
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",
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"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
"acoustic")$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/n_bins, 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 (in sec)",
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),
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"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
"acoustic")$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),
NULL, acoustic, fill=fishing_net,
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(1.65,1.65,1.72,1.65,1.72,1.65,1.65,1.72,1.72,1.72),
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"),
"fishing_net")
letters_df$acoustic <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
"acoustic")$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),
NULL, acoustic, fill=fishing_net,
old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of clicks 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),
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/n_bins),
myletters_df, behavior, height=1.5, ytitle="Mean whistling time per dolphin per min (in sec)",
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/n_bins),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
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 (in sec)")+
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),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
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))
# 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),
NULL,
net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
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 readability) ####
# Whistles
#KW test
kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/n_bins)["beacon"]
barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/n_bins),
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 (in sec)")+
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)
names = computeStats(acoustic.dta, beacon, BBPs_per_dolphin)["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))
# 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)
names = computeStats(acoustic.dta, beacon, clicks_per_dolphin)["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))
# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
#### Plots by number of dolphins ####
# Whistles
numb_stats_w <- computeStats(acoustic.dta, number, total_whistles_duration/n_bins)
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 dolphin per min (in sec)")+
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 dolphin 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 dolphin per min")+
xlab("Number of echolocation clicks in group")
#### Plots by Group ID ####
# Whistles
numb_stats_w <- computeStats(acoustic.dta, ID, whistling_time_per_dolphin/n_bins)
numb_stats_w[is.na(numb_stats_w)] <- 0
numb_stats_w$ID <- as.factor(numb_stats_w$ID)
numb_stats_w %>%
ggplot(aes(x=ID, y=mean, group=1)) +
geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+ci),
color="red", width=.1, show.legend = FALSE)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
theme_light() + theme(text=element_text(size=12)) +
ylab("Mean whistling time per dolphin per min (in sec)")+
xlab("ID of dolphins group")
# BBPs
numb_stats_b <- computeStats(acoustic.dta, ID, BBPs_per_dolphin)
numb_stats_b[is.na(numb_stats_b)] <- 0
numb_stats_b$ID <- as.factor(numb_stats_b$ID)
numb_stats_b %>%
ggplot(aes(x=ID, y=mean, group=1)) +
geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+ci),
color="red", width=.1, show.legend = FALSE)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
theme_light() + theme(text=element_text(size=12)) +
ylab("Number of BBPs per dolphin per min")+
xlab("ID of dolphins group")
# Clicks
numb_stats_c <- computeStats(acoustic.dta, ID, clicks_per_dolphin)
numb_stats_c[is.na(numb_stats_c)] <- 0
numb_stats_c$ID <- as.factor(numb_stats_c$ID)
numb_stats_c %>%
ggplot(aes(x=ID, y=mean, group=1)) +
geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+ci),
color="red", width=.1)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
theme_light() + theme(text=element_text(size=12)) +
ylab("Mean number of clicks per dolphin per min")+
xlab("ID of dolphin group")
# KW test on IDs
# whistles (excluding groups "2" because 1 sample and groups because no whistles recorded )
data_test <- acoustic.dta[acoustic.dta$ID!="2",]
print( posthocKW(data_test$whistling_time_per_dolphin, data_test$ID))
print( posthocKW(data_test$BBPs_per_dolphin, data_test$ID))
print( posthocKW(data_test$clicks_per_dolphin, data_test$ID))
#### Plots by days ####
# clicks
numb_stats_c <- computeStats(acoustic.dta, date, clicks_per_dolphin)
numb_stats_c %>%
ggplot(aes(x=date, y=mean, group=1)) +
geom_errorbar(aes(x=date, ymin=mean-sd, ymax=mean+ci),
color="black", width=.1)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge=2)) +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Mean number of clicks per dolphin per min")+
xlab("Days of recording")
# BBPs
numb_stats_c <- computeStats(acoustic.dta, date, BBPs_per_dolphin)
numb_stats_c %>%
ggplot(aes(x=date, y=mean, group=1)) +
geom_errorbar(aes(x=date, ymin=mean-sd, ymax=mean+ci),
color="black", width=.1)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge=2)) +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Number of BBPs per dolphin per min")+
xlab("Days of recording")
# Whistles
numb_stats_c <- computeStats(acoustic.dta, date, whistling_time_per_dolphin/n_bins)numb_stats_c %>%
ggplot(aes(x=date, y=mean, group=1)) +
geom_errorbar(aes(x=date, ymin=mean-sd, ymax=mean+ci),
color="black", width=.1)+
geom_point() + scale_x_discrete(guide = guide_axis(n.dodge=2)) +
theme_classic() + theme(text=element_text(size=12)) +
ylab("Mean whistling time per dolphin per min (in sec)")+
xlab("Days of recording")
View(acoustic.dta)
sum(acoustic.dta$date=="09/07/2021")
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########################################################################
# STATISTICS
# Author : Loic LEHNHOFF
# Adapted from Yannick OUTREMAN
# Agrocampus Ouest - 2020
#######################################################################
library(pscl)
library(MASS)
library(lmtest)
library(multcomp)
library(emmeans)
library(dplyr) # "%>%" function
library(forcats) # "fct_relevel" function
library(stringr) # "gsub" function
library(rcompanion) # "fullPTable" function
library(multcompView) # "multcompLetters" function
library(ggplot2)
library(pgirmess)
library(postHoc)
#library(tidyquant) # geom_ma() if rolling average needed
n_bins = 187.5 # number of bins per sec for spectrograms (whistles)
################# 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$total_whistles_duration
rm(whistles.dta, bbp.dta, clicks.dta)
# add group IDs
id2020 <- read.table(file=paste0(folder, 'CSV_data/Audio_Data_2020.csv'),
sep = ',', header=TRUE)[1:396,]
id2021 <- read.table(file=paste0(folder, 'CSV_data/Audio_Data_2021.csv'),
sep = ',', header=TRUE)[1:96,]
id2021$ID <- id2021$ID+max(id2020$ID)
id2021$Seq <- id2021$Seq+max(id2020$Seq)
id.dta <- rbind(id2020, id2021)
id.dta$Fichier.Audio <-str_sub(id.dta$Fichier.Audio, -27, -5)
acoustic.dta$ID <- rep(-1, 490)
for (name in acoustic.dta$audio_names){
acoustic.dta$ID[match(name, acoustic.dta$audio_names)] <- id.dta$ID[match(name, id.dta$Fichier.Audio)]
}
acoustic.dta$ID <- as.factor(acoustic.dta$ID)
rm(id2020, id2021, id.dta)
# suppress "T" acoustic data (other groups not tested on our variables)
acoustic.dta <- acoustic.dta[acoustic.dta$acoustic!="T",]
# shuffle dataframe
acoustic.dta <- acoustic.dta[sample(1:nrow(acoustic.dta)), ]acoustic.dta$acoustic <- factor(acoustic.dta$acoustic)
#################### DATA INSPECTION #################################
# Data description
names(acoustic.dta)
# self explenatory except acoustic : correspond to the activation sequence.
# Look for obvious correlations
plot(acoustic.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$total_whistles_duration == 0)/nrow(acoustic.dta)
# 5.8%, 53.7% & 27.1% of our data are zeros. Will have to be dealt with.
# QUESTION: This study is aimed at understanding if dolphin's acoustic activity
# is influenced by their behavior, the emission of a pinger or a fishing net.
# Dependent variables (Y): number_of_clicks, number_of_bbp, total_whistles_duration.
# Explanatory variables (X): acoustic, fishing_net, behavior, beacon, net, number.
# What are the H0/ H1 hypotheses ?
# H0 : No influence of any of the explanatory variables on a dependant one.
# H1 : Influence of an explanatory variable on a dependent one.
View(acoustic.dta)
sum(acoustic.dta$date=="09/07/2021")
sum(acoustic.dta$acoustic=="T")