Regression
Agathe Dumont
5/31/2021
data & packages
df = readRDS("ecd_obs_agathe.RDS")
df <- df[!is.na(df$obs_ts),]
df[is.na(df)] <- 0
library('rsq')
library(ggfortify)## Loading required package: ggplot2library('AER')## Loading required package: car## Loading required package: carData## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Loading required package: survivalSpecies to consider + Stratification+ Legende+ Distribution
espece<-c("num_Other_bony_fishes","num_Silky_shark","num_Common_dolphinfish")
df$num_Other_bony_fishes=df$`num_Other bony fishes`
df$num_Common_dolphinfish=df$`num_Common dolphinfish`
df$num_Silky_shark=df$`num_Silky shark`
df$group = paste0("O",df$ocean,"G",df$code_assoc_groupe)
# Création d'une variable groupe
# O1G1, O1G2, O2G1, O2G2
# O1: Atlantique 02: Indien
# G1: Floating object G2: Free swiming school
## Par océan et banc
df1<-df[df$group =="O1G1",]
# df1 contient tous les calais qui sont sont O1G1
df2<-df[df$group =="O1G2",]
# df2 contient tous les calais qui sont O1G2
df3<-df[df$group =="O2G1",]
# df3 contient tous les calais qui sont O2G1
df4<-df[df$group =="O2G2",]
# df4 contient tous les calais qui sont O2G2
# légende -----------------------------------------------
strat<-c("O1G1","O1G2","O2G1","O2G2")
data_list<-list(O1G1=df1,O1G2=df2,O2G1=df3,O2G2=df4)
# Distribution----------------------------------------
famille<-"poisson"Results regression
res_regression<-list()
for (i in 1:length(espece)){
for (j in 1:length(data_list)){
data <- data_list[[j]]
form <- formula(paste(espece[i],"capture_total_target_tunas_corrigee", sep="~"))
reg <- glm(form, data=data,family=famille)
gr=strat[j]
sp=gsub("^num_","",espece[i])
fn=paste(gr,sp,sep=".")
res_regression[[paste(gr,sp,sep=".")]] = list(sp=sp,gr=gr,fn=fn,reg=reg)
}
}Pvalues & overdispersion
pvalues<-list()
for (v in res_regression){
overdisp<-summary(v$reg)$deviance/summary(v$reg)$df.residual
test<-dispersiontest(v$reg)$p.value[[1]]
disp<-dispersiontest(v$reg)$estimate[[1]]
pvalue<-summary(v$reg)$coefficients["capture_total_target_tunas_corrigee","Pr(>|z|)"]
b<-c(pvalue,rsq(v$reg),overdisp,test,disp)
pvalues[[v$fn]]<-b
}## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
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## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)
## Warning in cbind(y, yfit): number of rows of result is not a multiple of vector
## length (arg 2)colnames<-c("Pvalue capture","R-square","Overdispersion","Overdispersion test","Dispersion ")
pvalues.table<-t(as.data.frame(pvalues))
colnames(pvalues.table)<-colnames
pvalues.table## Pvalue capture R-square Overdispersion
## O1G1.Other_bony_fishes 0.000000e+00 2.363634e-01 758.7641105
## O1G2.Other_bony_fishes 2.498381e-05 7.569965e-02 5.5751838
## O2G1.Other_bony_fishes 0.000000e+00 1.841816e-01 295.5319400
## O2G2.Other_bony_fishes 3.523655e-01 1.008813e-06 28.2283986
## O1G1.Silky_shark 0.000000e+00 3.380116e-02 3.2184410
## O1G2.Silky_shark 0.000000e+00 2.344117e-02 3.4743134
## O2G1.Silky_shark 0.000000e+00 2.377944e-01 7.2022793
## O2G2.Silky_shark 5.765928e-01 1.197639e-05 0.7512533
## O1G1.Common_dolphinfish 6.045728e-169 8.379433e-02 20.8424912
## O1G2.Common_dolphinfish 2.416903e-02 9.055430e-03 0.8027919
## O2G1.Common_dolphinfish 8.595653e-251 1.820994e-01 33.5334666
## O2G2.Common_dolphinfish 2.475783e-77 -9.734212e-04 2.9332413
## Overdispersion test Dispersion
## O1G1.Other_bony_fishes 2.041407e-22 1341.299137
## O1G2.Other_bony_fishes 3.396797e-03 87.236100
## O2G1.Other_bony_fishes 1.472409e-06 652.135720
## O2G2.Other_bony_fishes 1.846581e-02 615.483922
## O1G1.Silky_shark 1.598562e-07 8.304379
## O1G2.Silky_shark 1.075953e-02 45.378869
## O2G1.Silky_shark 1.363179e-21 11.343276
## O2G2.Silky_shark 8.993384e-03 8.498579
## O1G1.Common_dolphinfish 5.504921e-04 74.049824
## O1G2.Common_dolphinfish 1.342407e-01 67.390908
## O2G1.Common_dolphinfish 4.660110e-15 65.996285
## O2G2.Common_dolphinfish 1.042627e-01 82.282848regression by fishing mode
ocean 1 = Atlantic
reg_byocean_O1G1<-list(res_regression$O1G1.Other_bony_fishes, res_regression$O1G1.Silky_shark,res_regression$O1G1.Common_dolphinfish)
reg_byocean_O1G2<-list(res_regression$O1G2.Other_bony_fishes, res_regression$O1G2.Silky_shark,res_regression$O1G2.Common_dolphinfish)
for (i in 1:3){
v=reg_byocean_O1G1[[i]]
b1<-summary(v$reg)$coefficients[1]
a1<-summary(v$reg)$coefficients[2]
p=reg_byocean_O1G2[[i]]
b2<-summary(p$reg)$coefficients[1]
a2<-summary(p$reg)$coefficients[2]
#diagnostique
par(mfrow = c(2, 2))
plot(v$reg)
#diagnostique
par(mfrow = c(2, 2))
plot(p$reg)
data1et2<-rbind(df1,df2)
data1et2$group <- as.factor(data1et2$group)
plot<-ggplot(data1et2, aes(x=capture_total_target_tunas_corrigee,y=get(paste("num_",v$sp,sep = ""),data1et2)))+
geom_abline(slope = a1,intercept = b1,color="#20B2AA")+
geom_abline(slope = a2,intercept = b2,color="#FF7F50")+
ggtitle("Regression by fishing mode, Atlantic ocean" )+
geom_point(aes(colour=group))+
ylab(v$sp)
print(plot)
}
Ocean 2 = Indien
reg_byocean_O2G1<-list(res_regression$O2G1.Other_bony_fishes, res_regression$O2G1.Silky_shark,res_regression$O2G1.Common_dolphinfish)
reg_byocean_O2G2<-list(res_regression$O2G2.Other_bony_fishes, res_regression$O2G2.Silky_shark,res_regression$O2G2.Common_dolphinfish)
for (i in 1:3){
v=reg_byocean_O2G1[[i]]
b1<-summary(v$reg)$coefficients[1]
a1<-summary(v$reg)$coefficients[2]
p=reg_byocean_O2G2[[i]]
b2<-summary(p$reg)$coefficients[1]
a2<-summary(p$reg)$coefficients[2]
#diagnostique
par(mfrow = c(2, 2))
plot(v$reg)
#diagnostique
par(mfrow = c(2, 2))
plot(p$reg)
data1et2<-rbind(df3,df4)
data1et2$group <- as.factor(data1et2$group)
plot<-ggplot(data1et2, aes(x=capture_total_target_tunas_corrigee,y=get(paste("num_",v$sp,sep = ""),data1et2)))+
geom_abline(slope = a1,intercept = b1,color="#20B2AA")+
geom_abline(slope = a2,intercept = b2,color="#FF7F50")+
ggtitle("Regression by fishing mode, Indian ocean" )+
geom_point(aes(colour=group))+
ylab(v$sp)
print(plot)
}
Regression by ocean
Fishing mode 1 : Floating object
reg_fishingmode_O1G1<-list(res_regression$O1G1.Other_bony_fishes, res_regression$O1G1.Silky_shark,res_regression$O1G1.Common_dolphinfish)
reg_fishingmode_O2G1<-list(res_regression$O2G1.Other_bony_fishes, res_regression$O2G1.Silky_shark,res_regression$O2G1.Common_dolphinfish)
for (i in 1:3){
v=reg_byocean_O1G1[[i]]
b1<-summary(v$reg)$coefficients[1]
a1<-summary(v$reg)$coefficients[2]
p=reg_byocean_O2G1[[i]]
b2<-summary(p$reg)$coefficients[1]
a2<-summary(p$reg)$coefficients[2]
#diagnostique
par(mfrow = c(2, 2))
plot(v$reg)
#diagnostique
par(mfrow = c(2, 2))
plot(p$reg)
data1et2<-rbind(df1,df3)
data1et2$group <- as.factor(data1et2$group)
plot<-ggplot(data1et2, aes(x=capture_total_target_tunas_corrigee,y=get(paste("num_",v$sp,sep = ""),data1et2)))+
geom_abline(slope = a1,intercept = b1,color="#20B2AA")+
geom_abline(slope = a2,intercept = b2,color="#FF7F50")+
ggtitle("Regression by ocean, Floating object fishing")+
geom_point(aes(colour=group))+
ylab(v$sp)
print(plot)
}
fishing mode 2 : free school
reg_fishingmode_O1G2<-list(res_regression$O1G2.Other_bony_fishes, res_regression$O1G2.Silky_shark,res_regression$O1G2.Common_dolphinfish)
reg_fishingmode_O2G2<-list(res_regression$O2G2.Other_bony_fishes, res_regression$O2G2.Silky_shark,res_regression$O2G2.Common_dolphinfish)
for (i in 1:3){
v=reg_byocean_O1G2[[i]]
b1<-summary(v$reg)$coefficients[1]
a1<-summary(v$reg)$coefficients[2]
p=reg_byocean_O2G2[[i]]
b2<-summary(p$reg)$coefficients[1]
a2<-summary(p$reg)$coefficients[2]
#diagnostique
par(mfrow = c(2, 2))
plot(v$reg)
#diagnostique
par(mfrow = c(2, 2))
plot(p$reg)
data1et2<-rbind(df2,df4)
data1et2$group <- as.factor(data1et2$group)
plot<-ggplot(data1et2, aes(x=capture_total_target_tunas_corrigee,y=get(paste("num_",v$sp,sep = ""),data1et2)))+
geom_abline(slope = a1,intercept = b1,color="#20B2AA")+
geom_abline(slope = a2,intercept = b2,color="#FF7F50")+
ggtitle("Regression by ocean, free school fishing")+
geom_point(aes(colour=group))+
ylab(v$sp)
print(plot)
}
# Regression by Ocean and fishing mode
reg_O1G1<-list(res_regression$O1G1.Other_bony_fishes, res_regression$O1G1.Silky_shark,res_regression$O1G1.Common_dolphinfish)
reg_O1G2<-list(res_regression$O1G2.Other_bony_fishes, res_regression$O1G2.Silky_shark,res_regression$O1G2.Common_dolphinfish)
reg_O2G1<-list(res_regression$O2G1.Other_bony_fishes, res_regression$O2G1.Silky_shark,res_regression$O2G1.Common_dolphinfish)
reg_O2G2<-list(res_regression$O2G2.Other_bony_fishes, res_regression$O2G2.Silky_shark,res_regression$O2G2.Common_dolphinfish)
for (i in 1:3){
v<-reg_O1G1[[i]]
b1<-summary(v$reg)$coefficients[1]
a1<-summary(v$reg)$coefficients[2]
p<-reg_O1G2[[i]]
b2<-summary(p$reg)$coefficients[1]
a2<-summary(p$reg)$coefficients[2]
x<-reg_O2G1[[i]]
b3<-summary(x$reg)$coefficients[1]
a3<-summary(x$reg)$coefficients[2]
z<-reg_O2G2[[i]]
b4<-summary(z$reg)$coefficients[1]
a4<-summary(z$reg)$coefficients[2]
#diagnostique
par(mfrow = c(2,2))
plot(v$reg)
par(mfrow = c(2,2))
plot(p$reg)
par(mfrow = c(2,2))
plot(x$reg)
par(mfrow = c(2,2))
plot(z$reg)
df$group <- as.factor(df$group)
print<-ggplot(df, aes(x=capture_total_target_tunas_corrigee, y=get(paste("num_",v$sp,sep = ""),df)))+
geom_abline(slope = a1,intercept = b1,color="#20B2AA")+
geom_abline(slope = a2,intercept = b2,color="#008000")+
geom_abline(slope = a3,intercept = b3,color="#FF7F50")+
geom_abline(slope = a4,intercept = b4,color="#c541c5")+
ggtitle("Regression by ocean and fishing mode")+
geom_point(aes(colour=group))+
ylab(v$sp)
print(plot)
}
