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SAMTechReportFigures.R
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library(car)
library(MASS)
library(boot)
library(dplyr)
library(plyr)
library(gdata)
library(ggplot2)
library(multcompView)
#sources
source("C:/GitCode/r-AbSpatialAnalyses/GraphsUtils.r") # source of the TukeyHSD letters in ggplots
#########
# LOAD GwthResults050916.RData from R_stuff/SAM/Logistic
#########
####RESULTS ANALYSIS
GwthResults$PctLessLML<-100-GwthResults$PctU.LML
#############################
# l50% by zone figure and ANOVA
#############################
#anova L50 by Zone
boxcox(GwthResults$LD50^3.6~GwthResults$Zone)
fit<-aov(LD50^3.6~Zone, data=GwthResults)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
tHSDlm<- TukeyHSD(fit, ordered = FALSE, conf.level = 0.95)
tHSDlm
#set working dataframe for Tukey label function
ASM<-GwthResults
#Boxplot by maturity L%
ggplot(GwthResults, aes(x=Zone, y=LD50)) +
xlab("Zone") +
ylab(expression(paste(LM['50%']~'(mm)')))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
theme_bw()+#white background
theme(legend.position="none",
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))+
geom_text(data = generate_label_df(tHSDlm, "Zone"), aes(x = plot.labels, y = 60, label = labels))
ddply(GwthResults,.(Zone), summarize, mnLD50 = median(LD50, na.rm=T), number = length(LD50))
################
# SUMSTATS by Block
################
BlockSumStats<-ddply(GwthResults,.(BlockNo, Zone), summarize, n = length(SiteCode),
mn.L50 = mean(LD50, na.rm=T), mn.LCI50 = mean(Ld50BootL95, na.rm=T), mn.UCI50 = mean(Ld50BootU95, na.rm=T),
mn.L95 = mean(LD95, na.rm=T), mn.LCI95 = mean(Ld95BootL95, na.rm=T), mn.UCI95 = mean(Ld95BootU95, na.rm=T),
mn.IQR = mean(IQR, na.rm=T), sd.IQR = sd(IQR, na.rm=T),
mn.pct.L50 = mean(PctL50, na.rm=T), sd.pct.L50 = mean(PctL50, na.rm=T),
mn.Bootrange.L50 = mean(Ld50BootRange, na.rm=T), sd.Bootrange.L50 = mean(Ld50BootRange, na.rm=T),
mn.eLML = mean(eLML, na.rm=T) , sd.eLML = sd(eLML, na.rm=T), se.eLML = sd(eLML, na.rm=T)/sqrt(length(eLML)),
mn.SLmax = mean(SLmax, na.rm=T),
mn.PctU.LML = mean(PctU.LML, na.rm =T), sd.PctU.LML = sd(PctU.LML, na.rm =T))
# mn.eLMLbootL95 = mean(eLMLbootL95, na.rm=T) , sd.eLMLbootL95 = sd(eLMLbootL95, na.rm=T),
# mn.eLMLbootU95 = mean(eLMLbootU95, na.rm=T) , sd.eLMLbootU95 = sd(eLMLbootU95, na.rm=T),
# diffLML = mean(LMLDiff, na.rm=T))
write.csv(BlockSumStats, file= "blockSAMstats.csv")
#difference between maxSL and LD50
BlockSumStats$LD50SLmaxDiff<-BlockSumStats$mn.SLmax-BlockSumStats$mn.L50
mean(BlockSumStats$LD50SLmaxDiff)
range(BlockSumStats$LD50SLmaxDiff)
#############################
# (%<L50%) comparison at l50
#############################
#Range of IQ against CIrangeL50%
plot(GwthResults$PctL50, GwthResults$Ld50BootRange)
#
#remove outliers from dataset and run lm
pick <- which(GwthResults$Ld50BootRange >10)
IQRlm <- GwthResults[-pick,]
IQRlm <- droplevels(IQRlm)
#anova diferences L%
boxcox(log(IQRlm$Ld50BootRange)~IQRlm$PctL50)
fit<-lm(log(Ld50BootRange)~IQR, data=IQRlm)
summary(fit)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
ggplot(data = GwthResults, aes(x=PctL50, y=Ld50BootRange, color=ifelse(Ld50BootRange>10, 'red', 'black'))) +
geom_point()+
xlab(bquote('Proportion <'~LM['50%']~'(%)')) + ylab(bquote('C.I. Range'~LM['50%']~'(mm)'))+
#geom_smooth(method=lm, se=F, fill='Black', fullrange=F, size=1.2, color='black')+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
theme(legend.position=c(0.9, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
#############################
# END- (%<L50%) comparison at l50
#############################
#############################
# IQrange (L75-L25) CIrange comparison at l50
#############################
#PERCENTAGE IQR and bootstrapped CI of LM50
ggplot(data=GwthResults, aes(x=PctIQR, y=Ld50BootRange, color=ifelse(Ld50BootRange>10, 'red', 'black'))) +
geom_point()+
xlab(bquote(~IQ['Range']~'%')) +
ylab(bquote(~C.I.['Range']~LM['50%']~'(mm)'))+
#geom_smooth(method=lm, se=F, fill='Black', fullrange=F, size=1.2, color='black')+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
theme(legend.position=c(0.9, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
plot(GwthResults$PctIQR, GwthResults$Ld50BootRange)
#anova diferences L%
boxcox(log(GwthResultsLM50.10MM$Ld50BootRange)~GwthResultsLM50.10MM$PctIQR)
fit<-lm(log(GwthResultsLM50.10MM$Ld50BootRange)~GwthResultsLM50.10MM$PctIQR)
summary(fit)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
#Range of IQ against CIrangeL50%
plot(GwthResults$IQR, GwthResults$Ld50BootRange)
#
#remove outliers from dataset and run lm
pick <- which(GwthResults$Ld50BootRange >10)
IQRlm <- GwthResults[-pick,]
IQRlm <- droplevels(IQRlm)
#anova diferences L%
boxcox(IQRlm$Ld50BootRange^-0.7~IQRlm$IQR)
fit<-lm(Ld50BootRange^-0.7~IQR, data=IQRlm)
summary(fit)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
ggplot(data = GwthResults, aes(x=IQR, y=Ld50BootRange, color=ifelse(Ld50BootRange>10, 'red', 'black'))) +
geom_point()+
xlab(bquote(~IQ['range']~'(mm)')) + ylab(bquote('C.I. Range'~LM['50%']~'(mm)'))+
#geom_smooth(method=lm, se=F, fill='Black', fullrange=F, size=1.2, color='black')+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
theme(legend.position=c(0.9, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
# HISTOGRAM CI RANGE
mn_ld50ci<-mean(GwthResults$Ld50BootRange, na.rm=T)
#md_ld50ci<-median(GwthResults$Ld50BootRange, na.rm=T)
Usd_ld50ci<-mn_ld50ci+sd(GwthResults$Ld50BootRange, na.rm=T)
Lsd_ld50ci<-mn_ld50ci-sd(GwthResults$Ld50BootRange, na.rm=T)
####Histogram of CIrangeL50
ggplot(data = GwthResults, aes(x=Ld50BootRange)) +
geom_histogram(bins = 30)+
xlab(bquote(~CI['range']~'LM'['50%']))+
#ylim(0,120)+
geom_vline(xintercept=mn_ld50ci, colour = 'red', linetype= 3, size=1.5)+
geom_vline(xintercept=Usd_ld50ci, linetype= 3, size=1.2)+
geom_vline(xintercept=Lsd_ld50ci, linetype= 3, size=1.2)+
theme_bw()+
#scale_fill_identity()+ #this makes sure the color follows the color argument above in aes()
theme(legend.position=c(0.9, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
#############################
# END- IQrange (L75-L25) CIrange comparison at l50
#############################
#
#############################
# Figure 6 L50 and L95 CIrange comparison
#############################
rangeCI95<-as.data.frame(GwthResults$Ld95BootRange)
colnames(rangeCI95)[1] <- "CIRange"
rangeCI95$LM<-"LM95%"
rangeCI50<-as.data.frame(GwthResults$Ld50BootRange)
colnames(rangeCI50)[1] <- "CIRange"
rangeCI50$LM<-"LM50%"
CIRange<-rbind(rangeCI50, rangeCI95)
rm(rangeCI50, rangeCI95)
#anova diferences L%
boxcox(CIRange$CIRange^-0.35~CIRange$LM)
fit<-aov(CIRange^-0.35~LM, data=CIRange)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
tHSDlm<- TukeyHSD(fit, ordered = FALSE, conf.level = 0.95)
tHSDlm
#set working dataframe for Tukey label function
ASM<-CIRange
#Boxplot by maturity L%
ggplot(CIRange, aes(x=LM, y=CIRange)) +
xlab("Maturity Estimate") + ylab(bquote(~CI['range']~'LM'))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
theme_bw()+#white background
theme(legend.position="none",
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
#geom_text(data = generate_label_df(tHSDlm, "LM"), aes(x = plot.labels, y = 0.2, label = labels))
ddply(CIRange,.(LM), summarize, M = median(CIRange, na.rm=T))
#############################
# END - Figure 6 L50 and L95 CIrange comparison
#############################
#############################
# Mrange (L95-L05) CIrange comparison at l50 (figure not currently used)
#############################
#Range of maturity onset (l95-L05) against CIraangeL50%
GwthResults$Mrange<-GwthResults$LD95-GwthResults$LD05
plot(GwthResults$Mrange, GwthResults$Ld50BootRange)
#anova diferences L%
boxcox(GwthResults$Ld50BootRange^-0.7~GwthResults$Mrange)
fit<-lm(GwthResults$Ld50BootRange^-0.7~GwthResults$Mrange)
summary(fit)
anova(fit)
par(mfrow = c(2,2))
plot(fit)
par(mfrow = c(1,1))
ggplot(data=GwthResults, aes(x=Mrange, y=Ld50BootRange, color=ifelse(Ld50BootRange>10, 'red', 'black'))) +
geom_point()+
xlab(bquote(~M['range']~'(mm)')) + ylab(bquote('C.I. Range'~LM['50%']~'(mm)'))+
#geom_smooth(method=lm, se=F, fill='Black', fullrange=F, size=1.2, color='black')+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
theme(legend.position=c(0.9, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
#############################
# END- Mrange (L95-L05) CIrange comparison at l50 (figure not currently used)
#############################
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
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#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
#############################
# CIrange perecentages
#############################
#% below thresholds
pick <- which(GwthResults$Ld50BootRange <5)
ci5less <- GwthResults[pick,]
ci5less <- droplevels(ci5less)
dim(ci5less)
94/260*100
#Boxplot by MLStc by zone
ggplot(GwthResults, aes(x=Zone, y=eLML)) +
xlab("Zone") + ylab("eLML")+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
theme_bw()+#white background
theme(legend.position="none",
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
#geom_text(data = generate_label_df(tHSDlm, "Zone"), aes(x = plot.labels, y = 60, label = labels))
#
#########################
# Plots for eLML L50%
#########################
#
# add ylimits for plots
Zone<-c('BS', "CW", "E", "N", "W")
Top<-c(112, 135, 150, 135, 150)
Bottom<-c(95, 110, 130, 90, 135)
ylimits<-data.frame(Zone, Top, Bottom)
GwthResults<-left_join(GwthResults, ylimits, by = "Zone")
setwd("D:/Fisheries Research/Abalone/SAM")
doPlot = function(LFPlot) {
dum = subset(GwthResults, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(y=eLML, x=as.factor(BlockNo))) +
xlab("BlockNo") +
ylab("eLML (mm)") +
labs(title= dum$Zone, size=10)+
ylim(min(dum$Bottom), max(dum$Top))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
geom_hline(yintercept=dum$LML, colour = 'red', linetype= 3)+
theme_bw()+
theme(legend.title=element_blank(),
legend.text = element_text(size=14),
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position="none")
ggsave(sprintf("%s_eLMLplot.tiff", LFPlot, width = 6, height = 8, units = "cm"))
print(ggobj)
}
lapply(unique(GwthResults$Zone), doPlot)
################
# SUMSTATS by Block
################
BlockSumStats<-ddply(GwthResults,.(BlockNo, Zone), summarize, n = length(SiteCode),
mn.L50 = mean(LD50, na.rm=T), mn.LCI50 = mean(Ld50BootL95, na.rm=T), mn.UCI50 = mean(Ld50BootU95, na.rm=T),
mn.L95 = mean(LD95, na.rm=T), mn.LCI95 = mean(Ld95BootL95, na.rm=T), mn.UCI95 = mean(Ld95BootU95, na.rm=T),
mn.IQR = mean(IQR, na.rm=T), sd.IQR = sd(IQR, na.rm=T),
mn.pct.L50 = mean(PctL50, na.rm=T), sd.pct.L50 = mean(PctL50, na.rm=T),
mn.Bootrange.L50 = mean(Ld50BootRange, na.rm=T), sd.Bootrange.L50 = mean(Ld50BootRange, na.rm=T),
mn.eLML = mean(eLML, na.rm=T) , sd.eLML = sd(eLML, na.rm=T), se.eLML = sd(eLML, na.rm=T)/sqrt(length(eLML)))
doPlot = function(LFPlot) {
dum = subset(GwthResults, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(x=LD50, y=eLML)) +
geom_point(aes(colour=GrowthSite), size=3)+
xlab(bquote(''~LM['50%']~'(mm)')) + ylab(bquote(''~eLML['']~'(mm)'))+
geom_smooth(method=lm, se=F, color='grey', fullrange=F, size=1.2, color='black')+
#geom_text_repel(aes(label=BlockNo), size=3)+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
#scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
# theme(legend.position=c(0.1, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
}
lapply(unique(GwthResults$Zone), doPlot)
#
## PCT LML protection
#
doPlot = function(LFPlot) {
dum = subset(GwthResults, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(y=PctU.LML, x=as.factor(BlockNo))) +
xlab("BlockNo") +
ylab("Percentage Population Protected") +
labs(title= dum$Zone, size=10)+
#ylim(min(dum$sd.eLML-10), max(dum$sd.eLML+10))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
#geom_hline(yintercept=dum$LML, colour = 'red', linetype= 3)+
theme_bw()+
theme(legend.title=element_blank(),
legend.text = element_text(size=14),
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position="none")
ggsave(sprintf("%s_PctProtected.tiff", LFPlot))
print(ggobj)
}
lapply(unique(BlockSumStats$Zone), doPlot)
#
#########################
# Plots for eLML L50% on LM50 < 5mm
#########################
#
#
################
# SUMSTATS by Block
################
GwthResultsLM50.10MM<-droplevels(subset(GwthResults, Ld50BootRange <= 10))
GwthResultsLM50.5MM<-droplevels(subset(GwthResults, Ld50BootRange <= 5))
L50RangeMean<-mean(GwthResults$Ld50BootRange, na.rm=T)
pick <- which(GwthResults$Ld50BootRange <= L50RangeMean)
GwthRltLD50Mn <- GwthResults[pick,]
GwthRltLD50Mn <- droplevels(GwthRltLD50Mn)
doPlot = function(LFPlot) {
dum = subset(GwthResultsLM50.5MM, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(y=eLML, x=as.factor(BlockNo))) +
xlab("BlockNo") +
ylab("eLML (mm)") +
labs(title= dum$Zone, size=10)+
ylim(min(dum$Bottom), max(dum$Top))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
geom_hline(yintercept=dum$LML, colour = 'red', linetype= 3)+
theme_bw()+
theme(legend.title=element_blank(),
legend.text = element_text(size=14),
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position="none")
ggsave(sprintf("%s_eLML_5mmplot.tiff", LFPlot, width = 6, height = 8, units = "cm"))
print(ggobj)
}
lapply(unique(GwthResultsLM50.5MM$Zone), doPlot)
BlockSumStats5mm<-ddply(GwthResultsLM50.5MM,.(BlockNo, Zone), summarize, n = length(SiteCode),
mn.L50 = mean(LD50, na.rm=T), mn.LCI50 = mean(Ld50BootL95, na.rm=T), mn.UCI50 = mean(Ld50BootU95, na.rm=T),
mn.L95 = mean(LD95, na.rm=T), mn.LCI95 = mean(Ld95BootL95, na.rm=T), mn.UCI95 = mean(Ld95BootU95, na.rm=T),
mn.IQR = mean(IQR, na.rm=T), sd.IQR = sd(IQR, na.rm=T),
mn.pct.L50 = mean(PctL50, na.rm=T), sd.pct.L50 = mean(PctL50, na.rm=T),
mn.Bootrange.L50 = mean(Ld50BootRange, na.rm=T), sd.Bootrange.L50 = mean(Ld50BootRange, na.rm=T),
mn.eLML = mean(eLML, na.rm=T) , sd.eLML = sd(eLML, na.rm=T), se.eLML = sd(eLML, na.rm=T)/sqrt(length(eLML)))
doPlot = function(LFPlot) {
dum = subset(GwthResults, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(x=LD50, y=eLML)) +
geom_point(aes(colour=GrowthSite), size=3)+
xlab(bquote(''~LM['50%']~'(mm)')) + ylab(bquote(''~eLML['']~'(mm)'))+
geom_smooth(method=lm, se=F, color='grey', fullrange=F, size=1.2, color='black')+
#geom_text_repel(aes(label=BlockNo), size=3)+
#ggtitle(paste(dum$SubBlockNo, FishYear))+
#labs(title= Yeardum$SubBlockNo, size=10)+
#geom_histogram(binwidth=50)+
theme_bw()+
#scale_color_identity()+ #this makes sure the color follows the color argument above in aes()
# theme(legend.position=c(0.1, 0.8))+
theme(legend.title=element_blank())+
theme(legend.text = element_text(size=14))+
theme(axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14))+
theme(axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14))
}
lapply(unique(GwthResults$Zone), doPlot)
#
## PCT LML protection
#
doPlot = function(LFPlot) {
dum = subset(GwthResultsLM50.5MM, Zone == LFPlot)
ggobj = ggplot(data = dum, aes(y=Pctless.LML) +
xlab("BlockNo") +
ylab("Percentage Population Protected") +
labs(title= dum$Zone, size=10)+
#ylim(min(dum$sd.eLML-10), max(dum$sd.eLML+10))+
geom_boxplot(outlier.colour = "black", outlier.size = 3)+
#geom_hline(yintercept=dum$LML, colour = 'red', linetype= 3)+
theme_bw()+
theme(legend.title=element_blank(),
legend.text = element_text(size=14),
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position="none")
ggsave(sprintf("%s_PctProtected.tiff", LFPlot))
print(ggobj)
}
lapply(unique(GwthResultsLM50.5MM$Zone), doPlot)
#HISTO of percentage protection
ggplot(data = GwthResults, aes(x=Pctless.LML)) +
xlab("Percent") +
ylab("Number of sites") +
xlim(0,50)+
#labs(title= dum$Zone, size=10)+
#ylim(min(dum$sd.eLML-10), max(dum$sd.eLML+10))+
geom_histogram(breaks=seq(0, 50, by = 5), binwidth=5)+
#geom_hline(yintercept=dum$LML, colour = 'red', linetype= 3)+
theme_bw()+
theme(legend.title=element_blank(),
legend.text = element_text(size=14),
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position="none")+
facet_grid(Zone~ .)