Scripts-R/1GEV_ggplot_(chap1).R
In proto4426/PissoortThesis: Thesis in Extreme Value Theory
# *Antoine Pissoort* #
## Script providing the main plot for the thesis in the chapter 1 : GEV ##
##########################################################################
load("/home/proto4426/Documents/Thesis/Extreme/R resources/IRM/data1.Rdata")
library(evd)
library(fExtremes)
library(ismev)
library('ggplot2')
## Create data frames for ggplot
'GEV.df.Fun' <-
function (x = seq(-10, 10, length = 10^3), mu1 = 0, sig = 1, ksi = 0) {
df <- data.frame(x = x, density = evd::dgev(x, loc = mu1, scale = sig, shape = ksi),
xi = as.factor(ksi), mu = as.factor(mu1), scale = as.factor(sig))
return(df)
}
'GEVdfFun' <-
function (x = seq(-10, 10, length = 10^3), mu = 0, sig = 1, ksi = 0) {
if (ksi ==0) { dens <- exp(-x) * exp(-exp(-x)) }
else
s <- (1 + ksi * (x - mu)/sig)^(-(ksi)^-1 - 1)
t <- (1 + ksi * (x - mu)/sig)^(-(ksi)^-1)
if (ksi < 0) {dens <- s * exp(-t) * ( (x - mu)/sig < -1/ksi ) }
if (ksi > 0) {dens <- sig^{-1} * s * exp(-t) * ( (x - mu)/sig > -1/ksi ) }
df <- data.frame(x = x, density = dens,xi = as.factor(ksi),
mu = as.factor(mu), scale = as.factor(sig))
return(df)
}
GEVdf <- rbind(GEV.df.Fun(ksi = -.5),GEV.df.Fun(ksi = 0),GEV.df.Fun(ksi = .5))
GEVdf <- rbind(GEVdfFun(ksi = -.5),GEVdfFun(ksi = 0),GEVdfFun(ksi = .5))
# Dealing with endpoints
endpoint_w <- 0 - (1 / -0.5)
endpoint_f <- 0 - (1 / 0.5)
dens_f <- ifelse(GEVdf[GEVdf$xi == 0.5,]$density < endpoint_f, NA,
GEVdf[GEVdf$xi == 0.5,]$density )
GEVdf[GEVdf$xi == 0.5,]$density <- dens_f
# plot the normal distribution as reference
GEVdf <- cbind(GEVdf, norm = dnorm(GEVdf$x))
GEVdf[GEVdf$density < 10^{-312}, ]$density <- NA
pres <- labs(title = expression(paste(underline(bold('Generalized Extreme Value density (')),underline('µ'),
underline(bold('=0,')),
underline(bold(sigma)), underline(bold("=1)")))),
colour = expression(paste(xi,"="),linetype=expression(paste(xi,"="))))
pres <- labs(title = expression(paste(underline(bold('Generalized Extreme Value density')), " (µ",
'=0,',
sigma, "=1)")),
colour = expression(paste(xi,"=")),linetype = expression(paste(xi,"=")))
gf <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres + coord_cartesian(xlim = c(-8, 8)) +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3)+
theme_piss(20, 15, theme_classic() ) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(legend.key = element_rect(colour = "black")) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
geom_point(aes(x = endpoint_f, y = 0),size = 3.5) +
geom_point(aes(x = endpoint_w, y = 0), col="red",size = 3.5)
gf
gright <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(1.8, 5.5), ylim = c(0,.15)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
theme(axis.line = element_line(color="#33666C", size = .3)) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste(bold("Right tails")))) +
geom_point(aes(x = endpoint_w, y = 0), col = "red", size = 3) +
scale_color_discrete(guide=F)
gright
gleft <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(-0.9,-4.5), ylim = c(0,.2)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line = element_line(color="#33666C", size = .3),
legend.title = element_text(colour="#33666C",
size = 18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste(bold("Left tails")))) +
geom_point(aes(x = endpoint_f, y = 0), size = 3) +
scale_color_discrete(guide=F)
gleft
gleft2 <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(-1.2,-4.5), ylim = c(0.0001,.01)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line = element_line(color="#33666C", size = .3),
legend.title = element_text(colour="#33666C",
size = 18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste("Zoom : ", bold("Left tails")))) +
geom_point(aes(x = endpoint_f, y = 0), size = 3) +
scale_color_discrete(guide=F)
gleft2
library(grid)
vpleft <- viewport(width = 0.29,
height = 0.43,
x = 0.234,
y = 0.42)
vpright <- viewport(width = 0.28,
height = 0.415,
x = 0.73,
y = 0.43)
vpleft2 <- viewport(width = 0.29,
height = 0.13,
x = 0.234,
y = 0.12)
gf
print(gleft2, vp = vpleft)
print(gright, vp = vpright)
#print(gleft2, vp = vpleft2)
## Inspect the influence of the parameters mu and sigma on the shape of the distribution
x <- seq(min(TXTN_closed$TX), max(TXTN_closed$TX) +4, length = 10^3)
GEVmu <- rbind(GEV.df.Fun(x = x, ksi = -.5, mu = 25),GEV.df.Fun(x = x,ksi = 0, mu = 25),
GEV.df.Fun(x = x,ksi = .5, mu = 25),
GEV.df.Fun(x = x,ksi = -.5, mu = 30),GEV.df.Fun(x = x,ksi = 0, mu = 30),
GEV.df.Fun(x = x,ksi = .5, mu = 30),
GEV.df.Fun(x = x,ksi = -.5, mu = 35),GEV.df.Fun(x = x,ksi = 0, mu = 35),
GEV.df.Fun(x = x,ksi = .5, mu = 35))
titles <- labs( title = expression(paste("GEV densities where ", sigma, "=1 and different values for ", mu)),
caption = expression(italic("The endpoints of the distributions are not clearly displayed here for convenience, but you can easily find them.")),
colour = expression(paste(xi,"=")),linetype = expression(paste(mu,"=")))
gmu <- ggplot(GEVmu, aes(linetype = mu, colour = xi)) + geom_line(aes(x = x, y = density)) +
theme_bw() + theme_piss(legend.position = c(.96, .58)) + coord_cartesian(xlim = c(22, 40), ylim = c(0.015, .435)) +
geom_vline(xintercept = 25) + geom_vline(xintercept = 30, linetype = 3) +
geom_vline(xintercept = 35, linetype = 2) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) + titles
gmu
GEVsig <- rbind(GEV.df.Fun(ksi = -.5, sig = .5),GEV.df.Fun(ksi = 0, sig = .5),
GEV.df.Fun(ksi = .5, sig = .5),
GEV.df.Fun(ksi = -.5, sig = 1),GEV.df.Fun(ksi = 0, sig = 1),
GEV.df.Fun(ksi = .5, sig = 1),
GEV.df.Fun(ksi = -.5, sig = 1.5),GEV.df.Fun(ksi = 0, sig = 1.5),
GEV.df.Fun(ksi = .5, sig = 1.5))
# levels(GEVsig$scale) <- c(expression(paste(sigma, "= 0.5")), expression(paste(sigma, "= 1")),
# expression(paste(sigma, "= 1.5" )))
titles <- labs( title = expression(paste("GEV densities where µ=0 and ", sigma, " =")),
colour = expression(paste(xi,"=")))
gsig <- ggplot(GEVsig, aes(colour = xi)) +
geom_vline(xintercept = 0) +
geom_line(aes(x = x, y = density)) +
coord_cartesian(xlim = c(-5, 5), ylim = c(0.03,.81)) + theme_piss(18, 15, legend.position = c(.96, .79)) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +
facet_wrap(~scale) + titles
gsig
#grid_arrange_legend(gmu, gsig, ncol = 1, nrow = 2, position = "bottom")
grid.arrange(gmu, gsig, nrow = 2)
#### For our fitted model ?
gev_data <- GEVdfFun(mu = gev_fit$mle[1], sig = gev_fit$mle[2],
ksi = gev_fit$mle[3] )
gev_data <- GEV.df.Fun(mu1 = gev_fit$mle[1], sig = gev_fit$mle[2],
ksi = gev_fit$mle[3] )
ggplot(gev_data) +
geom_line(aes(x = x, y = density)) + coord_cartesian(xlim = c(-5, 5)) +
theme(plot.title = element_text(size=18, hjust=0.5,
colour = "#330000", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#330000")) +
theme(legend.title = element_text(colour="#330000",
size=18, face="bold")) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +theme_bw()
################# For Beamer Presentation
p <- ggplot(TXTN_closed) + geom_density(aes(x = TX), fill = "grey") +
labs(title = expression(paste("Distribution of maximum ",
underline("daily"), " temperatures (TX) in Uccle [1901-2016]") ) )
d <- ggplot_build(p)$data[[1]]
p + geom_area(data = subset(d, x > 30), aes(x=x, y=y), fill="green3") +
geom_segment(x=30, xend = 30,
y=0, yend=approx(x = d$x, y = d$y, xout = 30)$y,
colour="red", size=1) + theme_classic() +
theme(plot.title = element_text(size=18, hjust=0.5,
colour = "#33666C", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#33666C")) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
geom_segment(aes(x = 30, xend = 31, y = .011, yend = .003),
arrow = arrow(length = unit(0.5, "cm")), col = "green3") +
geom_segment(aes(x = 25, xend = 28, y = .005, yend = .002),
arrow = arrow(length = unit(0.8, "cm")), col = "blue") +
annotate("text", label = "Excesses over a",
x = 28, y = .016, col = "green3", size = 9, fontface = 2) +
annotate("text", label = "threshold",
x = 28, y = .013, col = "red", size = 9, fontface = 2) +
annotate("text", label = "Annual maxima",
x = 19, y = .007, col = "blue", size = 8, fontface = 2) +
annotate("text",
label = paste("n = ", as.character(nrow(TXTN_closed[TXTN_closed$TX>30, ]))),
x = 34.5, y = .007, col = "green3", size = 6, fontface = 1) +
annotate("text", label = "n = 116",
x = 34.5, y = .005, col = "blue", size = 6, fontface = 1) +
geom_point(data = max_years$df, aes(x = Max, y = 0.001), size = 0.5, col = "blue" )
library(evd)
'gpd.dfFun' <-
function (x = seq(0, 100, length = 10^3), mu1 = 0, sig = 1, ksi = 0) {
df <- data.frame(x = x, density = evd::dgpd(x, loc = mu1, scale = sig, shape = ksi),
xi = as.factor(ksi), mu = as.factor(mu1), scale = as.factor(sig))
return(df)
}
GPDdf <- rbind(gpd.dfFun(ksi = -.2),gpd.dfFun(ksi = 0),gpd.dfFun(ksi = 1))
gf <- ggplot(GPDdf, aes(x = x, y = density, colour = xi )) +
geom_line() +
pres + coord_cartesian(xlim = c(0.5, 6)) + theme_classic() +
#geom_line(aes(x = x, y = norm), linetype = 3)+
theme(plot.title = element_text(size = 20, hjust=0.5,
colour = "#33666C", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#33666C")) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(legend.key = element_rect(colour = "black")) +
guides(colour = guide_legend(override.aes = list(size = 1.5)))
gf
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# *Antoine Pissoort* #
## Script providing the main plot for the thesis in the chapter 1 : GEV ##
##########################################################################
load("/home/proto4426/Documents/Thesis/Extreme/R resources/IRM/data1.Rdata")
library(evd)
library(fExtremes)
library(ismev)
library('ggplot2')
## Create data frames for ggplot
'GEV.df.Fun' <-
function (x = seq(-10, 10, length = 10^3), mu1 = 0, sig = 1, ksi = 0) {
df <- data.frame(x = x, density = evd::dgev(x, loc = mu1, scale = sig, shape = ksi),
xi = as.factor(ksi), mu = as.factor(mu1), scale = as.factor(sig))
return(df)
}
'GEVdfFun' <-
function (x = seq(-10, 10, length = 10^3), mu = 0, sig = 1, ksi = 0) {
if (ksi ==0) { dens <- exp(-x) * exp(-exp(-x)) }
else
s <- (1 + ksi * (x - mu)/sig)^(-(ksi)^-1 - 1)
t <- (1 + ksi * (x - mu)/sig)^(-(ksi)^-1)
if (ksi < 0) {dens <- s * exp(-t) * ( (x - mu)/sig < -1/ksi ) }
if (ksi > 0) {dens <- sig^{-1} * s * exp(-t) * ( (x - mu)/sig > -1/ksi ) }
df <- data.frame(x = x, density = dens,xi = as.factor(ksi),
mu = as.factor(mu), scale = as.factor(sig))
return(df)
}
GEVdf <- rbind(GEV.df.Fun(ksi = -.5),GEV.df.Fun(ksi = 0),GEV.df.Fun(ksi = .5))
GEVdf <- rbind(GEVdfFun(ksi = -.5),GEVdfFun(ksi = 0),GEVdfFun(ksi = .5))
# Dealing with endpoints
endpoint_w <- 0 - (1 / -0.5)
endpoint_f <- 0 - (1 / 0.5)
dens_f <- ifelse(GEVdf[GEVdf$xi == 0.5,]$density < endpoint_f, NA,
GEVdf[GEVdf$xi == 0.5,]$density )
GEVdf[GEVdf$xi == 0.5,]$density <- dens_f
# plot the normal distribution as reference
GEVdf <- cbind(GEVdf, norm = dnorm(GEVdf$x))
GEVdf[GEVdf$density < 10^{-312}, ]$density <- NA
pres <- labs(title = expression(paste(underline(bold('Generalized Extreme Value density (')),underline('µ'),
underline(bold('=0,')),
underline(bold(sigma)), underline(bold("=1)")))),
colour = expression(paste(xi,"="),linetype=expression(paste(xi,"="))))
pres <- labs(title = expression(paste(underline(bold('Generalized Extreme Value density')), " (µ",
'=0,',
sigma, "=1)")),
colour = expression(paste(xi,"=")),linetype = expression(paste(xi,"=")))
gf <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres + coord_cartesian(xlim = c(-8, 8)) +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3)+
theme_piss(20, 15, theme_classic() ) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(legend.key = element_rect(colour = "black")) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
geom_point(aes(x = endpoint_f, y = 0),size = 3.5) +
geom_point(aes(x = endpoint_w, y = 0), col="red",size = 3.5)
gf
gright <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(1.8, 5.5), ylim = c(0,.15)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
theme(axis.line = element_line(color="#33666C", size = .3)) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste(bold("Right tails")))) +
geom_point(aes(x = endpoint_w, y = 0), col = "red", size = 3) +
scale_color_discrete(guide=F)
gright
gleft <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() + pres +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(-0.9,-4.5), ylim = c(0,.2)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line = element_line(color="#33666C", size = .3),
legend.title = element_text(colour="#33666C",
size = 18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste(bold("Left tails")))) +
geom_point(aes(x = endpoint_f, y = 0), size = 3) +
scale_color_discrete(guide=F)
gleft
gleft2 <- ggplot(GEVdf, aes(x = x, y = density, colour = xi )) +
geom_line() +
geom_line(aes(x = x, y = norm, col = "normal"), col = "black", linetype = 3) +
coord_cartesian(xlim = c(-1.2,-4.5), ylim = c(0.0001,.01)) +
theme_piss(18, 15, theme_minimal()) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line = element_line(color="#33666C", size = .3),
legend.title = element_text(colour="#33666C",
size = 18, face="bold")) +
theme(axis.title = element_blank()) +
labs(title = expression(paste("Zoom : ", bold("Left tails")))) +
geom_point(aes(x = endpoint_f, y = 0), size = 3) +
scale_color_discrete(guide=F)
gleft2
library(grid)
vpleft <- viewport(width = 0.29,
height = 0.43,
x = 0.234,
y = 0.42)
vpright <- viewport(width = 0.28,
height = 0.415,
x = 0.73,
y = 0.43)
vpleft2 <- viewport(width = 0.29,
height = 0.13,
x = 0.234,
y = 0.12)
gf
print(gleft2, vp = vpleft)
print(gright, vp = vpright)
#print(gleft2, vp = vpleft2)
## Inspect the influence of the parameters mu and sigma on the shape of the distribution
x <- seq(min(TXTN_closed$TX), max(TXTN_closed$TX) +4, length = 10^3)
GEVmu <- rbind(GEV.df.Fun(x = x, ksi = -.5, mu = 25),GEV.df.Fun(x = x,ksi = 0, mu = 25),
GEV.df.Fun(x = x,ksi = .5, mu = 25),
GEV.df.Fun(x = x,ksi = -.5, mu = 30),GEV.df.Fun(x = x,ksi = 0, mu = 30),
GEV.df.Fun(x = x,ksi = .5, mu = 30),
GEV.df.Fun(x = x,ksi = -.5, mu = 35),GEV.df.Fun(x = x,ksi = 0, mu = 35),
GEV.df.Fun(x = x,ksi = .5, mu = 35))
titles <- labs( title = expression(paste("GEV densities where ", sigma, "=1 and different values for ", mu)),
caption = expression(italic("The endpoints of the distributions are not clearly displayed here for convenience, but you can easily find them.")),
colour = expression(paste(xi,"=")),linetype = expression(paste(mu,"=")))
gmu <- ggplot(GEVmu, aes(linetype = mu, colour = xi)) + geom_line(aes(x = x, y = density)) +
theme_bw() + theme_piss(legend.position = c(.96, .58)) + coord_cartesian(xlim = c(22, 40), ylim = c(0.015, .435)) +
geom_vline(xintercept = 25) + geom_vline(xintercept = 30, linetype = 3) +
geom_vline(xintercept = 35, linetype = 2) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) + titles
gmu
GEVsig <- rbind(GEV.df.Fun(ksi = -.5, sig = .5),GEV.df.Fun(ksi = 0, sig = .5),
GEV.df.Fun(ksi = .5, sig = .5),
GEV.df.Fun(ksi = -.5, sig = 1),GEV.df.Fun(ksi = 0, sig = 1),
GEV.df.Fun(ksi = .5, sig = 1),
GEV.df.Fun(ksi = -.5, sig = 1.5),GEV.df.Fun(ksi = 0, sig = 1.5),
GEV.df.Fun(ksi = .5, sig = 1.5))
# levels(GEVsig$scale) <- c(expression(paste(sigma, "= 0.5")), expression(paste(sigma, "= 1")),
# expression(paste(sigma, "= 1.5" )))
titles <- labs( title = expression(paste("GEV densities where µ=0 and ", sigma, " =")),
colour = expression(paste(xi,"=")))
gsig <- ggplot(GEVsig, aes(colour = xi)) +
geom_vline(xintercept = 0) +
geom_line(aes(x = x, y = density)) +
coord_cartesian(xlim = c(-5, 5), ylim = c(0.03,.81)) + theme_piss(18, 15, legend.position = c(.96, .79)) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +
facet_wrap(~scale) + titles
gsig
#grid_arrange_legend(gmu, gsig, ncol = 1, nrow = 2, position = "bottom")
grid.arrange(gmu, gsig, nrow = 2)
#### For our fitted model ?
gev_data <- GEVdfFun(mu = gev_fit$mle[1], sig = gev_fit$mle[2],
ksi = gev_fit$mle[3] )
gev_data <- GEV.df.Fun(mu1 = gev_fit$mle[1], sig = gev_fit$mle[2],
ksi = gev_fit$mle[3] )
ggplot(gev_data) +
geom_line(aes(x = x, y = density)) + coord_cartesian(xlim = c(-5, 5)) +
theme(plot.title = element_text(size=18, hjust=0.5,
colour = "#330000", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#330000")) +
theme(legend.title = element_text(colour="#330000",
size=18, face="bold")) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +theme_bw()
################# For Beamer Presentation
p <- ggplot(TXTN_closed) + geom_density(aes(x = TX), fill = "grey") +
labs(title = expression(paste("Distribution of maximum ",
underline("daily"), " temperatures (TX) in Uccle [1901-2016]") ) )
d <- ggplot_build(p)$data[[1]]
p + geom_area(data = subset(d, x > 30), aes(x=x, y=y), fill="green3") +
geom_segment(x=30, xend = 30,
y=0, yend=approx(x = d$x, y = d$y, xout = 30)$y,
colour="red", size=1) + theme_classic() +
theme(plot.title = element_text(size=18, hjust=0.5,
colour = "#33666C", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#33666C")) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
geom_segment(aes(x = 30, xend = 31, y = .011, yend = .003),
arrow = arrow(length = unit(0.5, "cm")), col = "green3") +
geom_segment(aes(x = 25, xend = 28, y = .005, yend = .002),
arrow = arrow(length = unit(0.8, "cm")), col = "blue") +
annotate("text", label = "Excesses over a",
x = 28, y = .016, col = "green3", size = 9, fontface = 2) +
annotate("text", label = "threshold",
x = 28, y = .013, col = "red", size = 9, fontface = 2) +
annotate("text", label = "Annual maxima",
x = 19, y = .007, col = "blue", size = 8, fontface = 2) +
annotate("text",
label = paste("n = ", as.character(nrow(TXTN_closed[TXTN_closed$TX>30, ]))),
x = 34.5, y = .007, col = "green3", size = 6, fontface = 1) +
annotate("text", label = "n = 116",
x = 34.5, y = .005, col = "blue", size = 6, fontface = 1) +
geom_point(data = max_years$df, aes(x = Max, y = 0.001), size = 0.5, col = "blue" )
library(evd)
'gpd.dfFun' <-
function (x = seq(0, 100, length = 10^3), mu1 = 0, sig = 1, ksi = 0) {
df <- data.frame(x = x, density = evd::dgpd(x, loc = mu1, scale = sig, shape = ksi),
xi = as.factor(ksi), mu = as.factor(mu1), scale = as.factor(sig))
return(df)
}
GPDdf <- rbind(gpd.dfFun(ksi = -.2),gpd.dfFun(ksi = 0),gpd.dfFun(ksi = 1))
gf <- ggplot(GPDdf, aes(x = x, y = density, colour = xi )) +
geom_line() +
pres + coord_cartesian(xlim = c(0.5, 6)) + theme_classic() +
#geom_line(aes(x = x, y = norm), linetype = 3)+
theme(plot.title = element_text(size = 20, hjust=0.5,
colour = "#33666C", face="bold")) +
theme(axis.title = element_text(face = "bold", size= 15,
colour = "#33666C")) +
theme(legend.title = element_text(colour="#33666C",
size=18, face="bold")) +
theme(legend.key = element_rect(colour = "black")) +
guides(colour = guide_legend(override.aes = list(size = 1.5)))
gf
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