Nothing
R/makeSingleExpertPlot.R
In SHELF: Tools to Support the Sheffield Elicitation Framework
Defines functions
makeSingleExpertPlot
makeSingleExpertPlot <-
function(fit, d = "best", pl = -Inf, pu = Inf,
ql = NA, qu = NA, sf = 3, ex = 1,
lwd = 1, xlab, ylab, percentages ){
if(d == "best"){
d <- fit$best.fitting[ex, 1]
}
if(d == "normal"){
if(pl == -Inf){pl <- qnorm(0.001, fit$Normal[ex,1], fit$Normal[ex,2])}
if(pu == Inf){pu <- qnorm(0.999, fit$Normal[ex,1], fit$Normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- qnorm(ql, fit$Normal[ex,1], fit$Normal[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- qnorm(qu, fit$Normal[ex,1], fit$Normal[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dnorm(x, fit$Normal[ex,1], fit$Normal[ex,2])
dist.title <- paste("Normal (mean = ",
signif(fit$Normal[ex,1], sf),
", sd = ",
signif(fit$Normal[ex,2], sf), ")",
sep="")
}
if(d == "t"){
if(pl == -Inf){pl <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(0.001, fit$Student.t[ex,3])}
if(pu == Inf){pu <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(0.999, fit$Student.t[ex,3])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(ql, fit$Student.t[ex,3])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(qu, fit$Student.t[ex,3])
x <- sort(c(x, x.q2))
}
fx <- dt((x - fit$Student.t[ex,1])/fit$Student.t[ex,2], fit$Student.t[ex,3])/fit$Student.t[ex,2]
dist.title=paste("Student-t(",
signif(fit$Student.t[ex,1], sf),
", ",
signif(fit$Student.t[ex,2], sf),
"), df = ",
fit$Student.t[ex, 3],
sep="")
}
if(d == "gamma"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
if(pl == -Inf){pl <- xl + qgamma(0.001, fit$Gamma[ex,1], fit$Gamma[ex,2])}
if(pu == Inf){pu <- xl + qgamma(0.999, fit$Gamma[ex,1], fit$Gamma[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + qgamma(ql, fit$Gamma[ex,1], fit$Gamma[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- xl + qgamma(qu, fit$Gamma[ex,1], fit$Gamma[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dgamma(x - xl, fit$Gamma[ex,1], fit$Gamma[ex,2])
if(fit$Gamma[ex,1] == 1){
dist.title = paste("Gamma(",
signif(fit$Gamma[ex,1], sf),
", ",
signif(fit$Gamma[ex,2], sf),
") (exponential)", sep="")}else{
dist.title = paste("Gamma(",
signif(fit$Gamma[ex,1], sf),
", ",
signif(fit$Gamma[ex,2], sf),
")", sep="")
}
}
if(d == "lognormal"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
if(pl == -Inf){pl <- xl + qlnorm(0.001, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
if(pu == Inf){pu <- xl + qlnorm(0.999, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + qlnorm(ql, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + qlnorm(qu, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
x <- sort(c(x, x.q2))}
fx <- dlnorm(x - xl, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
dist.title = paste("Log normal(",
signif(fit$Log.normal[ex,1], sf),
", ",
signif(fit$Log.normal[ex,2], sf), ")",
sep="")
}
if(d == "logt"){ # log student t
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
# Calculate axes limits using the lognormal; log-t limits may be too extreme
if(pl == -Inf){pl <- xl + qlnorm(0.001, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
if(pu == Inf){pu <- xl + qlnorm(0.999, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + exp(fit$Log.Student.t[ex,1] + fit$Log.Student.t[ex,2] * qt(ql, fit$Log.Student.t[ex,3]))
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + exp(fit$Log.Student.t[ex,1] + fit$Log.Student.t[ex,2] * qt(qu, fit$Log.Student.t[ex,3]))
x <- sort(c(x, x.q2))}
fx <- dt( (log(x - xl) - fit$Log.Student.t[ex,1]) / fit$Log.Student.t[ex,2], fit$Log.Student.t[ex,3]) / ((x - xl) * fit$Log.Student.t[ex,2])
dist.title = paste("Log T(",
signif(fit$Log.Student.t[ex,1], sf),
", ",
signif(fit$Log.Student.t[ex,2], sf),
"), df = ",
fit$Log.Student.t[ex,3], sep="")
}
if(d == "beta"){
xl <- fit$limits[ex,1]
xu <- fit$limits[ex,2]
#if(xl == -Inf){xl <- 0}
#if(xu == Inf){xu <- 1}
# if(pl == -Inf){pl <- xl + (xu - xl) * qbeta(0.001, fit$Beta[ex,1], fit$Beta[ex,2])}
# if(pu == Inf){pu <- xl + (xu - xl) * qbeta(0.999, fit$Beta[ex,1], fit$Beta[ex,2])}
if(pl == -Inf){pl <- xl}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + (xu - xl) * qbeta(ql, fit$Beta[ex,1], fit$Beta[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + (xu - xl) * qbeta(qu, fit$Beta[ex,1], fit$Beta[ex,2])
x <- sort(c(x, x.q2))}
fx <- 1/(xu - xl) * dbeta( (x - xl) / (xu - xl), fit$Beta[ex,1], fit$Beta[ex,2])
dist.title =paste("Beta(",
signif(fit$Beta[ex,1], sf),
", ", signif(fit$Beta[ex,2], sf),
")", sep="")
}
if(d == "hist"){
if(fit$limits[ex, 1] == -Inf){
histl <- qnorm(0.001, fit$Normal[ex,1], fit$Normal[ex,2])
}else{
histl <- fit$limits[ex, 1]
}
if(fit$limits[ex, 2] == Inf){
histu <- qnorm(0.999, fit$Normal[ex,1], fit$Normal[ex,2])
}else{
histu <- fit$limits[ex, 2]
}
if(pl == -Inf){pl <- histl }
if(pu == Inf){pu <- histu }
p <- c(0, fit$probs[ex,], 1)
x2 <- c(histl, fit$vals[ex,], histu)
h <- rep(0, length(x2) -1)
for(i in 1:length(h)){
h[i]<-(p[i+1] - p[i]) / (x2[i+1]-x2[i])
}
x <- rep(x2, each = 2)
fx <- c(0, rep(h, each = 2), 0)
if(is.na(ql) == F){
x.q1 <- qhist(ql, x2, p)
if(!is.element(x.q1, x)){
x <- c(x, x.q1)
fx <-c(fx, dhist(x.q1, x2, p))
temp <- sort(x, index.return = T)
x <- temp$x
fx <- fx[temp$ix]}}
if(is.na(qu) == F){
x.q2 <- qhist(qu, x2, p)
if(!is.element(x.q2, x)){
x <- c(x, x.q2)
fx <-c(fx, dhist(x.q2, x2, p))
temp <- sort(x, index.return = T)
x <- temp$x
fx <- fx[temp$ix]}}
if(min(fx)<0){
fx <- rep(0, length(x))
ql <- NA
qu <- NA
}
dist.title = "histogram fit"
}
if(d == "mirrorgamma"){
xu <- fit$limits[ex, 2]
if(pl == -Inf){pl <- xu - qgamma(0.999, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - qgamma(1 - ql, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- xu - qgamma(1 - qu, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dgamma(xu - x, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
if(fit$mirrorgamma[ex,1] ==1){
dist.title = paste("Mirror gamma(",
signif(fit$mirrorgamma[ex,1], sf),
", ",
signif(fit$mirrorgamma[ex,2], sf),
") (mirror exponential)", sep="")}else{
dist.title = paste("Mirror gamma(",
signif(fit$mirrorgamma[ex,1], sf),
", ",
signif(fit$mirrorgamma[ex,2], sf),
")", sep="")
}
}
if(d == "mirrorlognormal"){
xu <- fit$limits[ex, 2]
if(pl == -Inf){pl <- xu - qlnorm(0.999, fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - qlnorm(1 - ql,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xu - qlnorm(1 - qu,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
x <- sort(c(x, x.q2))}
fx <- dlnorm(xu - x, fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
dist.title = paste("Mirror log normal(",
signif(fit$mirrorlognormal[ex,1], sf),
", ",
signif(fit$mirrorlognormal[ex,2], sf), ")",
sep="")
}
if(d == "mirrorlogt"){ # mirror log student t
xu <- fit$limits[ex, 2]
# Calculate axes limits using the mirror lognormal; log-t limits may be too extreme
if(pl == -Inf){pl <- xu - qlnorm(0.999,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = 0.99*xu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - exp(fit$mirrorlogt[ex,1] +
fit$mirrorlogt[ex,2] * qt(1 - ql, fit$mirrorlogt[ex,3]))
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <-
xu - exp(fit$mirrorlogt[ex,1] +
fit$mirrorlogt[ex,2] * qt(1 - qu, fit$mirrorlogt[ex,3]))
x <- sort(c(x, x.q2))}
fx <- dt( (log(xu - x) - fit$mirrorlogt[ex,1]) /
fit$mirrorlogt[ex,2],
fit$mirrorlogt[ex,3]) / ((xu - x) *
fit$mirrorlogt[ex,2])
dist.title = paste("Mirror log T(",
signif(fit$mirrorlogt[ex,1], sf),
", ",
signif(fit$mirrorlogt[ex,2], sf),
"), df = ",
fit$mirrorlogt[ex,3], sep="")
}
df1 <- data.frame(x = x, fx = fx)
p1 <- ggplot(df1, aes(x = x, y = fx)) +
geom_line(size = lwd) +
labs(title = dist.title, x = xlab, y = ylab )+
theme(plot.title = element_text(hjust = 0.5))
if(is.na(ql) == F ){
p1 <- p1 + geom_ribbon(data = subset(df1, x<=x.q1),
aes(ymax = fx, ymin = 0),
fill = "red",
alpha = 0.5)
}
if(is.na(qu) == F ){
p1 <- p1 + geom_ribbon(data = subset(df1, x>=x.q2),
aes(ymax = fx, ymin = 0),
fill = "red",
alpha = 0.5)
}
if(percentages){
p1 <- p1 + scale_x_continuous(labels = scales::percent,
limits = c(pl, pu))
}else{
p1 <- p1 + xlim(pl, pu)
}
p1
}
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Defines functions makeSingleExpertPlot
makeSingleExpertPlot <-
function(fit, d = "best", pl = -Inf, pu = Inf,
ql = NA, qu = NA, sf = 3, ex = 1,
lwd = 1, xlab, ylab, percentages ){
if(d == "best"){
d <- fit$best.fitting[ex, 1]
}
if(d == "normal"){
if(pl == -Inf){pl <- qnorm(0.001, fit$Normal[ex,1], fit$Normal[ex,2])}
if(pu == Inf){pu <- qnorm(0.999, fit$Normal[ex,1], fit$Normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- qnorm(ql, fit$Normal[ex,1], fit$Normal[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- qnorm(qu, fit$Normal[ex,1], fit$Normal[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dnorm(x, fit$Normal[ex,1], fit$Normal[ex,2])
dist.title <- paste("Normal (mean = ",
signif(fit$Normal[ex,1], sf),
", sd = ",
signif(fit$Normal[ex,2], sf), ")",
sep="")
}
if(d == "t"){
if(pl == -Inf){pl <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(0.001, fit$Student.t[ex,3])}
if(pu == Inf){pu <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(0.999, fit$Student.t[ex,3])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(ql, fit$Student.t[ex,3])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- fit$Student.t[ex,1] + fit$Student.t[ex,2] * qt(qu, fit$Student.t[ex,3])
x <- sort(c(x, x.q2))
}
fx <- dt((x - fit$Student.t[ex,1])/fit$Student.t[ex,2], fit$Student.t[ex,3])/fit$Student.t[ex,2]
dist.title=paste("Student-t(",
signif(fit$Student.t[ex,1], sf),
", ",
signif(fit$Student.t[ex,2], sf),
"), df = ",
fit$Student.t[ex, 3],
sep="")
}
if(d == "gamma"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
if(pl == -Inf){pl <- xl + qgamma(0.001, fit$Gamma[ex,1], fit$Gamma[ex,2])}
if(pu == Inf){pu <- xl + qgamma(0.999, fit$Gamma[ex,1], fit$Gamma[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + qgamma(ql, fit$Gamma[ex,1], fit$Gamma[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- xl + qgamma(qu, fit$Gamma[ex,1], fit$Gamma[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dgamma(x - xl, fit$Gamma[ex,1], fit$Gamma[ex,2])
if(fit$Gamma[ex,1] == 1){
dist.title = paste("Gamma(",
signif(fit$Gamma[ex,1], sf),
", ",
signif(fit$Gamma[ex,2], sf),
") (exponential)", sep="")}else{
dist.title = paste("Gamma(",
signif(fit$Gamma[ex,1], sf),
", ",
signif(fit$Gamma[ex,2], sf),
")", sep="")
}
}
if(d == "lognormal"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
if(pl == -Inf){pl <- xl + qlnorm(0.001, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
if(pu == Inf){pu <- xl + qlnorm(0.999, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + qlnorm(ql, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + qlnorm(qu, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
x <- sort(c(x, x.q2))}
fx <- dlnorm(x - xl, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
dist.title = paste("Log normal(",
signif(fit$Log.normal[ex,1], sf),
", ",
signif(fit$Log.normal[ex,2], sf), ")",
sep="")
}
if(d == "logt"){ # log student t
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
# Calculate axes limits using the lognormal; log-t limits may be too extreme
if(pl == -Inf){pl <- xl + qlnorm(0.001, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
if(pu == Inf){pu <- xl + qlnorm(0.999, fit$Log.normal[ex,1], fit$Log.normal[ex,2])}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + exp(fit$Log.Student.t[ex,1] + fit$Log.Student.t[ex,2] * qt(ql, fit$Log.Student.t[ex,3]))
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + exp(fit$Log.Student.t[ex,1] + fit$Log.Student.t[ex,2] * qt(qu, fit$Log.Student.t[ex,3]))
x <- sort(c(x, x.q2))}
fx <- dt( (log(x - xl) - fit$Log.Student.t[ex,1]) / fit$Log.Student.t[ex,2], fit$Log.Student.t[ex,3]) / ((x - xl) * fit$Log.Student.t[ex,2])
dist.title = paste("Log T(",
signif(fit$Log.Student.t[ex,1], sf),
", ",
signif(fit$Log.Student.t[ex,2], sf),
"), df = ",
fit$Log.Student.t[ex,3], sep="")
}
if(d == "beta"){
xl <- fit$limits[ex,1]
xu <- fit$limits[ex,2]
#if(xl == -Inf){xl <- 0}
#if(xu == Inf){xu <- 1}
# if(pl == -Inf){pl <- xl + (xu - xl) * qbeta(0.001, fit$Beta[ex,1], fit$Beta[ex,2])}
# if(pu == Inf){pu <- xl + (xu - xl) * qbeta(0.999, fit$Beta[ex,1], fit$Beta[ex,2])}
if(pl == -Inf){pl <- xl}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xl + (xu - xl) * qbeta(ql, fit$Beta[ex,1], fit$Beta[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xl + (xu - xl) * qbeta(qu, fit$Beta[ex,1], fit$Beta[ex,2])
x <- sort(c(x, x.q2))}
fx <- 1/(xu - xl) * dbeta( (x - xl) / (xu - xl), fit$Beta[ex,1], fit$Beta[ex,2])
dist.title =paste("Beta(",
signif(fit$Beta[ex,1], sf),
", ", signif(fit$Beta[ex,2], sf),
")", sep="")
}
if(d == "hist"){
if(fit$limits[ex, 1] == -Inf){
histl <- qnorm(0.001, fit$Normal[ex,1], fit$Normal[ex,2])
}else{
histl <- fit$limits[ex, 1]
}
if(fit$limits[ex, 2] == Inf){
histu <- qnorm(0.999, fit$Normal[ex,1], fit$Normal[ex,2])
}else{
histu <- fit$limits[ex, 2]
}
if(pl == -Inf){pl <- histl }
if(pu == Inf){pu <- histu }
p <- c(0, fit$probs[ex,], 1)
x2 <- c(histl, fit$vals[ex,], histu)
h <- rep(0, length(x2) -1)
for(i in 1:length(h)){
h[i]<-(p[i+1] - p[i]) / (x2[i+1]-x2[i])
}
x <- rep(x2, each = 2)
fx <- c(0, rep(h, each = 2), 0)
if(is.na(ql) == F){
x.q1 <- qhist(ql, x2, p)
if(!is.element(x.q1, x)){
x <- c(x, x.q1)
fx <-c(fx, dhist(x.q1, x2, p))
temp <- sort(x, index.return = T)
x <- temp$x
fx <- fx[temp$ix]}}
if(is.na(qu) == F){
x.q2 <- qhist(qu, x2, p)
if(!is.element(x.q2, x)){
x <- c(x, x.q2)
fx <-c(fx, dhist(x.q2, x2, p))
temp <- sort(x, index.return = T)
x <- temp$x
fx <- fx[temp$ix]}}
if(min(fx)<0){
fx <- rep(0, length(x))
ql <- NA
qu <- NA
}
dist.title = "histogram fit"
}
if(d == "mirrorgamma"){
xu <- fit$limits[ex, 2]
if(pl == -Inf){pl <- xu - qgamma(0.999, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - qgamma(1 - ql, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
x <- sort(c(x, x.q1))
}
if(is.na(qu) == F){
x.q2 <- xu - qgamma(1 - qu, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
x <- sort(c(x, x.q2))
}
fx <- dgamma(xu - x, fit$mirrorgamma[ex,1],
fit$mirrorgamma[ex,2])
if(fit$mirrorgamma[ex,1] ==1){
dist.title = paste("Mirror gamma(",
signif(fit$mirrorgamma[ex,1], sf),
", ",
signif(fit$mirrorgamma[ex,2], sf),
") (mirror exponential)", sep="")}else{
dist.title = paste("Mirror gamma(",
signif(fit$mirrorgamma[ex,1], sf),
", ",
signif(fit$mirrorgamma[ex,2], sf),
")", sep="")
}
}
if(d == "mirrorlognormal"){
xu <- fit$limits[ex, 2]
if(pl == -Inf){pl <- xu - qlnorm(0.999, fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = pu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - qlnorm(1 - ql,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <- xu - qlnorm(1 - qu,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
x <- sort(c(x, x.q2))}
fx <- dlnorm(xu - x, fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])
dist.title = paste("Mirror log normal(",
signif(fit$mirrorlognormal[ex,1], sf),
", ",
signif(fit$mirrorlognormal[ex,2], sf), ")",
sep="")
}
if(d == "mirrorlogt"){ # mirror log student t
xu <- fit$limits[ex, 2]
# Calculate axes limits using the mirror lognormal; log-t limits may be too extreme
if(pl == -Inf){pl <- xu - qlnorm(0.999,
fit$mirrorlognormal[ex,1],
fit$mirrorlognormal[ex,2])}
if(pu == Inf){pu <- xu}
x <- seq(from = pl, to = 0.99*xu, length = 200)
if(is.na(ql) == F){
x.q1 <- xu - exp(fit$mirrorlogt[ex,1] +
fit$mirrorlogt[ex,2] * qt(1 - ql, fit$mirrorlogt[ex,3]))
x <- sort(c(x, x.q1))}
if(is.na(qu) == F){
x.q2 <-
xu - exp(fit$mirrorlogt[ex,1] +
fit$mirrorlogt[ex,2] * qt(1 - qu, fit$mirrorlogt[ex,3]))
x <- sort(c(x, x.q2))}
fx <- dt( (log(xu - x) - fit$mirrorlogt[ex,1]) /
fit$mirrorlogt[ex,2],
fit$mirrorlogt[ex,3]) / ((xu - x) *
fit$mirrorlogt[ex,2])
dist.title = paste("Mirror log T(",
signif(fit$mirrorlogt[ex,1], sf),
", ",
signif(fit$mirrorlogt[ex,2], sf),
"), df = ",
fit$mirrorlogt[ex,3], sep="")
}
df1 <- data.frame(x = x, fx = fx)
p1 <- ggplot(df1, aes(x = x, y = fx)) +
geom_line(size = lwd) +
labs(title = dist.title, x = xlab, y = ylab )+
theme(plot.title = element_text(hjust = 0.5))
if(is.na(ql) == F ){
p1 <- p1 + geom_ribbon(data = subset(df1, x<=x.q1),
aes(ymax = fx, ymin = 0),
fill = "red",
alpha = 0.5)
}
if(is.na(qu) == F ){
p1 <- p1 + geom_ribbon(data = subset(df1, x>=x.q2),
aes(ymax = fx, ymin = 0),
fill = "red",
alpha = 0.5)
}
if(percentages){
p1 <- p1 + scale_x_continuous(labels = scales::percent,
limits = c(pl, pu))
}else{
p1 <- p1 + xlim(pl, pu)
}
p1
}
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