#
# Iso-sensitivity = constant d' (the ROC). The optimal operating point on
# that ROC is where slope = beta, i.e. k = d'/2 + log(beta)/d'.
#
# Usage:
#   Rscript tools/plot_roc_beta.R
d_prime <- 2
betas <- c(10, 0.1)
roc_curve <- function(d, n = 400) {
far <- ppoints(n)
data.frame(
d = d,
far = far,
hr = pnorm(qnorm(far) + d)
)
}
operating_point <- function(d, beta) {
k <- d / 2 + log(beta) / d
data.frame(
d = d,
beta = beta,
k = k,
far = 1 - pnorm(k),
hr = 1 - pnorm(k - d)
)
}
iso_bias <- function(beta, d_seq = seq(0.35, 4, length.out = 200)) {
k <- d_seq / 2 + log(beta) / d_seq
data.frame(
beta = beta,
d = d_seq,
far = 1 - pnorm(k),
hr = 1 - pnorm(k - d_seq)
)
}
# Tangent (iso-expected-value) line through the operating point, slope = beta
tangent_seg <- function(pt, beta, far_max = 1) {
# hr = pt$hr + beta * (far - pt$far)
far0 <- max(0, pt$far - pt$hr / beta)
far1 <- min(far_max, pt$far + (1 - pt$hr) / beta)
data.frame(
beta = beta,
far = c(far0, far1),
hr = pt$hr + beta * (c(far0, far1) - pt$far)
)
}
rocs <- do.call(rbind, lapply(c(1, d_prime, 3), function(d) {
out <- roc_curve(d)
out$label <- sprintf("d' = %g", d)
out
}))
pts <- do.call(rbind, lapply(betas, function(b) operating_point(d_prime, b)))
pts$label <- sprintf("beta = %s", format(pts$beta, scientific = FALSE, trim = TRUE))
bias <- do.call(rbind, lapply(betas, iso_bias))
bias$label <- sprintf("iso-bias beta = %s", format(bias$beta, scientific = FALSE, trim = TRUE))
tans <- do.call(rbind, lapply(seq_len(nrow(pts)), function(i) {
tangent_seg(pts[i, ], pts$beta[i])
}))
paper <- "#f0ebe1"
teal <- "#2a6f78"
rust <- "#c44a24"
olive <- "#6a7342"
ink <- "#2c2a26"
library(ggplot2)
p <- ggplot() +
geom_abline(slope = 1, intercept = 0, colour = ink, linetype = "dotted", linewidth = 0.4) +
geom_line(
data = rocs,
aes(far, hr, group = label, colour = "iso-sensitivity (ROC)", linetype = label),
linewidth = 0.7
) +
geom_line(
data = bias,
aes(far, hr, group = label, colour = "iso-bias (constant beta)"),
linewidth = 0.5,
linetype = "longdash"
) +
geom_line(
data = tans,
aes(far, hr, group = beta, colour = "optimal slope = beta"),
linewidth = 0.45
) +
geom_point(
data = pts,
aes(far, hr, colour = "optimal criterion on d' = 2"),
size = 2.6
) +
geom_text(
data = pts,
aes(far, hr, label = label),
colour = ink,
size = 3.1,
hjust = ifelse(pts$beta > 1, -0.12, 1.12),
vjust = ifelse(pts$beta > 1, 0.3, -0.4)
) +
coord_fixed(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
scale_x_continuous("False alarm rate P(Yes | N)", breaks = seq(0, 1, 0.25)) +
scale_y_continuous("Hit rate P(Yes | S)", breaks = seq(0, 1, 0.25)) +
scale_colour_manual(
values = c(
"iso-sensitivity (ROC)" = teal,
"iso-bias (constant beta)" = olive,
"optimal slope = beta" = rust,
"optimal criterion on d' = 2" = rust
)
) +
scale_linetype_manual(values = c("d' = 1" = "dotted", "d' = 2" = "solid", "d' = 3" = "dotdash")) +
labs(
title = "ROC iso-sensitivity and optimal criterion for a 10:1 base rate",
subtitle = sprintf(
"Equal-variance Gaussian. On d' = %g, k* = d'/2 + ln(beta)/d'. beta = 10 (noise 10:1) or 0.1 (signal 10:1).",
d_prime
),
colour = NULL,
linetype = NULL
) +
theme_minimal(base_size = 11) +
theme(
plot.background = element_rect(fill = paper, colour = NA),
panel.background = element_rect(fill = paper, colour = NA),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "#d9d3c6", linewidth = 0.3),
legend.position = "bottom",
legend.box = "vertical",
plot.title = element_text(face = "bold", colour = ink),
plot.subtitle = element_text(colour = ink, size = 9)
) +
guides(
colour = guide_legend(order = 1, override.aes = list(linetype = c("solid", "longdash", "solid", "blank"), shape = c(NA, NA, NA, 16))),
linetype = guide_legend(order = 2)
)
print(pts[, c("beta", "k", "far", "hr")])
print(p)
source("~/Dropbox/Articles & Presentations/2026/human_ai_handbook/tools/plot_roc_beta.R")
print(p)
roc_curve <- function(d, n = 500) {
far <- ppoints(n)
data.frame(d = d, far = far, hr = pnorm(qnorm(far) + d))
}
print(p)
source("~/Dropbox/Articles & Presentations/2026/human_ai_handbook/tools/plot_roc_beta.R")
source("~/Dropbox/Articles & Presentations/2026/human_ai_handbook/tools/plot_roc_beta.R")
print(p)
x <
3
x <- rnorm(50) * 10
plot(x)
x <- x - mean(x) +33
mean(x)
plot(x)
abline(mean(x),0)
text(25,mean(x),paste("The mean is",mean(x)))
x <- print("the mean is", 33)
x
ChickWeight
ChickWeight(order(ChickWeight$Time))
data(ChickWeight)
ChickWeight(order(ChickWeight$Time))
ChickWeight[order(ChickWeight$Time)]
ChickWeight[order(ChickWeight$time)]
ChickWeight[order(ChickWeight$time),]
ChickWeight$Time
order(ChickWeight$Time)
ChickWeight[order(ChickWeight$Time),]
ChickWeight[order(ChickWeight$Time)]
setwd("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter5")
colors()
colors()[3]
colors()[1:20]
palette()
barplot(rep(1,8))
barplot(rep(1,8),colors=1:8)
barplot(rep(1,8),col=1:8)
pallete()
palette9)
palette()
barplot(rep(1,8),col=3)
barplot(rep(1,8),col="#61D04F")
barplot(rep(1,8),col="#61D040")
barplot(rep(1,8),col="#61D010")
barplot(rep(1,8),col="#33D010")
barplot(rep(1,8),col=1:5)
barplot(rep(1,16),col=1:16)
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
plot(x,y,type="b",pch=16)
colors()[1:10]
barplot(rep(1,10,col=colors()[1:10])
)
barplot(rep(1,10),col=colors()[1:10])
par(mfrow=c(1,3))
plot(x,y,type="b",pch=16, col = cm.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="black")
cm.colors()
cm.colors(10)
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
plot(x,y,type="b",pch=16)
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
plot(x,y,type="b",pch=16)
par(mfrow=c(1,3))
plot(x,y,type="b",pch=16, col = cm.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="black")
plot(x,y,type="b",pch=16, col = terrain.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="white")
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
grey()
grey(1)
grey(0)
grey(.5)
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
dev.off()
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
dev.off()
dev.off()
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
plot(x,y,type="b",pch=16)
##there is a start and an end--let's adjust the color:
#pdf("c6col4.pdf",width=9,height=4)
par(mfrow=c(1,3))
plot(x,y,type="b",pch=16, col = cm.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="black")
plot(x,y,type="b",pch=16, col = terrain.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="white")
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
#text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
dev.off()
dev.off()
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
plot(x,y,type="b",pch=16)
##there is a start and an end--let's adjust the color:
#pdf("c6col4.pdf",width=9,height=4)
par(mfrow=c(1,3))
plot(x,y,type="b",pch=16, col = cm.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="black")
plot(x,y,type="b",pch=16, col = terrain.colors(16),cex=3)
text(x,y,1:16,cex=.8,col="white")
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="white") ##notice reverse colors
#text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="black") ##notice reverse colors
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
dev.off()
dev.off()
dev.off()
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="grey") ##notice reverse colors
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
plot(x,y,type="b",pch=16)
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="grey") ##notice reverse colors
#text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="orange") ##notice reverse colors
source("~/Dropbox/courses/5210-2026/web-5210/psy5210/Projects/Chapter6/Chapter6Walkthrough.R", echo = TRUE)
dev.off()
x <- c(3,2.5,0,12,15, 18, 22,33,26,23,18,17,8,9,5,11)
y <- c(14, 8,2,15, 3, 1, 6, 9,  12,11,12,7,8,6,1,5)
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="red") ##notice reverse colors
#text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
dev.off()
dev.off()
##Greyscale
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="red") ##notice reverse colors
#text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
#dev.off()
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
text(x,y,1:16,cex=.8,col="red") ##notice reverse colors
text(x,y,1:16,cex=.8,col= rep(c("white","black"),each=8)) ##notice reverse colors
rep(c("white","black"),each=8)
rep(c("white","black"),8)
rep(c("white","black"),each=8)
plot(x,y,type="l",cex=2)
points(x,y,type="p",cex=3, col = grey(1:16/16),pch=16)
points(x,y,type="p",cex=3,col="black")
library(gplots)
#pdf("c6col5.pdf",width=9,height=4)
plot(1:50,rep(10,50),pch=15,cex=2,xlim=c(-20,50),ylim=c(0,12),col=redgreen(50),
xaxt="n",yaxt="n",xlab="",ylab="",bty="n")
text(-12,10,"redgreen:")
points(1:50,rep(9,50),pch=15,cex=2,col=greenred(50))
text(-12,9,"greenred:")
points(1:50,rep(8,50),pch=15,cex=2,col=bluered(50))
text(-12,8,"bluered:")
points(1:50,rep(7,50),pch=15,cex=2,col=redblue(50))
text(-12,7,"redblue:")
points(1:50,rep(6,50),pch=15,cex=2,col=colorpanel(50,"orange","red"))
text(-12,6,"colorpanel: orange red")
points(1:50,rep(5,50),pch=15,cex=2,col=colorpanel(50,"black","green"))
text(-12,5,"colorpanel: black green")
points(1:50,rep(4,50),pch=15,cex=2,col=colorpanel(50,"blue","green"))
text(-12,4,"colorpanel: blue green")
points(1:50,rep(3,50),pch=15,cex=2,col=colorpanel(50,"pink","red"))
text(-12,3,"colorpanel: pink red")
points(1:50,rep(2,50),pch=15,cex=2,col=colorpanel(50,"yellow","orange","brown"))
text(-12,2,"colorpanel: yel-or-brn")
points(1:50,rep(1,50),pch=15,cex=2,col=rich.colors(50))
text(-12,1,"richcolors:")
points(1:50,rep(0,50),pch=15,cex=2,col=rich.colors(50,palette="blues"))
text(-12,0,"richcolors: blues")
#install.packages("RColorBrewer")
library(RColorBrewer)
display.brewer.all()
display.brewer.pal(10,"Blues")
display.brewer.pal(6,"Greens")
display.brewer.pal(12,"BrBG")
display.brewer.pal(7,"Accent")
display.brewer.pal(10,"Blues")
display.brewer.pal(6,"Greens")
display.brewer.pal(12,"BrBG")
display.brewer.pal(7,"Accent")
palette <- brewer.pal(11,"Spectral")
palette(palette)
barplot(runif(10),col=1:10,main="Spectral colors")
palettex <- brewer.pal(11,"Spectral")
palette(palettex)
palette()
barplot(runif(10),col=1:10,main="Spectral colors")
barplot(runif(22),col=brewer.pal(11,"BrBG"),main="Brown-Green palette")
x <- rnorm(2500)*3
y <- x + sqrt(rnorm(2500)^2) *8
tab <- table(round(x),round(y))
par(mfrow=c(2,3))
image(tab)
round(x)
round(y)
tab <- table(round(x),round(y))
tab
table(round(x))
table(round(x),round(y))
image(tab)
image(tab,col=brewer.pal(11,"Oranges"))
image(tab,col=brewer.pal(11,"Blues"),legend=T)
x <-   rnorm(2500)*3
y <- x + sqrt(rnorm(2500)^2) *8
tab <- table(round(x),round(y))
par(mfrow=c(2,3))
image(tab)
image(tab,col=brewer.pal(11,"Oranges"))
image(tab,col=brewer.pal(11,"Blues"),legend=T)
image(tab,col=blue2green(50))
image(tab,col=blue2yellow(50))
barplot(runif(20),col=primary.colors(20))
library("colorRamps")
#pdf("c6col9.pdf",width=9,height=5)
par(mfrow=c(1,3))
image(tab,col=blue2green(50))
image(tab,col=blue2yellow(50))
barplot(runif(20),col=primary.colors(20))
colorblind <- c(rgb(0,0,0,maxColorValue=255),
rgb(230,159,0,maxColorValue=255),
rgb(86,180,233,maxColorValue=255),
rgb(0,158,115,maxColorValue=255),
rgb(240,228,66,maxColorValue=255),
rgb(0,114,178,maxColorValue=255),
rgb(213,94,0,maxColorValue=255),
rgb(204,121,167,maxColorValue=255))
par(mfrow=c(1,2))
colorblind <- c(rgb(0,0,0,maxColorValue=255),
rgb(230,159,0,maxColorValue=255),
rgb(86,180,233,maxColorValue=255),
rgb(0,158,115,maxColorValue=255),
rgb(240,228,66,maxColorValue=255),
rgb(0,114,178,maxColorValue=255),
rgb(213,94,0,maxColorValue=255),
rgb(204,121,167,maxColorValue=255))
palette(colorblind)
barplot(1:10,col=1:10)
barplot(1:10,col=1:10)
barplot(1:10,density=10,angle=runif(10)*180,add=T)
barplot(1:10,density=10,angle=runif(10)*180,add=T)
set.seed(101)
dat <- t(matrix(runif(20),5,4) * (1:5)^2)
cats =c("Fr","So","Ju","Sr","Gr")
#pdf("c6-colorscheme1.pdf",width=9,height=4)
par(mfrow=c(1,4))
palette(c("midnightblue","gold","darkgreen","maroon","dodgerblue"))
matplot(dat,type="l",lwd=3,lty=1,xaxt="n",ylim=c(1,25))
legend(2,25,rev(cats),lty=1,lwd=3,col=5:1)
pie(c(1,13,5,2,1),labels=c("Fr","So","Ju","Sr","Gr"),col=1:5)
dotchart(c(1,13,5,2,1),labels=c("Fr","So","Ju","Sr","Gr"),col=1:5,pch=16,pt.cex=3)
barplot(c(10,5,8,4,6),names=cats,col=1:5)
#dev.off()
##Whoops, somebody didn't like the color scheme.
palette( brewer.pal(9,"Set1"))
matplot(dat,type="l",lwd=3,lty=1,xaxt="n",ylim=c(1,25))
legend(2,25,rev(cats),lty=1,lwd=3,col=5:1)
pie(c(1,13,5,2,1),labels=c("Fr","So","Ju","Sr","Gr"),col=1:5)
dotchart(c(1,13,5,2,1),labels=c("Fr","So","Ju","Sr","Gr"),col=1:5,pch=16,pt.cex=3)
barplot(c(10,5,8,4,6),names=cats,col=1:5)
#dev.off()
par(mfrow=c(1,1),mar=c(0,0,0,0))
cols <- c(rgb(240,0,5,maxColorValue=255,alpha=120),
rgb(230,225,5,maxColorValue=255,alpha=255),
rgb(12,100,250,maxColorValue=255,alpha=120))
##In a random sample, of women, we might have 40 self-identified feminists,
##and 10 employed in the financial industry
plot(0,type="n",xlim=c(0,10),ylim=c(0,10),bty="n",xaxt="n",yaxt="n",xlab="",ylab="")
segments(0:10,0,0:10,10,lty=3)
segments(0,0:10,10,0:10,lty=3)
rect(0,0,10,10,col=cols[3])
rect(0,6,10,7,col=cols[2])
rect(1,1,6,9,col=cols[1])
text(3.5,5,"Feminists (40)")
text(3.5,6.5,"Lindas (5)")
text(8,6.5,"Bank\nworkers (10)")
text(7,.5,"Women (100)")
catnames <- c("izzy", "dilly", "spooky", "bernard", "jack")
catcolors <- c("black", "orange", "gray", "brown")
datavals <- round(exp(runif(20)*5))
##random 600 numbers with a mean of 76 and a standard deviation of 6.9
data <- data.frame(Cat=rep(catnames,4),
Color=rep(catcolors, each=5),
Count=datavals )
#pdf("c6balloon1.pdf",width=6,height=6)
balloonplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name")
#dev.off()
data
balloonplot(data$Cat,data$color,data$Count)
#pdf("c6balloon1.pdf",width=6,height=6)
balloonplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name")
balloonplot(data$Cat,data$color,data$Count)
dev.off()
dev.off()
balloonplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name")
?balloonplot
balloonplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name",cum.margins=F)
bubbleplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name",cum.margins=F)
bubbleplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name")
bubbleplot( data$Cat, data$Color, data$Count, colmar=2,ylab="Color",xlab="Cat name")
catch.t <- xtabs(Catch~Year+Age, catch.d)              # example table
catch.m <- as.matrix(as.data.frame(unclass(catch.t)))  # example matrix
catch.t
catch.m
bubbleplot(Catch~Age+Year, data=catch.d)
##Joyplot from plotrix:
x1<-c(sample(20:50,20),sample(40:80,30))
x2<-c(sample(10:40,30),sample(50:90,30))
x3<-sample(20:90,50)
xdens1<-density(x1)
xdens2<-density(x2)
xdens3<-density(x3)
xlist<-list(first=xdens1,second=xdens2,third=xdens3,fourth=xdens2,fifth=xdens3,sixth=xdens1)
joyPlot(xlist,fill=rgb(.2,.2,.2,alpha=.80),newrange=c(0,2.5),border="grey70")
##Anne's example
par(mfrow=c(1,6),mar=c(1,5,1,0))
barplot(runif(20),horiz=T,xaxt="n",col="blue",names=LETTERS[1:20],las=1)
par(mar=c(1,0,1,0))
barplot(runif(20),horiz=T,xaxt="n",col="salmon")
barplot(runif(20),horiz=T,xaxt="n",col="limegreen")
barplot(runif(20),horiz=T,xaxt="n",col="orange")
barplot(runif(20),horiz=T,xaxt="n",col="purple")
barplot(runif(20),horiz=T,xaxt="n",col="orange4")