# NOT RUN {
####
## Basic usage
####
data(geyser, package="MASS")
x <- geyser$duration
y <- geyser$waiting
# 2-d confidence intervals based on binned kernel density estimate
ci2d(x,y) # filled contour plot
ci2d(x,y, show.points=TRUE) # show original data
# image plot
ci2d(x,y, show="image")
ci2d(x,y, show="image", show.points=TRUE)
# contour plot
ci2d(x,y, show="contour", col="black")
ci2d(x,y, show="contour", col="black", show.points=TRUE)
####
## Control Axis scales
####
x <- rnorm(2000, sd=4)
y <- rnorm(2000, sd=1)
# 2-d confidence intervals based on binned kernel density estimate
ci2d(x,y)
# 2-d confidence intervals based on 2d histogram
ci2d(x,y, method="hist2d", nbins=25)
# Require same scale for each axis, this looks oval
ci2d(x,y, range.x=list(c(-20,20), c(-20,20)))
ci2d(x,y, method="hist2d", same.scale=TRUE, nbins=25) # hist2d
####
## Control smoothing and binning
####
x <- rnorm(2000, sd=4)
y <- rnorm(2000, mean=x, sd=2)
# Default 2-d confidence intervals based on binned kernel density estimate
ci2d(x,y)
# change the smoother bandwidth
ci2d(x,y,
bandwidth=c(sd(x)/8, sd(y)/8)
)
# change the smoother number of bins
ci2d(x,y, nbins=10)
ci2d(x,y)
ci2d(x,y, nbins=100)
# Default 2-d confidence intervals based on 2d histogram
ci2d(x,y, method="hist2d", show.points=TRUE)
# change the number of histogram bins
ci2d(x,y, nbin=10, method="hist2d", show.points=TRUE )
ci2d(x,y, nbin=25, method="hist2d", show.points=TRUE )
####
## Perform plotting manually
####
data(geyser, package="MASS")
# let ci2d handle plotting contours...
ci2d(geyser$duration, geyser$waiting, show="contour", col="black")
# call contour() directly, show the 90 percent CI, and the mean point
est <- ci2d(geyser$duration, geyser$waiting, show="none")
contour(est$x, est$y, est$cumDensity,
xlab="duration", ylab="waiting",
levels=0.90, lwd=4, lty=2)
points(mean(geyser$duration), mean(geyser$waiting),
col="red", pch="X")
####
## Extract confidence region values
###
data(geyser, package="MASS")
## Empirical 90 percent confidence limits
quantile( geyser$duration, c(0.05, 0.95) )
quantile( geyser$waiting, c(0.05, 0.95) )
## Bivariate 90 percent confidence region
est <- ci2d(geyser$duration, geyser$waiting, show="none")
names(est$contours) ## show available contours
ci.90 <- est$contours[names(est$contours)=="0.9"] # get region(s)
ci.90 <- rbind(ci.90[[1]],NA, ci.90[[2]], NA, ci.90[[3]]) # join them
print(ci.90) # show full contour
range(ci.90$x, na.rm=TRUE) # range for duration
range(ci.90$y, na.rm=TRUE) # range for waiting
####
## Visually compare confidence regions
####
data(geyser, package="MASS")
## Bivariate smoothed 90 percent confidence region
est <- ci2d(geyser$duration, geyser$waiting, show="none")
names(est$contours) ## show available contours
ci.90 <- est$contours[names(est$contours)=="0.9"] # get region(s)
ci.90 <- rbind(ci.90[[1]],NA, ci.90[[2]], NA, ci.90[[3]]) # join them
plot( waiting ~ duration, data=geyser,
main="Comparison of 90 percent confidence regions" )
polygon( ci.90, col="green", border="green", density=10)
## Univariate Normal-Theory 90 percent confidence region
mean.x <- mean(geyser$duration)
mean.y <- mean(geyser$waiting)
sd.x <- sd(geyser$duration)
sd.y <- sd(geyser$waiting)
t.value <- qt(c(0.05,0.95), df=gdata::nobs(geyser$duration), lower=TRUE)
ci.x <- mean.x + t.value* sd.x
ci.y <- mean.y + t.value* sd.y
plotCI(mean.x, mean.y,
li=ci.x[1],
ui=ci.x[2],
barcol="blue", col="blue",
err="x",
pch="X",
add=TRUE )
plotCI(mean.x, mean.y,
li=ci.y[1],
ui=ci.y[2],
barcol="blue", col="blue",
err="y",
pch=NA,
add=TRUE )
# rect(ci.x[1], ci.y[1], ci.x[2], ci.y[2], border="blue",
# density=5,
# angle=45,
# col="blue" )
## Empirical univariate 90 percent confidence region
box <- cbind( x=quantile( geyser$duration, c(0.05, 0.95 )),
y=quantile( geyser$waiting, c(0.05, 0.95 )) )
rect(box[1,1], box[1,2], box[2,1], box[2,2], border="red",
density=5,
angle=-45,
col="red" )
## now a nice legend
legend( "topright", legend=c(" Region type",
"Univariate Normal Theory",
"Univarite Empirical",
"Smoothed Bivariate"),
lwd=c(NA,1,1,1),
col=c("black","blue","red","green"),
lty=c(NA,1,1,1)
)
####
## Test with a large number of points
####
# }
# NOT RUN {
x <- rnorm(60000, sd=1)
y <- c( rnorm(40000, mean=x, sd=1),
rnorm(20000, mean=x+4, sd=1) )
hist2d(x,y)
ci <- ci2d(x,y)
ci
# }
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