# Lattice graphics
#-----------------

library(faraway)
library(lattice)
str(nels88)
summary(nels88)

xtabs(~paredu, data=nels88)

# Graphing counts and percents

histogram(~ paredu, data=nels88)                 # basic
histogram(~ paredu, data=nels88, type="count")   #rescaled


histogram(~ math, data=nels88, type="count")   # auto-binned
nt <- length(xtabs(~math, data=nels88))
histogram(~ math, data=nels88, type="count", nint=nt)
barchart(~ table(math), data=nels88) # chart the table, not the variable

densityplot(~ math, data=nels88)

with(nels88, hist(math))    # the graphics-package version

# Crosstabs

histogram(~math | race, data=nels88)
densityplot(~math | race, data=nels88)

histogram(~paredu | race, data=nels88)

# T-test

bwplot(math~sex, data=nels88)

# Extending the comparison, ala lm()
bwplot(math~sex | race, data=nels88)

bwplot(math~sex | race*paredu, data=nels88)
dotplot(math~sex | race*paredu, data=nels88)

# F-test
bwplot(math~race, data=nels88)

bwplot(math~race | paredu, data=nels88)

# Correlation
xyplot(math ~ ses, data=nels88)

xyplot(math ~ ses, data=nels88,
    panel=function(x, y){
	  panel.xyplot(x, y)
	  panel.lmline(x, y)
})

xyplot(math ~ ses | race, data=nels88,
    panel=function(x, y){
	  panel.xyplot(x, y)
	  panel.lmline(x, y)
})

mathplot <- xyplot(math ~ ses | paredu, data=nels88,
    panel=function(x, y){
	  panel.xyplot(x, y)
	  panel.lmline(x, y)
})

plot(mathplot)

str(mathplot)

mathplot <- update(mathplot, main="Math Score v SES by Paredu")
print(mathplot)

xyplot(math ~ ses | paredu, groups=race, data=nels88, auto.key=TRUE)

#

d <- matrix(rnorm(15000, mean=7.5, sd=5), nrow=15)

samplemeans <- colMeans(d)

dp <- densityplot(as.vector(d))
plot(dp)
str(dp)

sp <- densityplot(samplemeans, plot.points=FALSE)
plot(sp)
sp1<- update(sp, xlim=dp$x.limits)
plot(sp1)