This vignette is simply a suite of plots that exist primarily as part of our quality control for the package. But since the examples might be useful to others as well, we've added this as a vignette in the package.
The mosaic
package resets the default panel function for histograms. This changes
the default for bin selection and provides some additional arguments to histogram.
histogram(~ rbinom( 500, 20, .3), width=1, fit="normal", v=c(6,10), h=0.1 )
ladd()
provides a relatively easy way to add additional things to a lattice graphic.
xyplot( rnorm(100) ~ rnorm(100) )
ladd( grid.text("Here is some text", x=0, y=0, default.units="native") )
ladd( panel.abline( a=0, b=1, col="red", lwd=3, alpha=.4 ) )
ladd( panel.rect(x=-1, y=-1, width=1, height=1, col="gray80", fill="lightsalmon"))
ladd( panel.rect(x=0, y=0, width=2, height=2, col="gray80", fill="lightskyblue"),
under=TRUE)
In addition to the interactive uses of mplot()
, it can be used in place of plot()
in
several settings.
require(gridExtra)
## Loading required package: gridExtra
## Loading required package: grid
mod <- lm( width ~ length * sex, data=KidsFeet )
mplot(mod, which=1:7, multiplot = TRUE, ncol=2)
mplot(mod, which=1:7, system="ggplot", ncol=2)
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
mplot(mod, which=7)
mplot(mod, which=7, rows=-1)
mplot(mod, which=7, rows=c("sexG", "length", "length:sexG"),
title="Custom titles are supported")
mod <- lm(age ~ substance, data=HELPrct)
mplot(TukeyHSD(mod))
mplot(TukeyHSD(mod), system="ggplot")
mod <- lm(width ~ length* sex, data = KidsFeet)
L <- makeFun(mod)
L( length=15, sex="B")
## 1
## 7.041437
L( length=15, sex="G")
## 1
## 6.654868
xyplot(width ~ length, groups = sex, data = KidsFeet, auto.key=TRUE)
plotFun( L(length, sex="B") ~ length, add=TRUE, col=1 )
## converting numerical color value into a color using lattice settings
plotFun( L(length, sex="G") ~ length, add=TRUE, col=2 )
## converting numerical color value into a color using lattice settings
## converting numerical color value into a color using lattice settings
For logistic regression, makeFun()
handles the conversion back to
probabilities by default.
mod <- glm( smoker=="yes" ~ age + sex, data=NHANES, family=binomial())
SmokerProb <- makeFun(mod)
xyplot( smoker=="yes" ~ age, groups=sex, data=NHANES, alpha=.01 )
plotFun(SmokerProb(age, sex="female") ~ age, col=1, add=TRUE)
## converting numerical color value into a color using lattice settings
plotFun(SmokerProb(age, sex="male") ~ age, add=TRUE, col=2)
## converting numerical color value into a color using lattice settings
## converting numerical color value into a color using lattice settings
f <- makeFun(sin(x) ~ x)
plotFun( f(x) ~ x, xlim = c( -2 * pi, 2 * pi) )
plotFun( x * sin(1/x) ~ x, xlim=c(-1,1) )
plotFun( x * sin(1/x) ~ x, xlim=c(-1,1), npts=10000 )
plotDist("chisq", df=3)
plotDist("chisq", df=3, kind="cdf")
xpnorm(80, mean=100, sd=15)
##
## If X ~ N(100,15), then
##
## P(X <= 80) = P(Z <= -1.333) = 0.0912
## P(X > 80) = P(Z > -1.333) = 0.9088
## [1] 0.09121122
xpnorm(c(80,120), mean=100, sd=15)
##
## If X ~ N(100,15), then
##
## P(X <= 80) = P(Z <= -1.333) = 0.0912
## P(X <= 120) = P(Z <= 1.333) = 0.9088
## P(X > 80) = P(Z > -1.333) = 0.9088
## P(X > 120) = P(Z > 1.333) = 0.0912
## [1] 0.09121122 0.90878878
pdist("chisq", 4, df=3)
## [1] 0.7385359
pdist("f", 3, df1=5, df2=20)
## [1] 0.9647987
qdist("t", c(.025, .975) , df=5)
## [1] -2.570582 2.570582
histogram( ~ rbinom(1000, 20, .4), width=1, v=20 * .4 )
SD <- sqrt(20 * .4 * .6)
plotDist("norm", mean=.4*20, sd=SD, add=TRUE, alpha=.7)
plotDist("norm", col="blue", mean=2, xlim=c(-4,8))
plotDist("norm", mean=5, col="green", kind='histogram', add=TRUE) # add, overtop
plotDist("norm", mean=0, col="red", kind='histogram', under=TRUE) # add, but underneath!
The mosaic
package now provides facilities for producing choropleth maps. The API is still under developement and may change in future releases.
# we need to get state names into the data frame and then fix two of them with
# wrong state abbreviations. Then we are ready to make maps
sAnscombe <- Anscombe %>%
group_by(state = rownames(Anscombe)) %>%
summarise(income = sum(income)) %>%
mutate(state = standardName(state, c(IO = "IA", KA = "KS"), quiet=TRUE))
mUSMap(sAnscombe, key="state", fill="income")
## Mapping API still under development and may change in future releases.
mUSMap(sAnscombe, key="state", fill="income", style="real")
## Mapping API still under development and may change in future releases.
# A sillier example
if (require(mapproj)) {
Countries %>% mutate( nletters = nchar(gapminder) ) %>%
mWorldMap( key="gapminder", fill="nletters") + coord_map()
} else {
Countries %>% mutate( nletters = nchar(gapminder) ) %>%
mWorldMap( key="gapminder", fill="nletters")
}
## Loading required package: mapproj
## Loading required package: maps
## Mapping API still under development and may change in future releases.
## Warning in standardName(x, countryAlternatives, ignore.case = ignore.case,
## : 99 items were not translated