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.
This way of doing this is largely superceded by our ggformula
package which provides a formula interface to ggplot2
. You might also like to see the vignette that compares using lattice
to using ggformula
.
The mosaic
package resets the default panel function for histograms. This changes the default for bin selection and provides some additional arguments to histogram.
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) mod <- lm(width ~ length * sex, data = KidsFeet) mplot(mod, which = 1:7, multiplot = TRUE, ncol = 2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
mplot(mod, which=1:7, system="ggplot", ncol=2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
mplot(mod, which=7)
mplot(mod, which=7, rows=-1)
## 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( SmokeNow =="Yes" ~ Age + Race3, data=NHANES, family=binomial()) SmokerProb <- makeFun(mod) xyplot( SmokeNow=="Yes" ~ Age, groups=Race3, data=NHANES, alpha=.01, xlim=c(20,90) ) plotFun(SmokerProb(Age, Race3="Black") ~ Age, col="black", add=TRUE) plotFun(SmokerProb(Age, Race3="White") ~ Age, col="red", add=TRUE) ladd(grid.text("Black", x=25, y=SmokerProb(25, Race="Black"),hjust = 0, vjust=-0.2, gp=gpar(col="black"), default.units="native")) ladd(grid.text("White", x=25, y=SmokerProb(25, Race="White"),hjust = 0, vjust=-0.2, gp=gpar(col="red"), default.units="native"))
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) = 0.09121
## P(X > 80) = P(Z > -1.333) = 0.9088
##
## [1] 0.09121122
##
## If X ~ N(100, 15), then
## P(X <= 80) = P(Z <= -1.333) = 0.09121 P(X <= 120) = P(Z <= 1.333) = 0.90879
## P(X > 80) = P(Z > -1.333) = 0.90879 P(X > 120) = P(Z > 1.333) = 0.09121
##
## [1] 0.09121122 0.90878878
pdist("chisq", 4, df=3)
## [1] 0.7385359
pdist("f", 3, df1=5, df2=20)
## [1] 0.9647987
## [1] -2.570582 2.570582
The mosaic
package now provides facilities for producing choropleth maps. The API is still under developement and may change in future releases.
## Mapping API still under development and may change in future releases.
Looks like it is safer to live in the North:
## Mapping API still under development and may change in future releases.
Here is a sillier example
## 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