positioning.functions(directlabels)
positioning.functions()所属R语言包：directlabels
Built-in Positioning Methods for direct label placement
内置定位方法直接标记的位置
译者：生物统计家园网 机器人LoveR
描述----------Description----------
When adding direct labels to a grouped plot, label placement can be specified using a Positioning Method (or a list of them), of the form function(d,...), where d is a data frame of the points to plot, with columns x y groups. The job of the Positioning Method(s) is to return the position of each direct label you want to plot as a data frame, with 1 row for each label. Thus normally a Positioning Method will return 1 row for each group. Several built-in Positioning Methods are discussed below, but you can also create your own, either from scratch or by using dl.indep and
标签的位置时直接加入标签分组的图的，可以指定使用的功能的定位方法（或它们的列表），（四），其中d是一个数据框的点来绘制，与列XY团体。工作的定位方法（）返回的每一个直接的标签，你要绘制一个数据框，有1行的每一个标签的位置。因此，一般的定位方法将返回各组的1行。以下几个内置的定位方法进行了讨论，但你也可以创建你自己，从头开始或使用dl.indep
（作者）----------Author(s)----------
Toby Dylan Hocking <toby.hocking@inria.fr>
实例----------Examples----------
## Not run: [＃不运行：]
### contourplot Positioning Methods[：＃＃contourplot定位方法]
for(p in list({
## Example from help(contourplot)[＃示例从帮助（contourplot）的]
require(stats)
require(lattice)
attach(environmental)
ozo.m <- loess((ozone^(1/3)) ~ wind * temperature * radiation,
parametric = c("radiation", "wind"), span = 1, degree = 2)
w.marginal <- seq(min(wind), max(wind), length.out = 50)
t.marginal <- seq(min(temperature), max(temperature), length.out = 50)
r.marginal <- seq(min(radiation), max(radiation), length.out = 4)
wtr.marginal <- list(wind = w.marginal, temperature = t.marginal,
radiation = r.marginal)
grid <- expand.grid(wtr.marginal)
grid[, "fit"] <- c(predict(ozo.m, grid))
detach(environmental)
library(ggplot2)
p <- ggplot(grid,aes(wind,temperature,z=fit))+
stat_contour(aes(colour=..level..))+
facet_wrap(~radiation)
},
{
## example from help(stat_contour)[＃例如，从帮助（stat_contour）]
library(reshape2)
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
library(ggplot2)
p <- ggplot(volcano3d, aes(x, y, z = z))+
stat_contour(aes(colour = ..level..))
})){
print(direct.label(p,"bottom.pieces"))
print(direct.label(p,"top.pieces"))
}
### densityplot Positioning Methods[：＃＃densityplot定位方法]
for(p in list({
data(Chem97,package="mlmRev")
library(lattice)
p <- densityplot(~gcsescore|gender,Chem97,
groups=factor(score),layout=c(1,2),
n=500,plot.points=FALSE)
},
{
library(reshape2)
iris2 <- melt(iris,id="Species")
library(lattice)
p <- densityplot(~value|variable,iris2,groups=Species,scales="free")
},
{
loci <- data.frame(ppp=c(rbeta(800,10,10),rbeta(100,0.15,1),rbeta(100,1,0.15)),
type=factor(c(rep("NEU",800),rep("POS",100),rep("BAL",100))))
library(ggplot2)
p <- qplot(ppp,data=loci,colour=type,geom="density")
})){
print(direct.label(p,"top.bumptwice"))
print(direct.label(p,"top.bumpup"))
print(direct.label(p,"top.points"))
}
### dotplot Positioning Methods[：＃＃dotplot定位方法]
for(p in list({
library(lattice)
p <- dotplot(VADeaths,xlim=c(8,85),type="o")
},
{
vad <- as.data.frame.table(VADeaths)
names(vad) <- c("age","demographic","deaths")
library(ggplot2)
p <- qplot(deaths,age,data=vad,group=demographic,geom="line",colour=demographic)+
xlim(8,80)
})){
print(direct.label(p,"angled.endpoints"))
print(direct.label(p,"top.qp"))
}
### lineplot Positioning Methods[：＃＃lineplot定位方法]
for(p in list({
data(BodyWeight,package="nlme")
library(lattice)
p <- xyplot(weight~Time|Diet,BodyWeight,groups=Rat,type='l',
layout=c(3,1),xlim=c(-10,75))
},
{
data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore|gender,Chem97,groups=factor(score),
type=c('l','g'),f.value=ppoints(100))
},
{
data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore,Chem97,groups=gender,
type=c("l","g"),f.value=ppoints(100))
},
{
data(prostate,package="ElemStatLearn")
pros <- subset(prostate,select=-train,train==TRUE)
ycol <- which(names(pros)=="lpsa")
x <- as.matrix(pros[-ycol])
y <- pros[[ycol]]
library(lars)
fit <- lars(x,y,type="lasso")
beta <- scale(coef(fit),FALSE,1/fit$normx)
arclength <- rowSums(abs(beta))
library(reshape2)
path <- data.frame(melt(beta),arclength)
names(path)[1:3] <- c("step","variable","standardized.coef")
library(ggplot2)
p <- ggplot(path,aes(arclength,standardized.coef,colour=variable))+
geom_line(aes(group=variable))+
opts(title="LASSO path for prostate cancer data calculated using the LARS")+
xlim(0,20)
},
{
## complicated ridge regression lineplot ex. fig 3.8 from Elements of[＃复杂的的岭回归lineplot前。图3.8从要素]
## Statistical Learning, Hastie et al.[＃统计学习，哈斯蒂等。人。]
myridge <- function(f,data,lambda=c(exp(-seq(-15,15,l=200)),0)){
require(MASS)
require(reshape2)
fit <- lm.ridge(f,data,lambda=lambda)
X <- data[-which(names(data)==as.character(f[[2]]))]
Xs <- svd(scale(X)) ## my d's should come from the scaled matrix[＃D的应该是从规模矩阵]
dsq <- Xs$d^2
## make the x axis degrees of freedom[＃使x轴的自由度]
df <- sapply(lambda,function(l)sum(dsq/(dsq+l)))
D <- data.frame(t(fit$coef),lambda,df) # scaled coefs[规模COEFS]
molt <- melt(D,id=c("lambda","df"))
## add in the points for df=0[＃添加DF = 0的点]
limpts <- transform(subset(molt,lambda==0),lambda=Inf,df=0,value=0)
rbind(limpts,molt)
}
data(prostate,package="ElemStatLearn")
pros <- subset(prostate,train==TRUE,select=-train)
m <- myridge(lpsa~.,pros)
library(lattice)
p <- xyplot(value~df,m,groups=variable,type="o",pch="+",
panel=function(...){
panel.xyplot(...)
panel.abline(h=0)
panel.abline(v=5,col="grey")
},
xlim=c(-1,9),
main="Ridge regression shrinks least squares coefficients",
ylab="scaled coefficients",
sub="grey line shows coefficients chosen by cross-validation",
xlab=expression(df(lambda)))
},
{
library(ggplot2)
tx <- time(mdeaths)
Time <- ISOdate(floor(tx),round(tx
uk.lung <- rbind(data.frame(Time,sex="male",deaths=as.integer(mdeaths)),
data.frame(Time,sex="female",deaths=as.integer(fdeaths)))
p <- qplot(Time,deaths,data=uk.lung,colour=sex,geom="line")+
xlim(ISOdate(1973,9,1),ISOdate(1980,4,1))
})){
print(direct.label(p,"first.bumpup"))
print(direct.label(p,"first.points"))
print(direct.label(p,"first.qp"))
print(direct.label(p,"lasso.labels"))
print(direct.label(p,"last.bumpup"))
print(direct.label(p,"last.points"))
print(direct.label(p,"last.qp"))
print(direct.label(p,"lines2"))
print(direct.label(p,"maxvar.points"))
print(direct.label(p,"maxvar.qp"))
}
### scatterplot Positioning Methods[：＃＃散点图定位方法]
for(p in list({
data(mpg,package="ggplot2")
m <- lm(cty~displ,data=mpg)
mpgf <- fortify(m,mpg)
library(lattice)
library(latticeExtra)
p <- xyplot(cty~hwy|manufacturer,mpgf,groups=class,aspect="iso",
main="City and highway fuel efficiency by car class and manufacturer")+
layer_(panel.abline(0,1,col="grey90"))
},
{
data(mpg,package="ggplot2")
m <- lm(cty~displ,data=mpg)
mpgf <- fortify(m,mpg)
library(lattice)
p <- xyplot(jitter(.resid)~jitter(.fitted),mpgf,groups=factor(cyl))
},
{
library(lattice)
p <- xyplot(jitter(Sepal.Length)~jitter(Petal.Length),iris,groups=Species)
},
{
data(mpg,package="ggplot2")
library(lattice)
p <- xyplot(jitter(cty)~jitter(hwy),mpg,groups=class,
main="Fuel efficiency depends on car size")
},
{
library(ggplot2)
data(mpg,package="ggplot2")
p <- qplot(jitter(hwy),jitter(cty),data=mpg,colour=class,
main="Fuel efficiency depends on car size")
},
{
data(normal.l2.cluster,package="directlabels")
library(ggplot2)
p <- ggplot(normal.l2.cluster$path,aes(x,y))+
geom_path(aes(group=row),colour="grey")+
geom_point(aes(size=lambda),colour="grey")+
geom_point(aes(colour=class),data=normal.l2.cluster$pts,pch=21,fill="white")+
coord_equal()
})){
print(direct.label(p,"ahull.grid"))
print(direct.label(p,"chull.grid"))
print(direct.label(p,"extreme.grid"))
print(direct.label(p,"perpendicular.grid"))
print(direct.label(p,"smart.grid"))
}
## End(Not run)[＃（不执行）]
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-16 20:32:48
