directlabels - lineplot - Positioning Method - lasso.labels

Label points at the zero before the first nonzero y value.

lasso.labels <-
  list(rot=60,
       gapply.fun({ ## figure out where the path hits 0
         d <- d[order(d$x),]
         zero <- d$y[1]
         i <- which(d$y!=zero)[1]
         just <- as.integer(d[i,"y"]>zero)
         transform(d[i-1,],hjust=just,vjust=just)
       }),
       "calc.boxes",
       ## calculate how wide the tilted box is
       dl.trans(hyp=h/sin(2*pi*rot/360)),
       dl.trans(left=x-hyp/2,right=x+hyp/2),
       ## avoid collisions between tilted boxes
       function(d,...){
         solver <- qp.labels("x","left","right")
         ## apply the solver independently for top and bottom labels.
         solution <- data.frame()
         for(vj in c(0,1)){
           these <- d$vjust == vj
           if(any(these)){
             one.side <- d[these,]
             solved <- solver(one.side)
             solution <- rbind(solution,solved)
           }
         }
         solution
       })
bodyweight

bodyweight

data(BodyWeight,package="nlme")
library(lattice)
p <- xyplot(weight~Time|Diet,BodyWeight,groups=Rat,type='l',
       layout=c(3,1),xlim=c(-10,75))
direct.label(p,"lasso.labels")
chemqqmathscore

chemqqmathscore

data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore|gender,Chem97,groups=factor(score),
       type=c('l','g'),f.value=ppoints(100))
direct.label(p,"lasso.labels")
chemqqmathsex

chemqqmathsex

data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore,Chem97,groups=gender,
       type=c("l","g"),f.value=ppoints(100))
direct.label(p,"lasso.labels")
lars

lars

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))+
  ggtitle("LASSO path for prostate cancer data calculated using the LARS")+
  xlim(0,20)
direct.label(p,"lasso.labels")
projectionSeconds

projectionSeconds

data(projectionSeconds, package="directlabels")
p <- ggplot(projectionSeconds, aes(vector.length/1e6))+
  geom_ribbon(aes(ymin=min, ymax=max,
                  fill=method, group=method), alpha=1/2)+
  geom_line(aes(y=mean, group=method, colour=method))+
  ggtitle("Projection Time against Vector Length (Sparsity = 10%)")+
  guides(fill="none")+
  ylab("Runtime (s)")
direct.label(p,"lasso.labels")
ridge

ridge

## complicated ridge regression lineplot ex. fig 3.8 from Elements of
## Statistical Learning, Hastie et al.
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
  dsq <- Xs$d^2
  ## make the x axis degrees of freedom
  df <- sapply(lambda,function(l)sum(dsq/(dsq+l)))
  D <- data.frame(t(fit$coef),lambda,df) # scaled coefs
  molt <- melt(D,id=c("lambda","df"))
  ## add in the points for df=0
  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)))
direct.label(p,"lasso.labels")
sexdeaths

sexdeaths

library(ggplot2)
tx <- time(mdeaths)
Time <- ISOdate(floor(tx),round(tx%%1 * 12)+1,1,0,0,0)
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))
direct.label(p,"lasso.labels")
Please contact Toby Dylan Hocking if you are using directlabels or have ideas to contribute, thanks!
Documentation website generated from source code version 2021.2.24 (git revision bb6db07 Mon, 14 Jun 2021 22:38:45 +0530) using inlinedocs.
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