Description Usage Arguments Author(s) Examples
based on a clustering result, verify using cvxmod
1 |
df |
data frame of l1 or l2 solutions |
lambda |
lambda values on which we will calculate the solutions |
... |
passed to cvxmod.cluster |
Toby Dylan Hocking
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | sim <- gendata(N <- 5,2,2,0.1)
colnames(sim$mat) <- c("height","length")
xyplot(length~height,data.frame(sim$mat,row=1:N),aspect="iso",group=row)
df <- clusterpath.l1.id(sim$mat)
if(cvxmod.available()){
cvx <- cvxcheck(df)
library(reshape2)
cvx.melt <- melt(cvx,measure.vars=1:2)
## plot each dimension separately using lattice
library(latticeExtra)
(p <- plot(df))
update(p,main="the path algorithm (lines) agrees with cvxmod (points)")+
xyplot(value~lambda|variable,cvx.melt,groups=row)
## plot the 2 dimensions together using ggplot2
(p <- plot2d(df))
## compare with cvx manually
p+
geom_point(aes(size=lambda/max(lambda)),data=cvx,shape=17,colour="red")+
ggtitle(paste("Optimal solutions from path algorithm (black circles)",
"agree with cvxmod (red triangles)"))
## or use a legend
p+
aes(shape=solver,colour=solver)+
geom_point(aes(size=lambda/max(lambda)),data=cvx)
}
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