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### Weighted and robust archetypal analysis: simulation study
###
### Analysis used in 'Weighted and Robust Archetypal Analysis' by
### Manuel J. A. Eugster and Friedrich Leisch.
library('archetypes')
library('ggplot2')
sim.file <- function(x) {
system.file('opt', 'robust-simulation',
sprintf('%s.Rdata', x),
package = 'archetypes')
}
### Simulation 1: ####################################################
load(sim.file("sim1"))
str(sim1)
### Distances for one dimension and one number of outliers:
p1 <- subset(sim1, dim == 10 & perf %in% c("dist1", "dist2") & nout == 100)
p1 <- cast(p1, dim + n + nout + radius + sample + alg ~ perf)
ggplot(p1, aes(dist2, dist1)) +
geom_point() +
facet_grid(alg ~ radius)
### Distances for over the dimensions:
p2 <- subset(sim1, perf %in% c("dist1", "dist2") & nout == 100 & radius == 15)
p2 <- cast(p2, dim + n + nout + radius + sample + alg ~ perf)
ggplot(p2, aes(dist2, dist1)) +
geom_point() +
facet_grid(alg ~ dim)
### Number of iterations:
p3 <- subset(sim1, perf == "iters")
p3 <- ddply(p3, c("dim", "n", "nout", "radius", "perf", "alg"),
function(x)
c(iters = median(x$value)))
ggplot(p3, aes(dim, iters, group = alg, linetype = alg)) +
geom_line() +
facet_grid(nout ~ radius)
### Simulation 2: ####################################################
load(sim.file("sim2"))
str(sim2)
### Median distances and weighted RSS for the robust algorithm:
p4 <- subset(sim2, alg == "robust" & nout == 100 & radius == 15 &
perf %in% c("dist1", "dist2", "wrss"))
p4 <- ddply(p4, c("dim", "n", "nout", "radius", "k", "alg", "perf"),
function(p)
c(value = median(p$value)))
p4$panel <- ifelse(p4$perf != "wrss", "Distance", "Weighted RSS")
ggplot(p4, aes(k, value, group = perf)) +
geom_line() + geom_point() +
facet_grid(panel ~ ., scales = "free_y")
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