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### Weighted and robust archetypal analysis: ozone data set
###
### Analysis used in 'Weighted and Robust Archetypal Analysis' by
### Manuel J. A. Eugster and Friedrich Leisch.
library('archetypes')
### Data set:
data('Ozone', package = 'mlbench')
oz <- Ozone[, -c(1, 2, 3, 9)]
oz <- na.omit(oz)
colnames(oz) <- c('OZONE', '500MH', 'WDSP', 'HMDTY', 'STMP',
'INVHT', 'PRGRT', 'INVTMP', 'VZBLTY')
oz0 <- scale(oz)
### The three original archetypes:
set.seed(1234)
a.oz <- archetypes(oz0, 3)
parameters(a.oz)
barplot(a.oz, oz0, percentiles = TRUE)
panorama(a.oz, oz0)
### Data set with outliers:
set.seed(1234)
outliers <- t(sapply(runif(5, min = 1.5, max = 2),
function(x)
x * apply(oz, 2, max) + apply(oz, 2, IQR)))
oz1 <- scale(rbind(oz, outliers))
pairs(oz1)
### Original archetypal algorithm:
set.seed(1234)
a.oz1 <- archetypes(oz1, 3)
parameters(a.oz1)
barplot(a.oz1, oz1, percentiles = TRUE)
panorama(a.oz1, oz1)
### Robust archetypal algorithm:
set.seed(1236)
ra.oz1 <- robustArchetypes(oz1, 3)
parameters(ra.oz1)
barplot(ra.oz1, oz1, percentiles = TRUE)
panorama(a.oz1, oz1)
plot(rss(ra.oz1, type = 'single'), xlab = '', ylab = 'RSS')
plot(weights(ra.oz1, type = 'reweights'), xlab = '', ylab = 'Weight')
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