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#' @demo Archetypal analysisof the UCI domain benchmark experiment.
#'
#' @details
#' Note that domain-based benchmark experiments are not fully
#' integrated in the workflow provided by benchmark
#' v0.3-2. Therefore, the one or the other HACK might be available.
library("benchmark")
library("archetypes")
data("uci621raw", package = "benchmark")
alg_cols <- c(lda = "#984EA3", rf = "#FF7F00",
knn = "#FFFF33", rpart = "#E41A1C",
svm = "#377EB8", nnet = "#4DAF4A")
alg_light_cols <-c(lda = "#DECBE4", rf = "#FED9A6",
knn = "#FFFFCC", rpart = "#FBB4AE",
svm = "#B3CDE3", nnet = "#CCEBC5")
### Aggregation of the data: #########################################
dat <- do.call(rbind, lapply(1:6, function(i) uci621raw[, i, 1, ]))
alg <- rep(dimnames(uci621raw)$alg, each = 250)
dat <- na.omit(dat)
alg <- alg[-attr(dat, "na.action")]
### Data set order based on hierarchical clustering:
# data("uci621rel", package = "benchmark")
# d <- relation_dissimilarity(uci621rel)
# hc <- hclust(d, method = "complete")
# dat <- dat[, hc$order]
### Archetypes: ######################################################
set.seed(1234)
as <- stepArchetypes(data = dat, k = 2:10)
screeplot(as)
### Go with k = 4:
k <- 4
a <- bestModel(as[[k-1]])
## Archetypes:
barplot(a, dat)
## Data versus archetypes, parallel coordinates plot:
pcplot(a, dat,
atypes.col = atype_cols,
data.col = alg_light_cols[alg])
## Coefficient matrix alpha, parallel coordinates plot:
alpha <- coef(a, "alphas")
colnames(alpha) <- sprintf("A%s", 1:k)
pcplot(alpha, col = alg_cols[alg])
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