| M_ABCdist.WC | R Documentation |
Runs ABCdist.SingleInMultiple on each source, then feeds the
per-source dissimilarities to WeightedClust (type =
"dist") to explore convex combinations over a weight grid and return the
focus-weight clustering.
M_ABCdist.WC(
List,
distmeasure = rep("euclidean", length(List)),
weighting = rep("var", length(List)),
normalize = rep(list(NULL), length(List)),
gr = c(),
bag = rep(TRUE, length(List)),
numsim = 1000L,
numvar = rep(100L, length(List)),
linkage = rep("ward.D", length(List)),
NC = NULL,
NC2 = NULL,
StopRange = FALSE,
wc_weight = seq(1, 0, -0.1),
wc_weightclust = 0.5,
wc_linkage = "ward",
wc_alpha = 0.625,
mds = FALSE,
nugget_type = "between",
nugget_args = list()
)
List |
A list of |
distmeasure, weighting, normalize, bag, numvar, linkage |
Per-source vectors
(recycled to |
gr |
Unused; kept for compatibility. |
numsim |
Iterations per source. |
NC |
Base-cluster count per source ( |
NC2 |
Optional number of clusters to cut the final tree into. |
StopRange |
Passed to |
wc_weight |
Weight grid for |
wc_weightclust |
Focus weight whose result is returned in |
wc_linkage, wc_alpha |
Final clustering controls for |
mds |
Logical; MDS of the consensus dissimilarities. |
nugget_type, nugget_args |
Forwarded to the nugget weighting. |
A list of class "M_ABCdist_WC" with source_D, the full
WC object, the focus DistM and Clust, and the
weight_grid.
Amaratunga2008MosaiClusteR
M_ABCdist, WeightedClust
data(mosaic_toy)
fit <- M_ABCdist.WC(mosaic_toy$List, numsim = 40,
wc_weight = seq(1, 0, -0.25), wc_weightclust = 0.5)
dim(fit$DistM)
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