hca | R Documentation |
Hierarchical clustering analysis
hca(
x,
cor.method = scalop::cor.methods,
dist.method = scalop::dist.methods,
cluster.method = scalop::cluster.methods,
max.dist = 1,
h = NULL,
k = NULL,
min.size = 5,
max.size = 0.5
)
hca_cor(
x,
return.steps = F,
reorder = T,
reorder.col = reorder,
reorder.row = reorder,
...
)
hca_dist(x, return.steps = F, ...)
hca_tree(x, return.steps = F, ...)
hca_order(x, return.steps = F, cor.method = "pearson", ...)
hca_groups(x, return.steps = F, ...)
hca_reorder(x, col = T, row = T, cor.method = "none", ...)
x |
a matrix (features X observations), an object of class 'dist' or an object of class 'hclust'. |
cor.method |
option if <x> is a matrix. One of 'pearson' (default), 'kendall', 'spearman' or 'none' (no correlation coefficents computed). Default: scalop::cor.methods |
dist.method |
option if <x> is a matrix or correlation matrix. One of 'euclidean' (default), 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski' or 'none' (in which case stats::as.dist() will be used). Default: scalop::dist.methods |
cluster.method |
one of "average" (default), "complete", "single", "ward.D", "ward.D2", "mcquitty", "median" or "centroid". Default: scalop::cluster.methods |
max.dist |
maximum distance between observations. Default: 1 |
h |
height(s) at which to cut the tree. If NULL, h will be set to all tree heights. Default: NULL |
k |
number of groups to return from tree. If <h> and <k> are both not NULL, <k> takes precedence. Default: NULL |
min.size |
minimum allowed cluster/group size. Values between 0 and 1 are interpreted as fractions of total count. Groups smaller than <min.size> are filtered out. Default: 5 |
max.size |
maximum allowed cluster/group size. Values between 0 and 1 are interpreted as fractions of total count. Groups larger than <max.size> are filtered.out. Default: 0.5 |
list
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