HCA | R Documentation |
Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples.
HCA( dist_method = "euclidean", cluster_method = "complete", minkowski_power = 2, factor_name, ... )
dist_method |
(character) Distance measure. Allowed values are limited to the following:
The default is |
cluster_method |
(character) Agglomeration method. Allowed values are limited to the following:
The default is |
minkowski_power |
(numeric) The default is |
factor_name |
(character) The name of a sample-meta column to use. |
... |
Additional slots and values passed to |
This object makes use of functionality from the following packages:
stats
A HCA
object with the following output
slots:
dist_matrix | (dist) An object containing pairwise distance information between samples. |
hclust | (hclust) An object of class hclust which describes the tree produced by the clustering process. |
factor_df | (data.frame) |
R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
D = iris_DatasetExperiment() M = HCA(factor_name='Species') M = model_apply(M,D)
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