getRSKCAlpha: Computes best alpha using Robust and Sparse Clustering...

In neobernad/evaluomeR: Evaluation of Bioinformatics Metrics

Computes best alpha using Robust and Sparse Clustering getRSKCAlpha

Description

Computes the proportion of the cases to be trimmed in robust sparse K-means, 0 <= alpha <= 1, see RSKC.

Usage

getRSKCAlpha(df, k, L1, max_alpha = 0.1, seed = NULL)

Arguments

df	Input data frame. The first column denotes the identifier of the evaluated individuals. The remaining columns contain the metrics used to evaluate the individuals. Rows with NA values will be ignored.
k	K value (number of clusters)
L1	A single L1 bound on weights (the feature weights), see RSKC.
max_alpha	Maximum value of alpha, iterating over seq(0, max_alpha, 0.05). Default is 0.1.
seed	Random seed to be used.

Value

Best suitable alpha.

Examples

data("ontMetrics")
alpha = getRSKCAlpha(ontMetrics, k=3, L1=2, seed=100)

neobernad/evaluomeR documentation built on Nov. 12, 2024, 11:29 p.m.