somhca (version 0.2.0)

optimalSOM: Estimate Optimal SOM Grid Size

Description

Computes the optimal grid size for training a SOM using various quality measures and heuristic approaches.

Usage

optimalSOM(data, method = "A", increments, iterations)

Value

A data frame summarizing quality measures and their associated optimal grid dimensions. Use these results to select the most suitable grid size for your SOM.

Arguments

data

A preprocessed data matrix containing the input data for SOM training.

method

A character string indicating the method for estimating the maximum grid dimension. Options are:

"A"

Uses the heuristic formula by Vesanto et al. (default).

"B"

Applies an alternative heuristic approach.

numeric

Manually specified maximum dimension.

increments

An integer specifying the step size for increasing grid dimensions. For example, set increments to 2 or 5 to increment the grid size by 2 or 5 rows/columns at each step. Smaller increments lead to more granular searches but may increase computation time; larger increments risk errors if they exceed the estimated maximum SOM grid dimensions.

iterations

An integer defining the number of iterations for SOM training. A lower value, such as less than 500, helps reduce computation time. If the process takes too long or an error occurs, try reducing the number of iterations for quicker results.

Examples

# Create a toy matrix with 9 columns and 100 rows
data <- matrix(rnorm(900), ncol = 9, nrow = 100)  # 900 random numbers, 100 rows, 9 columns

# Run the optimalSOM function with the mock data
myOptimalSOM <- optimalSOM(data, method = "A", increments = 2, iterations = 300)