optimalSOM {somhca} | R Documentation |
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)
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:
|
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. |
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.
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)