# slopeHeuristic: Slope Heuristic for HDDC objects In HDclassif: High Dimensional Supervised Classification and Clustering

 slopeHeuristic R Documentation

## Slope Heuristic for HDDC objects

### Description

This function computes the slope heuristic for a set of objects obtained by the function `hddc`. The slope heuristic is a criterion in which the likelihood is penalized according to the result of the fit of the likelihoods on the complexities of the models.

### Usage

``````slopeHeuristic(x, plot = FALSE)
``````

### Arguments

 `x` An `hdc` object, obtained from the function `hddc`. `plot` Logical, default is `FALSE`. If `TRUE`, then a graph representing: 1) the likelihoods, the complexity, the fit (i.e. the slope) and 2) the value of the slope heuristic (in blue squares).

### Details

This function is only useful if there are many models (at least 3, better if more) that were estimated by the function `hddc`. If there are less than 2 models, the function wil l return an error.

### Value

A list of two elements:

 `best_model_index` The index of the best model, among all estimated models. `allCriteria` The data.frame containing all the criteria, with the new slope heuristic.

### Examples

``````# Clustering of the Crabs data set
data(Crabs)
prms = hddc(Crabs[,-1], K = 1:10) # we estimate ten models
slope = slopeHeuristic(prms, plot = TRUE)
plot(slope\$allCriteria) # The best model is indeed for 4 clusters
prms\$all_results[[slope\$best_model_index]] # we extract the best model

``````

HDclassif documentation built on Aug. 23, 2023, 9:06 a.m.