summary.MixtureMissing: Summary for MixtureMissing

In MixtureMissing: Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random

Summary for MixtureMissing

Description

Summarizes main information regarding a MixtureMissing object.

Usage

## S3 method for class 'MixtureMissing'
summary(object, ...)

Arguments

object	A MixtureMissing object or an output of select_mixture. In the latter, only the best model will be considered.
...	Arguments to be passed to methods, such as graphical parameters.

Details

Information includes the model used to fit the data set, initialization method, clustering table, total outliers, outliers per cluster, mixing proportions, component means and variances, final log-likelihood value, information criteria.

Value

No return value, called to summarize the fitted model's results

Examples

#++++ With no missing values ++++#

X <- auto[, c('horsepower', 'highway_mpg', 'price')]
mod <- MCNM(X, G = 2, init_method = 'kmedoids', max_iter = 10)
summary(mod)

#++++ With missing values ++++#

X <- auto[, c('normalized_losses', 'horsepower', 'highway_mpg', 'price')]
mod <- MCNM(X, G = 2, init_method = 'kmedoids', max_iter = 10)
summary(mod)

MixtureMissing documentation built on Oct. 16, 2024, 1:09 a.m.