em_clust_exp: Mixture modeling of exponentially distributed data.

In alexWhitworth/emclustr: Mixture Modeling for Model-Based Clustering and Classification

Description

This function uses the EM algorithm to do clustering of k-mixture components where each component is exponential(λ).

Usage

em_clust_exp(data, nclust, itmax = 10000, tol = 10^-8)

Arguments

data	An n-length vector. Must not be character.
nclust	The number of clusters.
itmax	The maximum number of iterations allowed. Defaults to 10000.
tol	Tuning parameter for convergence. Defaults to 10^-8.

Value

A list containing: it the number of iterations; clust_prop the estimated mixture proportions; clust_params the estimated mixture parameters; mix_est a vector of the estimated mixture for each data point; log_lik the log likelihood of the data; bic the modeled BIC.

Examples

# generate test data
c1 <- rexp(100, 1); c2 <- rexp(100, 50); c3 <- rexp(100, 100);
c_tot <- c(c1, c2, c3); rm(c1,c2,c3)
# run example
exp_clust <- em_clust_exp(c_tot, nclust= 3)

alexWhitworth/emclustr documentation built on May 11, 2019, 11:25 p.m.