disclapmix (version 0.2)

disclapmix: Discrete Laplace mixture inference using the EM algorithm

Description

disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. After the EM algorithm has converged, the centers are moved if the marginal likelihood increases by doing so. And then the EM algorithm is run again. This continues until the centers are not moved.

Usage

disclapmix(x, centers = 1:5, use.parallel = FALSE, iterations = 25, eps = 0.001, calculate.logLs = FALSE, plots.prefix = NULL, verbose = 1)

Arguments

x

Dataset

centers

A vector of center counts to fit the model for

use.parallel

Whether to do the computations in parallel (requires verbose = 0)

iterations

Maximum number of iterations in the EM-algorithm

eps

Convergence stop criteria in the EM algorithm which is compared to $\frac{\max { v_{new} - v_{old} }}{\max { v_{old} }}$, where v is a matrix of each observation's probability of belonging to a certain center

calculate.logLs

Whether to calculate the model's log likelihood at each iterations (might be slightly slower than not doing it)

plots.prefix

Where to put plots (NULL to avoid plotting)

verbose

from 0 to 3 (both including): 0 for silent, 3 for extra verbose

Value

• [object Object],[object Object],[object Object]

See Also

disclapmix-package disclapmix disclapmixfit plot.disclapmixfit predict.disclapmixfit extractMarginalBIC extractFullBIC disclap

Examples

demo(danes)
demo(simpop)