ClickClustCont (version 0.1.7)

ClickClust_EM: EM Algorithm for Continuous Time Markov Models

Description

This function fits the continuous time first-order Markov model for a specified set of groups and returns the model chosen by the BIC. This is an implementation of the methodology developed in Gallaugher and McNicholas (2019).

Usage

ClickClust_EM(x, t, J, G, itemEM = 5, starts = 100, maxit = 5000,
  tol = 0.001, Contin = TRUE, Verbose = TRUE, seed = 1,
  known = NULL, crit = "BIC", returnall = FALSE)

Arguments

x

A list of states

t

A list of times spent in each state

J

The total number of states

G

A vector containing the number of groups to test

itemEM

The number of emEM iterations for initialization (defaults to 5)

starts

The number of random starting values for the emEM algorithm (defaults to 100)

maxit

The maximum number of iterations after initialization (defaults to 5000)

tol

The tolerance for convergence (defaults to 0.001)

Contin

Fit the continuous time model (defaults to TRUE). If FALSE, fit the discrete model.

Verbose

Display Messages (defaults to TRUE)

seed

Sets the seed for the emEM algorithm (defaults to 1)

known

A vector of labels for semi-supervised classification. 0 indicates unknown observations. The known labels are denoted by their group number (1,2,3, etc.).

crit

The model selection criterion to use ("BIC" or "ICL"). Defaults to "BIC".

returnall

If true, returns the results for all groups considered. Defaults to FALSE.

Value

Returns a list with parameter and classification estimates for the best model chosen by the selection criterion.

References

Michael P.B. Gallaugher and Paul D. McNicholas (2019). Clustering and semi-supervised classification for clickstream data via mixture models. arXiv preprint arXiv:1802.04849v2.

Examples

# NOT RUN {
library(gtools)
data(SimData)
x<-SimData[[1]]
t<-SimData[[2]]
Click_2G<-ClickClust_EM(x=x,t=t,J=5,G=2,starts=10)
# }