ClickClust_EM {ClickClustCont} | R Documentation |

## 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

```
library(gtools)
data(SimData)
x<-SimData[[1]]
t<-SimData[[2]]
Click_2G<-ClickClust_EM(x=x,t=t,J=5,G=2,starts=10)
```

*ClickClustCont*version 0.1.7 Index]