Clustering longitudinal data using the likelihood as a metric...

The log-likelihood is calculated with taking into account the type of data ('gaussian', 'binomial', ... etc) and the link function.

1 |

`y` |
Observed values. |

`n` |
Vector of '1's and same length as y. |

`mu` |
Predicted values. |

`wt` |
Weights. |

`family` |
An object of class |

`nparam` |
Number of parameters of the model. |

`disp_mod` |
Dispersion of the 'glm' model. |

This function calculates the log-likelihood for the
exponential `family`

, it uses the 'AIC'
function to realise this operatin.

The log-likelihood of an individual (trajectory).

Meant to be used internally.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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