bmixfit | R Documentation |

`Bmix`

mixtureFits a mixture of 'k' components, where each component can be either a Binomial or a Beta-Binomial random variable. This function take as input two paramters that determine the possible numer of compoennets, for both distributions, and creates all possible input combinations to fit the model.

The best mode is scored using the Integrated Classification Likelihood, an extension of the Bayesian Information Criterion. Through one parameters it is possible to switch to the Bayesian Information Criterion.

Multiple fits can be computed, and a parameter controls when the Expectation Maximization algorithm should stop.

bmixfit( data, K.Binomials = 1:2, K.BetaBinomials = 0, epsilon = 1e-08, samples = 2, score = "ICL", silent = FALSE, description = "My BMix model" )

`data` |
A matrix or dataframe with two columns, the first one must represent the number of successes in the Binomial trials, the second the total number of trials. |

`K.Binomials` |
A vector of values that represents how many Binomial components should be fit to the data.By default this parameter is set to 'c(0:2)'. |

`K.BetaBinomials` |
A vector of values that represents how many Beta-Binomial components should be fit to the data. By default this parameter is set to '0'. |

`epsilon` |
The parameter that controls when the Expectation Maximization algorithm should stop. This is compared to the variation in the negative loglikelihood. |

`samples` |
Number of Expectation Maximization fits that should be computed per configuration of mixture. |

`score` |
The score for model selection, any of 'NLL', 'BIC' or 'ICL'. |

`silent` |
If 'FALSE', does not print outputs. |

An object of class `bmix`

that represents a fit mixture of this package.

# The same dataset used in the package vignette data = data.frame(successes = c(rbinom(30, 100, .4), rbinom(70, 100, .7)), trials = 100) # BMix fit with default parameters x = bmixfit(data) print(x)

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