amofa {autoMFA} | R Documentation |

## Adaptive Mixture of Factor Analyzers (AMoFA)

### Description

An implementation of the Adaptive Mixture of Factor Analyzers (AMoFA) algorithm from (Kaya and Salah 2015). This code is a R port of the MATLAB code which was included with that paper.

### Usage

```
amofa(data, itmax = 100, verbose = FALSE, varimax = FALSE)
```

### Arguments

`data` |
An |

`itmax` |
The maximum number of EM iterations allowed for the estimation of each MFA model. |

`verbose` |
Boolean indicating whether or not to print more verbose output, including the number of EM-iterations used and the total running time. Default is FALSE. |

`varimax` |
Boolean indicating whether the output factor loading matrices should be constrained using varimax rotation or not. |

### Value

A list containing the following elements:

`model`

: A list specifying the final MFA model. This contains:`B`

: A list containing the factor loading matrices for each component.`D`

: A*p*by*p*by*g*array of error variance matrices.`mu`

: A*p*by*g*array containing the mean of each cluster.`pivec`

: A 1 by g vector containing the mixing proportions for each FA in the mixture.`numFactors`

: A*1*by*g*vector containing the number of factors for each FA.

`clustering`

: A list specifying the clustering produced by the final model. This contains:`responsibilities`

: A*n*by*g*matrix containing the probability that each point belongs to each FA in the mixture.`allocations`

: A*n*by 1 matrix containing which FA in the mixture each point is assigned to based on the responsibilities.

`diagnostics`

: A list containing various pieces of information related to the fitting process of the algorithm. This contains:`bic`

: The BIC of the final model.`logL`

: The log-likelihood of the final model.`totalEM`

: The total number of EM iterations used.`progress`

: A matrix containing information about the decisions made by the algorithm.`times`

: The time taken for each loop in the algorithm.`totalTime`

: The total time taken to fit the final model.

### References

Kaya H, Salah AA (2015).
“Adaptive Mixtures of Factor Analyzers.”
*arXiv preprint arXiv:1507.02801*.

### Examples

```
RNGversion('4.0.3'); set.seed(3)
MFA.fit <- amofa(autoMFA::MFA_testdata)
```

*autoMFA*version 1.0.0 Index]