vbmfa {autoMFA} | R Documentation |

## Variational Bayesian Mixture of Factor Analyzers (VB-MoFA)

### Description

An implementation of the Variational Bayesian Mixutre of Factor Analysers (Ghahramani and Beal 2000). This code is an R port of the MATLAB code which was written by M.J.Beal and released alongside their paper.

### Usage

```
vbmfa(Y, qmax = NULL, maxtries = 3, verbose = FALSE, varimax = FALSE)
```

### Arguments

`Y` |
An |

`qmax` |
Maximum factor dimensionality (default |

`maxtries` |
The maximum number of times the algorithm will attempt to split each component. |

`verbose` |
Whether or not verbose output should be printed during the model fitting process (defaults to 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*p*by*p*by*q*array 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.`Fhist`

:The values of*F*at each iteration of the algorithm.*F*is defined in (Ghahramani and Beal 2000).`times`

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

: The total time taken to fit the final model.

### References

Ghahramani Z, Beal MJ (2000).
“Variational inference for Bayesian Mixtures of Factor Analysers.”
In *Advances in neural information processing systems*, 449–455.

### See Also

`preprocess`

for centering and scaling data prior to using `vbmfa`

.

### Examples

```
RNGversion('4.0.3'); set.seed(3)
Yout <- preprocess(MFA_testdata)
MFA.fit <- vbmfa(Yout$Yout, maxtries = 2)
```

*autoMFA*version 1.0.0 Index]