Description Usage Arguments Value References See Also Examples

Clustering by fitting a mixture model using EM with `K`

groups
and unconstrained covariance matrices for a matrix variate normal or
matrix variate t distribution (with specified degrees of freedom `nu`

).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

`x` |
data, |

`init` |
a list containing an array of |

`prior` |
prior for the |

`K` |
number of classes - provide either this or the prior. If this is provided, the prior will be of uniform distribution among the classes. |

`iter` |
maximum number of iterations. |

`model` |
whether to use the |

`method` |
what method to use to fit the distribution. Currently no options. |

`row.mean` |
By default, |

`col.mean` |
By default, |

`tolerance` |
convergence criterion, using Aitken acceleration of the log-likelihood by default. |

`nu` |
degrees of freedom parameter. Can be a vector of length |

`...` |
pass additional arguments to |

`verbose` |
whether to print diagnostic output, by default |

`miniter` |
minimum number of iterations |

`convergence` |
By default, |

A list of class `MixMatrixModel`

containing the following
components:

`prior`

the prior probabilities used.

`init`

the initialization used.

`K`

the number of groups

`N`

the number of observations

`centers`

the group means.

`U`

the between-row covariance matrices

`V`

the between-column covariance matrix

`posterior`

the posterior probabilities for each observation

`pi`

the final proportions

`nu`

The degrees of freedom parameter if the t distribution was used.

`convergence`

whether the model converged

`logLik`

a vector of the log-likelihoods of each iteration ending in the final log-likelihood of the model

`model`

the model used

`method`

the method used

`call`

The (matched) function call.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
Andrews, Jeffrey L., Paul D. McNicholas, and Sanjeena Subedi. 2011.
"Model-Based Classification via Mixtures of Multivariate
T-Distributions." Computational Statistics & Data Analysis 55 (1):
520–29. \doi{10.1016/j.csda.2010.05.019}.
Fraley, Chris, and Adrian E Raftery. 2002. "Model-Based Clustering,
Discriminant Analysis, and Density Estimation." Journal of the
American Statistical Association 97 (458). Taylor & Francis: 611–31.
\doi{10.1198/016214502760047131}.
McLachlan, Geoffrey J, Sharon X Lee, and Suren I Rathnayake. 2019.
"Finite Mixture Models." Annual Review of Statistics and Its
Application 6. Annual Reviews: 355–78.
\doi{10.1146/annurev-statistics-031017-100325}.
Viroli, Cinzia. 2011. "Finite Mixtures of Matrix Normal Distributions
for Classifying Three-Way Data." Statistics and Computing 21 (4):
511–22. \doi{10.1007/s11222-010-9188-x}.
``` |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
set.seed(20180221)
A <- rmatrixt(20,mean=matrix(0,nrow=3,ncol=4), df = 5)
# 3x4 matrices with mean 0
B <- rmatrixt(20,mean=matrix(1,nrow=3,ncol=4), df = 5)
# 3x4 matrices with mean 1
C <- array(c(A,B), dim=c(3,4,40)) # combine into one array
prior <- c(.5,.5) # equal probability prior
# create an intialization object, starts at the true parameters
init = list(centers = array(c(rep(0,12),rep(1,12)), dim = c(3,4,2)),
U = array(c(diag(3), diag(3)), dim = c(3,3,2))*20,
V = array(c(diag(4), diag(4)), dim = c(4,4,2))
)
# fit model
res<-matrixmixture(C, init = init, prior = prior, nu = 5,
model = "t", tolerance = 1e-3, convergence = FALSE)
print(res$centers) # the final centers
print(res$pi) # the final mixing proportion
plot(res) # the log likelihood by iteration
logLik(res) # log likelihood of final result
BIC(res) # BIC of final result
predict(res, newdata = C[,,c(1,21)]) # predicted class membership
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.