Description Usage Arguments Value Examples

A function that combines model-based clustering as well as your input to cluster your data

1 2 |

`data` |
the data you wish to use (must be continuous) |

`n` |
the number of points you wish to be queried on at once |

`G` |
number of clusters. The default allows Mclust to identify the number of clusters |

`query` |
how you wish to be queried. This must be one of "minimax", "maxWithinClust" or "minBetweenClust", with a default of "minimax". |

`distanceMethod` |
a method used to find distances between points. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", with a default of "euclidean." |

`iterationMax` |
the maximum number of iterations you wish to see when converging mstep and estep |

An object providing the optimal (according to BIC) mixture model estimation.

The details of the output components are as follows:

`call` |
The matched call |

`data` |
The input data matrix |

`modelName` |
A character string denoting the model at which the optimal BIC occurs |

`n` |
The number of observations in the data |

`d` |
The dimension of the data |

`G` |
The optimal number of mixture components |

`BIC` |
All BIC values |

`bic` |
Optimal BIC value |

`loglik` |
The log-likelihood corresponding to the optimal BIC |

`df` |
The number of estimated parameters |

`hypvol` |
The hypervolume parameter for the noise component if required, otherwise set to NULL (see hypvol). |

`parameters` |
A list with the following components: |

`pro` |
A vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed |

`mean` |
The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model. |

`variance` |
A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for mclustVariance for details. |

`z` |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class |

`classification` |
The classification corresponding to z, i.e. map(z). |

`uncertainty` |
The uncertainty associated with the classification. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
#Load data
library(mclust)
data(banknote)
#Create new dataset with only continuous variables
bankdata <- banknote[,2:7]
#Run imbc while querying user on onyl 1 data point at a time
#Use default querying algorithm (minimax)
output <- imbc(bankdata)
#query two points at once and using minimum between cluster distance as query method, and specifying 2 clusters
output2 <- imbc(bankdata, n = 2, G = 2, query = "minBetweenClust")
#gives vector of classification of each row
output2$classification
#classification probability matrix
#output2$z
``` |

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