`summary.mclustBIC.Rd`

Optimal model characteristics and classification for model-based
clustering via `mclustBIC`

.

```
# S3 method for mclustBIC
summary(object, data, G, modelNames, ...)
```

- object
An

`'mclustBIC'`

object, which is the result of applying`mclustBIC`

to`data`

.- data
The matrix or vector of observations used to generate `object'.

- G
A vector of integers giving the numbers of mixture components (clusters) from which the best model according to BIC will be selected (

`as.character(G)`

must be a subset of the row names of`object`

). The default is to select the best model for all numbers of mixture components used to obtain`object`

.- modelNames
A vector of integers giving the model parameterizations from which the best model according to BIC will be selected (

`as.character(model)`

must be a subset of the column names of`object`

). The default is to select the best model for parameterizations used to obtain`object`

.- ...
Not used. For generic/method consistency.

A list giving the optimal (according to BIC) parameters,
conditional probabilities `z`

, and log-likelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:

- modelName
A character string denoting the model corresponding to the optimal BIC.

- n
The number of observations in the data.

- d
The dimension of the data.

- G
The number of mixture components in the model corresponding to the optimal BIC.

- bic
The optimal BIC value.

- loglik
The log-likelihood corresponding to the optimal BIC.

- parameters
A list with the following components:

`pro`

A vector whose

*k*th component is the mixing proportion for the*k*th 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

*k*th 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 data belongs to the*k*th class.- classification
`map(z)`

: The classification corresponding to`z`

.- uncertainty
The uncertainty associated with the classification.

- Attributes:
`"bestBICvalues"`

Some of the best bic values for the analysis.`"prior"`

The prior as specified in the input.`"control"`

The control parameters for EM as specified in the input.`"initialization"`

The parameters used to initial EM for computing the maximum likelihood values used to obtain the BIC.

```
irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
#> Best BIC values:
#> VEV,2 VEV,3 VVV,2
#> BIC -561.7285 -562.5522369 -574.01783
#> BIC diff 0.0000 -0.8237748 -12.28937
#>
#> Classification table for model (VEV,2):
#>
#> 1 2
#> 50 100
summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))
#> Best BIC values:
#> VVV,2 VVV,3 VVV,4
#> BIC -574.0178 -580.839630 -630.59996
#> BIC diff 0.0000 -6.821798 -56.58213
#>
#> Classification table for model (VVV,2):
#>
#> 1 2
#> 50 100
```