Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.

# S3 method for MclustDR
predict(object, dim = 1:object$numdir, newdata, eval.points, ...)

Arguments

object

an object of class 'MclustDR' resulting from a call to MclustDR.

dim

the dimensions of the reduced subspace used for prediction.

newdata

a data frame or matrix giving the data. If missing the data obtained from the call to MclustDR are used.

eval.points

a data frame or matrix giving the data projected on the reduced subspace. If provided newdata is not used.

...

further arguments passed to or from other methods.

Value

Returns a list of with the following components:

dir

a matrix containing the data projected onto the dim dimensions of the reduced subspace.

density

densities from mixture model for each data point.

z

a matrix whose [i,k]th entry is the probability that observation i in newdata belongs to the kth class.

uncertainty

The uncertainty associated with the classification.

classification

A vector of values giving the MAP classification.

References

Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.

Author

Luca Scrucca

See also

Examples

mod = Mclust(iris[,1:4])
dr = MclustDR(mod)
pred = predict(dr)
str(pred)
#> List of 5
#>  $ dir           : num [1:150, 1] 1.89 1.49 1.66 1.44 1.94 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr "Dir1"
#>  $ density       : num [1:150] 0.363 0.28 0.366 0.245 0.341 ...
#>  $ z             : num [1:150, 1:2] 1 1 1 1 1 ...
#>  $ uncertainty   : num [1:150] 1.08e-09 3.75e-07 2.88e-08 8.46e-07 5.17e-10 ...
#>  $ classification: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...

data(banknote)
mod = MclustDA(banknote[,2:7], banknote$Status)
dr = MclustDR(mod)
pred = predict(dr)
str(pred)
#> List of 5
#>  $ dir           : num [1:200, 1:2] -0.946 -1.751 -1.583 -1.742 -1.528 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:2] "Dir1" "Dir2"
#>  $ density       : num [1:200] 0.137 0.277 0.169 0.31 0.462 ...
#>  $ z             : num [1:200, 1:2] 1.47e-08 4.43e-16 4.83e-18 2.49e-17 1.67e-15 ...
#>  $ uncertainty   : num [1:200] 1.47e-08 4.44e-16 0.00 0.00 1.78e-15 ...
#>  $ classification: Factor w/ 2 levels "counterfeit",..: 2 2 2 2 2 2 2 2 2 2 ...