predict.MclustDR: Classify multivariate observations on a dimension reduced...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/mclustdr.R

Description

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

Usage

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  ## S3 method for class '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.

Author(s)

Luca Scrucca

References

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

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

MclustDR.

Examples

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mod = Mclust(iris[,1:4])
dr = MclustDR(mod)
pred = predict(dr)
str(pred)

data(banknote)
mod = MclustDA(banknote[,2:7], banknote$Status)
dr = MclustDR(mod)
pred = predict(dr)
str(pred)

Example output

Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
List of 5
 $ dir           : num [1:150, 1:4] 1.86 1.45 1.62 1.41 1.91 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:4] "Dir1" "Dir2" "Dir3" "Dir4"
 $ density       : num [1:150] 10.55 4.76 7.37 6.2 9.02 ...
 $ z             : num [1:150, 1:2] 1 1 1 1 1 ...
 $ uncertainty   : num [1:150] 2.51e-11 5.56e-08 3.64e-09 8.61e-08 8.50e-12 ...
 $ classification: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
List of 5
 $ dir           : num [1:200, 1:6] -0.863 -1.911 -1.748 -1.92 -1.684 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:6] "Dir1" "Dir2" "Dir3" "Dir4" ...
 $ density       : num [1:200] 7.14e-06 2.34e-01 2.72e-01 3.52e-01 3.37e-04 ...
 $ z             : num [1:200, 1:2] 2.22e-07 9.05e-21 2.53e-23 3.42e-21 2.77e-24 ...
 $ uncertainty   : num [1:200] 2.22e-07 0.00 0.00 0.00 0.00 ...
 $ classification: Factor w/ 2 levels "counterfeit",..: 2 2 2 2 2 2 2 2 2 2 ...

mclust documentation built on May 29, 2017, 5:36 p.m.