Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.
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object |
an object of class |
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 |
eval.points |
a data frame or matrix giving the data projected on the reduced subspace. If provided |
... |
further arguments passed to or from other methods. |
Returns a list of with the following components:
dir |
a matrix containing the data projected onto the |
density |
densities from mixture model for each data point. |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
uncertainty |
The uncertainty associated with the classification. |
classification |
A vector of values giving the MAP classification. |
Luca Scrucca
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.
MclustDR
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