MclustMEM | R Documentation |
Modal-clustering estimation by applying the Modal EM algorithm to Gaussian mixtures fitted using the mclust package.
MclustMEM(mclustObject, data = NULL, ...) ## S3 method for class 'MclustMEM' print(x, digits = getOption("digits"), ...) ## S3 method for class 'MclustMEM' summary(object, ...)
mclustObject |
An object of class |
data |
If provided, a numeric vector, matrix, or data frame of observations. If a matrix or data frame, rows correspond to observations (n) and columns correspond to variables (d). If not provided, the data used for fitting the Gaussian mixture model, and provided with the |
x, object |
An object of class |
digits |
The number of significant digits to use for printing. |
... |
Further arguments passed to or from other methods. |
Returns an object of class 'MclustMEM'
. See also the output returned by GaussianMixtureMEM
.
Luca Scrucca
Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527
GaussianMixtureMEM
, plot.MclustMEM
.
data(Baudry_etal_2010_JCGS_examples, package = "mclust") plot(ex4.1) GMM <- Mclust(ex4.1) plot(GMM, what = "classification") MEM <- MclustMEM(GMM) MEM summary(MEM) plot(MEM) plot(ex4.4.2) GMM <- Mclust(ex4.4.2) plot(GMM, what = "classification") MEM <- MclustMEM(GMM) MEM summary(MEM) plot(MEM, addDensity = FALSE)
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