GaussianMixtureMEM {mclustAddons} | R Documentation |
Modal EM algorithm for Gaussian Mixtures
Description
A function implementing a fast and efficient Modal EM algorithm for Gaussian mixtures.
Usage
GaussianMixtureMEM(
data,
pro,
mu,
sigma,
control = list(eps = 1e-05, maxiter = 1000, stepsize = function(t) 1 - exp(-0.1 * t),
denoise = TRUE, alpha = 0.01, keep.path = FALSE),
...
)
Arguments
data |
A numeric vector, matrix, or data frame of observations.
Categorical variables are not allowed. If a matrix or data frame, rows
correspond to observations ( |
pro |
A |
mu |
A |
sigma |
A |
control |
A list of control parameters:
|
... |
Further arguments passed to or from other methods. |
Value
Returns a list containing the following elements:
-
n
The number of input data points. -
d
The number of variables/features. -
parameters
The Gaussian mixture parameters. -
iter
The number of iterations of MEM algorithm. -
nmodes
The number of modes estimated by the MEM algorithm. -
modes
The coordinates of modes estimated by MEM algorithm. -
path
If requested, the coordinates of full paths to modes for each data point. -
logdens
The log-density at the estimated modes. -
logvol
The log-volume used for denoising (if requested). -
classification
The modal clustering classification of input data points.
Author(s)
Luca Scrucca
References
Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. doi: 10.1002/sam.11527