simMultiGauss: Simulate Random Samples from Multivariate Gaussian Mixture...
In nwakim/nwREM: Implementation of an L0 penalized mvGMM EM algorithm
Description
Usage
Arguments
Value
Description
Simulate Random Samples from Multivariate Gaussian Mixture Model
Usage
1
simMultiGauss(n, mix_prop, mus, covs, perc_out)
Arguments
n
Number of random samples to simulate
mix_prop
k-dimensional Vector of mixing proportions (to be normalized)
mus
(d x k) Matrix of mean parameters
covs
(d x d x k) Multi-way array of covariance parameters
perc_out
Percent (%) of random samples that will be outliers
Value
A list containing the following values
* x : A matrix of simulated random samples from multi-dimensional Gaussian mixtures
* z : A vector of hidden variables indicating the source components
nwakim/nwREM documentation built on May 22, 2019, 5:34 p.m.
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Description Usage Arguments Value
Description
Simulate Random Samples from Multivariate Gaussian Mixture Model
Usage
1 | simMultiGauss(n, mix_prop, mus, covs, perc_out)
|
Arguments
n |
Number of random samples to simulate |
mix_prop |
k-dimensional Vector of mixing proportions (to be normalized) |
mus |
(d x k) Matrix of mean parameters |
covs |
(d x d x k) Multi-way array of covariance parameters |
perc_out |
Percent (%) of random samples that will be outliers |
Value
A list containing the following values * x : A matrix of simulated random samples from multi-dimensional Gaussian mixtures * z : A vector of hidden variables indicating the source components
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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