LogLikelihood4Mixtures: LogLikelihood for Gaussian Mixture Models
In AdaptGauss: Gaussian Mixture Models (GMM)
Description
Usage
Arguments
Value
Author(s)
References
View source: R/LogLikelihood4Mixtures.R
Description
Computes the LogLikelihood for Gaussian Mixture Models.
Usage
1
LogLikelihood4Mixtures(Data, Means, SDs, Weights, IsLogDistribution)
Arguments
Data
Data for empirical PDF. Has to be an Array of values. NaNs
and NULLs will be deleted
Means
Optional: Means of gaussians of GMM.
SDs
Optional: StandardDevations of gaussians of GMM. (Has to be the same length as Means)
Weights
Optional: Weights of gaussians of GMM. (Has to be the same length as Means)
IsLogDistribution
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L
Value
List with
LogLikelihood
LogLikelihood = = sum(log(PDFmixture)
LogPDF
=log(PDFmixture)
PDFmixture
die Probability density function for each point
Author(s)
Alfred Ultsch, Catharina Lippmann
References
Pattern Recogintion and Machine Learning, C.M. Bishop, 2006, isbn: ISBN-13: 978-0387-31073-2, p. 433 (9.14)
AdaptGauss documentation built on March 26, 2020, 7:57 p.m.
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Description Usage Arguments Value Author(s) References
View source: R/LogLikelihood4Mixtures.R
Description
Computes the LogLikelihood for Gaussian Mixture Models.
Usage
1 | LogLikelihood4Mixtures(Data, Means, SDs, Weights, IsLogDistribution)
|
Arguments
Data |
Data for empirical PDF. Has to be an Array of values. NaNs and NULLs will be deleted |
Means |
Optional: Means of gaussians of GMM. |
SDs |
Optional: StandardDevations of gaussians of GMM. (Has to be the same length as Means) |
Weights |
Optional: Weights of gaussians of GMM. (Has to be the same length as Means) |
IsLogDistribution |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L |
Value
List with
LogLikelihood |
LogLikelihood = = sum(log(PDFmixture) |
LogPDF |
=log(PDFmixture) |
PDFmixture |
die Probability density function for each point |
Author(s)
Alfred Ultsch, Catharina Lippmann
References
Pattern Recogintion and Machine Learning, C.M. Bishop, 2006, isbn: ISBN-13: 978-0387-31073-2, p. 433 (9.14)
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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