GaussmixEM: Demo of EM algorithm for univariate Gaussian Mixture Model....
In jlin-vt/SML: Statistical Machine Learning
Description
Usage
Arguments
Value
View source: R/em_mix_gauss_faithful.R
Description
Demo of EM algorithm for univariate Gaussian Mixture Model.
using the Old Faithful dataset.
Usage
1
2
GaussmixEM(params, X, clusters = 2, tol = 1e-05, maxits = 100,
showits = T)
Arguments
params:
initial parameters (means, covariances, cluster probability).
X:
data.
clusters:
number of clusters desired.
tol:
tolerance.
maxits:
maximum iterations.
showits:
whether to show iterations.
Value
a list of objects:
probs: probability.
mu: mean.
var: variance.
resp: responsibilities.
cluster: cluster assignment.
jlin-vt/SML documentation built on Dec. 5, 2019, 2:05 a.m.
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Description Usage Arguments Value
View source: R/em_mix_gauss_faithful.R
Description
Demo of EM algorithm for univariate Gaussian Mixture Model. using the Old Faithful dataset.
Usage
1 2 | GaussmixEM(params, X, clusters = 2, tol = 1e-05, maxits = 100,
showits = T)
|
Arguments
params: |
initial parameters (means, covariances, cluster probability). |
X: |
data. |
clusters: |
number of clusters desired. |
tol: |
tolerance. |
maxits: |
maximum iterations. |
showits: |
whether to show iterations. |
Value
a list of objects: probs: probability. mu: mean. var: variance. resp: responsibilities. cluster: cluster assignment.
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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