Spark Online Training by Edureka

PlotMixtures: Shows GMM

Description Arguments Author(s) See Also Examples

Description

Plots Gaussian Mixture Model without Bayes decision boundaries, such that:

Black is the PDE of Data

Red is color of the GMM

Blue is the color of components of the mixture

Arguments

Data

vector (1:N) of data points

Means

vector[1:L] of Means of Gaussians (of GMM),L == Number of Gaussians

SDs

vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means

Weights

vector of relative number of points in Gaussians (prior probabilities), 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

SingleColor

Optional,Color for line plot of all the single gaussians, default magenta

MixtureColor

Optional,Color of line lot for the mixture default red

DataColor

Optional,Color of line plot for the data, default black

SingleGausses

Optional, If TRUE, single gaussians are shown, default FALSE

axes

Optional,Default:TRUE with axis, see argument axis of plot

xlab

Optional, see plot

ylab

Optional, see plot

xlim

Optional, see plot

ylim

Optional, see plot

ParetoRad

Optional: Precalculated Pareto Radius to use

...

other plot arguments like xlim = c(1,10)

Author(s)

Michael Thrun

See Also

PlotMixturesAndBoundaries

Examples

1
2
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
PlotMixtures(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5),SingleColor='blue',SingleGausses=TRUE)


Search within the AdaptGauss package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.