PlotMixtures {AdaptGauss} | R Documentation |
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
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 |
xlab |
Optional, see |
ylab |
Optional, see |
xlim |
Optional, see |
ylim |
Optional, see |
ParetoRad |
Optional: Precalculated Pareto Radius to use |
... |
other plot arguments like xlim = c(1,10) |
Example shows that overlapping variances of gaussians will result in inappropriate decision boundaries.
Michael Thrun
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)