mclust (version 3.4.7)

mclust1Dplot: Plot one-dimensional data modeled by an MVN mixture.

Description

Plot one-dimensional data given parameters of an MVN mixture model for the data.

Usage

mclust1Dplot(data, parameters=NULL, z=NULL, 
             classification=NULL, truth=NULL, uncertainty=NULL, 
             what = c("classification", "density", "errors", "uncertainty"),
             symbols=NULL, ngrid=length(data), xlab = NULL, xlim=NULL, CEX=1, 
             identify=FALSE, ...)

Arguments

data

A numeric vector of observations. Categorical variables are not allowed.

parameters

A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows: [object Object],[object Object],[object Object]

z

A matrix in which the [i,k]th entry gives the probability of observation i belonging to the kth class. Used to compute classification and uncertainty if those arguments aren'

classification

A numeric or character vector representing a classification of observations (rows) of data. If present argument z will be ignored.

truth

A numeric or character vector giving a known classification of each data point. If classification or z is also present, this is used for displaying classification errors.

uncertainty

A numeric vector of values in (0,1) giving the uncertainty of each data point. If present argument z will be ignored.

what

Choose from one of the following three options: "classification" (default), "density", "errors", "uncertainty".

symbols

Either an integer or character vector assigning a plotting symbol to each unique class classification. Elements in symbols correspond to classes in classification in order of appearance in the observation

ngrid

Number of grid points to use for density computation over the interval spanned by the data. The default is the length of the data set.

xlab

An argument specifying a label for the horizontal axis.

xlim

An argument specifying bounds of the plot. This may be useful for when comparing plots.

CEX

An argument specifying the size of the plotting symbols. The default value is 1.

identify

A logical variable indicating whether or not to add a title to the plot identifying the dimensions used.

...

Other graphics parameters.

Side Effects

A plot showing location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separated levels above the whole of the data.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.

See Also

mclust2Dplot, clPairs, coordProj

Examples

n <- 250 ## create artificial data
set.seed(1)
y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
yclass <- c(rep(1,n), rep(2,n), rep(3,n))

yModel <- mclustModel(y, mclustBIC(y))

mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
             what = "classification", identify = TRUE)

mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
             truth = yclass, what = "errors", identify = TRUE)

mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
             what = "density", identify = TRUE)

mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters,
            what = "uncertainty", identify = TRUE)