mclust (version 5.3)

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, colors = NULL, ngrid = length(data), 
             xlab = NULL, xlim = NULL, CEX = 1, 
             main = 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:

pro

Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of Gaussian components if the mixture model includes a Poisson noise term.

mean

The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.

variance

A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for mclustVariance for details.

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't available.

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 observations (the order used by the function unique). The default is to use a single plotting symbol |. Classes are delineated by showing them in separate lines above the whole of the data.

colors

Either an integer or character vector assigning a color to each unique class classification. Elements in colors correspond to classes in order of appearance in the observations (the order used by the function unique). The default is given is mclust.options("classPlotColors").

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.

main

A logical variable or NULL 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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

mclust2Dplot, clPairs, coordProj

Examples

# NOT RUN {
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 <- Mclust(y)

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

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

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

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

# }