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

mclust1Dplot {mclust}

R Documentation

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.

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 <- 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)

[Package mclust version 5.3 Index]