R: Random projections of multidimensional data modeled by an MVN...

randProj {mclust}

R Documentation

Random projections of multidimensional data modeled by an MVN mixture.

Description

Plots random projections given multidimensional data and parameters of an MVN mixture model for the data.

Usage

randProj(data, seeds=0, parameters=NULL, z=NULL,
         classification=NULL, truth=NULL, uncertainty=NULL, 
         what = c("classification", "errors", "uncertainty"),
         quantiles = c(0.75, 0.95), symbols=NULL, colors=NULL, scale = FALSE, 
         xlim=NULL, ylim=NULL, CEX = 1, PCH = ".", main = FALSE, ...)

Arguments

`data`	A numeric matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`seeds`	A vector if integer seeds for random number generation. Elements should be in the range `0:1000`. Each seed should produce a different projection.
`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: `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), `"errors"`, `"uncertainty"`.
`quantiles`	A vector of length 2 giving quantiles used in plotting uncertainty. The smallest symbols correspond to the smallest quantile (lowest uncertainty), medium-sized (open) symbols to points falling between the given quantiles, and large (filled) symbols to those in the largest quantile (highest uncertainty). The default is (0.75,0.95).
`symbols`	Either an integer or character vector assigning a plotting symbol to each unique class in `classification`. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `unique`). The default is given by `mclust.options("classPlotSymbols")`.
`colors`	Either an integer or character vector assigning a color to each unique class in `classification`. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `unique`). The default is given by `mclust.options("classPlotColors")`.
`scale`	A logical variable indicating whether or not the two chosen dimensions should be plotted on the same scale, and thus preserve the shape of the distribution. Default: `scale=FALSE`
`xlim, ylim`	Arguments specifying bounds for the ordinate, abscissa 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.
`PCH`	An argument specifying the symbol to be used when a classificatiion has not been specified for the data. The default value is a small dot ".".
`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 a random two-dimensional projection of the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.

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

est <- meVVV(iris[,-5], unmap(iris[,5]))

## Not run: 
par(pty = "s", mfrow = c(1,1))
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
          what = "classification", main = TRUE) 
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
          truth = iris[,5], what = "errors", main = TRUE) 
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
          what = "uncertainty", main = TRUE) 

## End(Not run)

[Package mclust version 5.3 Index]