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 = NULL, parameters = NULL, z = NULL,
             classification = NULL, truth = NULL, uncertainty = NULL,
             what = c("classification", "error", "uncertainty"),
             quantiles = c(0.75, 0.95),
             addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"),
             symbols = NULL, colors = NULL, scale = FALSE,
             xlim = NULL, ylim = NULL, xlab = NULL, ylab = 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` | An integer value or a vector of integer values to be used as seed for random number generation. If multiple values are provided, then each seed should produce a different projection. By default, a single seed is drawn randomnly, so each call of `randProj()` produces different projections.
`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), `"error"`, `"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).
`addEllipses` | A logical indicating whether or not to add ellipses with axes corresponding to the within-cluster covariances in case of `"classification"` or `"uncertainty"` plots.
`fillEllipses` | A logical specifying whether or not to fill ellipses with transparent colors when `addEllipses = TRUE`.
`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` | Optional arguments specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots.
`xlab, ylab` | Optional arguments specifying the labels for, respectively, the horizontal and vertical axis.
`cex` | A numerical value 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.

### Value

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.

The function also returns an invisible list with components `basis`, the randomnly generated basis of the projection subspace, `data`, a matrix of projected data, and `mu` and `sigma` the component parameters transformed to the projection subspace.

### See Also

`clPairs`, `coordProj`, `mclust2Dplot`, `mclust.options`

### Examples

    est <- meVVV(iris[,-5], unmap(iris[,5]))
    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 = "error", main = TRUE)
    randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
              what = "uncertainty", main = TRUE)

[Package mclust version 5.4.8 Index]