R: Coordinate projections of multidimensional data modeled by an...

coordProj {mclust} | R Documentation

## Coordinate projections of multidimensional data modeled by an MVN mixture.

### Description

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

### Usage

    coordProj(data, dimens = c(1,2), parameters = NULL, z = NULL,
              classification = NULL, truth = NULL, uncertainty = NULL,
              what = c("classification", "errors", "uncertainty"),
              addEllipses = TRUE, 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.
`dimens` | A vector of length 2 giving the integer dimensions of the desired coordinate projections. The default is `c(1,2)`, in which the first dimension is plotted against the second.
`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"`.
`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.
`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 classification 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 two-dimensional coordinate projection of the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.

### See Also

`clPairs`, `randProj`, `mclust2Dplot`, `mclust.options`

### Examples

    ## Not run:
    est <- meVVV(iris[,-5], unmap(iris[,5]))
    par(pty = "s", mfrow = c(1,1))
    coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z,
              what = "classification", main = TRUE)
    coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z,
              truth = iris[,5], what = "errors", main = TRUE)
    coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z,
              what = "uncertainty", main = TRUE)

    ## End(Not run)

[Package mclust version 5.4.2 Index]