randProj {mclust} | R Documentation

## Random projections for data in more than two dimensions modelled by an MVN mixture.

### Description

Plots random projections given data in more than two dimensions and parameters of an MVN mixture model for the data.

### Usage

    randProj(data, seeds = 0, ...,
             type = c("classification", "uncertainty", "errors"), ask = TRUE,
             quantiles = c(0.75,0.95), symbols, scale = FALSE, identify = FALSE,
             CEX = 1, PCH = ".", xlim, ylim)

### 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 of integers between 0 and 1000, specifying seeds for the random projections. The default value is the single seed 0.
`...` | Any number of the following: classification A numeric or character vector representing a classification of observations (rows) of `data`. uncertainty A numeric vector of values in (0,1) giving the uncertainty of each data point. z A matrix in which the [i,k]the entry gives the probability of observation i belonging to the kth class. Used to compute `classification` and `uncertainty` if those arguments aren't available. 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. mu A matrix whose columns are the means of each group. sigma A three dimensional array in which `sigma[,,k]` gives the covariance for the kth group. decomp A list with `scale`, `shape` and `orientation` components giving an alternative form for the covariance structure of the mixture model.
`type` | Any subset of `c("classification","uncertainty","errors")`. The function will produce the corresponding plot if it has been supplied sufficient information to do so. If more than one plot is possible then users will be asked to choose from a menu if `ask=TRUE`.
`ask` | A logical variable indicating whether or not a menu should be produced when more than one plot is possible. The default is `ask=TRUE`.
`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 `classification`. Elements in `symbols` correspond to classes in `classification` in order of appearance in `classification` (the order used by the S-PLUS function `unique`). Default: If G is the number of groups in the classification, the first G symbols in `.Mclust\$symbols`, otherwise if G is less than 27 then the first G capital letters in the Roman alphabet.
`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`
`identify` | A logical variable indicating whether or not to add a title to the plot identifying the dimensions used.
`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 ".".
`xlim, ylim` | Arguments specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots.

### Value

Random projections of the data, possibly showing location of the mixture components, classification, uncertainty, and classficaition errors.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`coordProj`, `spinProj`, `mclust2Dplot`, `mclustOptions`, `do.call`,

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]

    msEst <- mstepVVV(irisMatrix, unmap(irisClass))

    par(pty = "s", mfrow = c(2,3))
    randProj(irisMatrix, seeds = 0:5, truth=irisClass,
             mu = msEst$mu, sigma = msEst$sigma, z = msEst$z)
    do.call("randProj", c(list(data = irisMatrix, seeds = 0:5, truth=irisClass),
                               msEst))

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