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", "error", "uncertainty"),
                 symbols = NULL, colors = NULL, ngrid = length(data),
                 xlab = NULL, ylab = NULL,
                 xlim = NULL, ylim = 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 options: `"classification"` (default), `"density"`, `"error"`, `"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, ylab` | An argument specifying a label for the axes.
`xlim, ylim` | 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.

### Value

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.

### See Also

`mclust2Dplot`, `clPairs`, `coordProj`

### Examples

    ## Not run:
    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")

    mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
                 what = "error", truth = yclass)

    mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
                 what = "density")

    mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters,
                what = "uncertainty")

    ## End(Not run)

[Package mclust version 5.4.5 Index]