mclust1Dplot {mclust} | R Documentation

## Plot one-dimensional data modelled by an MVN mixture.

### Description

Plot one-dimensional data given parameters of an MVN mixture model for the data.

### Usage

    mclust1Dplot(data, ...,
                 type = c("classification","uncertainty","density","errors"),
                 ask = TRUE, symbols, grid = 100, identify = FALSE, CEX = 1, xlim)

### Arguments

`data` | A numeric vector of observations. Categorical variables are not allowed.
`...` | One or more 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 vector whose entries are the means of each group. sigma Either a vector whose entries are the variances for each group or a scalar giving a common variance for the groups. pro The vector of mixing proportions.
`type` | Any subset of `c("classification","uncertainty","density","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`.
`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.
`grid` | Number of grid points to use.
`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.
`xlim` | An argument specifying bounds of the plot. This may be useful for when comparing plots.

### Side Effects

One or more plots showing location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separate lines above the whole of the data.

### 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. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002). 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

`mclust2Dplot`, `clPairs`, `coordProj`, `do.call`

### Examples

    n <- 250 ## create artificial data
    set.seed(0)
    y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
    yclass <- c(rep(1,n), rep(2,n), rep(3,n))

    yEMclust <- summary(EMclust(y),y)

    mclust1Dplot(y, identify = TRUE, truth = yclass, z = yEMclust$z, ask=FALSE,
                 mu = yEMclust$mu, sigma = yEMclust$sigma, pro = yEMclust$pro)

    do.call("mclust1Dplot",
            c(list(data = y, identify = TRUE, truth = yclass, ask=FALSE),
            yEMclust))

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