density {mclust} | R Documentation

## Kernel Density Estimation

### Description

This is exaclty the same function as in the base package but for the `method` argument: if it is given and equals `"mclust"`, the `mclust` density estimation is used. Optionally, the number of gaussians to be considered can be given as well (`G`).

### Usage

    density(..., method, G)

### Arguments

`...` | Arguments to the `density` function in the base package.
`method` | If equal to "mclust", `EMclust` is used to estimate the density.
`G` | The number of gaussians to consider in the model-based density estimation. Default: 1:9. Ignored if method is not equal to "mclust".

### Value

If `give.Rkern` is true, the number R(K), otherwise an object with class `"density"` whose underlying structure is a list containing the following components.

`x` | the `n` coordinates of the points where the density is estimated.
`y` | the estimated density values.
`bw` | the bandwidth used.
`N` | the sample size after elimination of missing values.
`call` | the call which produced the result.
`data.name` | the deparsed name of the `x` argument.
`has.na` | logical, for compatibility (always FALSE).

### References

Fraley, C. and Raftery, A.E. (2002) MCLUST: software for model-based clustering, density estimation and discriminant analysis. Technical Report No. 415, Dept. of Statistics, University of Washington.

Scott, D. W. (1992) Multivariate Density Estimation. Theory, Practice and Visualization. New York: Wiley.

Sheather, S. J. and Jones M. C. (1991) A reliable data-based bandwidth selection method for kernel density estimation. J. Roy. Statist. Soc. B, 683–690.

Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. New York: Springer.

### See Also

`density` (base package), `bw.nrd`, `plot.density`, `hist`.

### Examples

    plot(density(c(-20,rep(0,98),20)), xlim = c(-4,4))# IQR = 0

    # The Old Faithful geyser data
    data(faithful)
    d <- density(faithful$eruptions, bw = "sj")
    d
    plot(d)
    dmc <- density(faithful$eruptions, method="mclust")
    plot(dmc, type = "n")
    polygon(dmc, col = "wheat")
    lines(d, col="red")

    ## Missing values:
    x <- xx <- faithful$eruptions
    x[i.out <- sample(length(x), 10)] <- NA
    doRmc <- density(x=x, method="mclust", na.rm = TRUE)
    lines(doRmc, col="blue")
    doR <- density(x, bw = 0.15, na.rm = TRUE)
    lines(doR, col = "green")
    rug(x)
    points(xx[i.out], rep(0.01, 10))

    ## function formals returns something different now the original
    ## density function is masked...
    base.density <- if(exists("density", envir = NULL)) {
      get("density", envir = NULL)
    } else
      stats::density
    (kernels <- eval(formals(base.density)$kernel))

    ## show the kernels in the R parametrization
    plot (density(0, bw = 1), xlab = "",
          main="R's density() kernels with bw = 1")
    for(i in 2:length(kernels))
       lines(density(0, bw = 1, kern =  kernels[i]), col = i)
    legend(1.5,.4, legend = kernels, col = seq(kernels),
           lty = 1, cex = .8, y.int = 1)

    data(precip)
    bw <- bw.SJ(precip) ## sensible automatic choice
    plot(density(precip, bw = bw, n = 2^13))
    lines(density(precip, G=2:5, method="mclust"), col="red")
    rug(precip)

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