mclust (version 5.0.2)

cdfMclust: Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution

Description

Compute the cumulative density function (cdf) or quantiles from an estimated one-dimensional Gaussian mixture fitted using densityMclust.

Usage

cdfMclust(object, data, ngrid = 100, ...)
quantileMclust(object, p, ...)

Arguments

object

a densityMclust model object.

data

a numeric vector of evaluation points.

ngrid

the number of points in a regular grid to be used as evaluation points if no data are provided.

p

a numeric vector of probabilities.

...

further arguments passed to or from other methods.

Value

• cdfMclust returns a list of x and y values providing, respectively, the evaluation points and the estimated cdf.

  quantileMclust returns a vector of quantiles.

References

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

Details

The cdf is evaluated at points given by the optional argument data. If not provided, a regular grid of length ngrid for the evaluation points is used.

The quantiles are computed using interpolating splines on an adaptive finer grid.

See Also

densityMclust, plot.densityMclust.

Examples

x <- c(rnorm(100), rnorm(100, 3, 2))
dens <- densityMclust(x)
summary(dens, parameters = TRUE)
cdf <- cdfMclust(dens)
str(cdf)
q <- quantileMclust(dens, p = c(0.01, 0.1, 0.5, 0.9, 0.99))
cbind(quantile = q, cdf = cdfMclust(dens, q)$y)
plot(cdf, type = "l", xlab = "x", ylab = "CDF")
points(q, cdfMclust(dens, q)$y, pch = 20, col = "red3")

par(mfrow = c(2,2))
dens.waiting <- densityMclust(faithful$waiting)
plot(dens.waiting)
plot(cdfMclust(dens.waiting), type = "l", 
     xlab = dens.waiting$varname, ylab = "CDF")
dens.eruptions <- densityMclust(faithful$eruptions)
plot(dens.eruptions)
plot(cdfMclust(dens.eruptions), type = "l", 
     xlab = dens.eruptions$varname, ylab = "CDF")
par(mfrow = c(1,1))