Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
Compute the cumulative density function (cdf) or quantiles from an estimated one-dimensional Gaussian mixture fitted using densityMclust.
cdfMclust(object, data, ngrid = 100, ...) quantileMclust(object, p, ...)
object |
a |
data |
a numeric vector of evaluation points. |
ngrid |
the number of points in a regular grid to be used as evaluation points if no |
p |
a numeric vector of probabilities. |
... |
further arguments passed to or from other methods. |
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 bisection linear search algorithm.
cdfMclust returns a list of x and y values providing, respectively, the evaluation points and the estimated cdf.
quantileMclust returns a vector of quantiles.
Luca Scrucca
x <- c(rnorm(100), rnorm(100, 3, 2))
dens <- densityMclust(x, plot = FALSE)
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$waiting)
plot(cdfMclust(dens.waiting), type = "l",
xlab = dens.waiting$varname, ylab = "CDF")
dens.eruptions <- densityMclust(faithful$eruptions)
plot(cdfMclust(dens.eruptions), type = "l",
xlab = dens.eruptions$varname, ylab = "CDF")
par(mfrow = c(1,1))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.