normmix: Density, Distribution, Quantiles and Random Number Generation...

In cylcop: Circular-Linear Copulas with Angular Symmetry for Movement Data

Density, Distribution, Quantiles and Random Number Generation for the mixed normal distribution

Description

The number of components in the mixed normal distribution is specified by the length of the parameter vectors. The quantiles are numerically obtained from the distribution function using monotone cubic splines.

Usage

rnormmix(n, mu, sigma, prop)

dnormmix(x, mu, sigma, prop)

pnormmix(q, mu, sigma, prop)

qnormmix(p, mu, sigma, prop)

Arguments

n	integer value, the number of random samples to be generated with rnormmix().
mu	numeric vector holding the means of the components.
sigma	numeric vector holding the standard deviations of the components.
prop	numeric vector, holding the mixing proportions of the components.
x	numeric vector giving the points where the density function is evaluated.
q	numeric vector giving the quantiles where the distribution function is evaluated.
p	numeric vector giving the probabilities where the quantile function is evaluated.

Value

• dnormmix() gives a vector of length length(x) containing the density at x.

• pnormmix() gives a vector of length length(q) containing the distribution function at the corresponding values of q.

• qnormmix() gives a vector of length length(p) containing the quantiles at the corresponding values of p.

• rnormmix() generates a vector of length n containing the random samples.

Examples

rnormmix(10, mu = c(0, 3, 7), sigma = c(2, 2, 4), prop = c(0.6, 0.3, 0.1))

dnormmix(c(0, 2, 1), mu = c(0, 3), sigma = c(2, 2), prop = c(0.6, 0.4))

prob <- pnormmix(c(0.1, 7), mu = c(0, 3, 7), sigma = c(2, 2, 4), prop = c(0.6, 0.3, 0.1))
prob
qnormmix(prob, mu = c(0, 3, 7), sigma = c(2, 2, 4), prop = c(0.6, 0.3, 0.1))

cylcop documentation built on Oct. 30, 2022, 1:05 a.m.