mixtdistr {usefr}  R Documentation 
Density, distribution function, quantile function and random generation for mixture of distributions
dmixtdistr(x, phi, arg, log = FALSE, lower.tail = TRUE, log.p = FALSE) pmixtdistr(q, phi, arg, lower.tail = TRUE, log.p = FALSE) qmixtdistr(p, interval = c(0, 1000), phi, arg, lower.tail = TRUE, log.p = FALSE) rmixtdistr(n, phi, arg)
x, 
q vector of quantiles. 
phi 
Numerical vector with mixture proportions, where sum(phi) = 1. 
arg 
A list of named vectors with the corresponding named distribution
parameters values. The names of the vector of parameters and the
parameter names must correspond to defined functions. For example, if
one of the involved distributions is the gamma density (see

log, log.p 
logical; if TRUE, probabilities p are given as log(p). 
lower.tail 
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x]. 
p 
vector of probabilities. 
n 
number of observations. If length(n) > 1, the length is taken to be the number required. 
Robersy Sanchez (https://genomaths.com).
fitMixDist
, mcgoftest
(for goodnessof fit), and for additional examples:
https://genomaths.com/stats/samplingfromamixtureofdistributions/
set.seed(123) # set a seed for random generation # A mixture of three distributions phi = c(5/10, 3/10, 2/10) # Mixture proportions # Named vector of the corresponding distribution function parameters # must be provided args < list(gamma = c(shape = 20, scale = 1/10), weibull = c(shape = 4, scale = 0.8), lnorm = c(meanlog = 1.2, sdlog = 0.08)) # Sampling from the specified mixture distribution x < rmixtdistr(n = 1e5, phi = phi , arg = args) # The graphics for the simulated dataset and the corresponding theoretical # mixture distribution hist(x, 100, freq = FALSE) x1 < seq(0, 10, by = 0.001) lines(x1, dmixtdistr(x1, phi = phi, arg = args), col = "red")