rmixture {actuar} | R Documentation |
Generate random variates from a discrete mixture of distributions.
rmixture(n, probs, models, shuffle = TRUE)
n |
number of random variates to generate. If |
probs |
numeric non-negative vector specifying the probability
for each model; is internally normalized to sum 1. Infinite
and missing values are not allowed. Values are recycled as necessary
to match the length of |
models |
vector of expressions specifying the simulation models
with the number of variates omitted (see details). Models are
recycled as necessary to match the length of |
shuffle |
logical; should the random variates from the distributions be shuffled? |
rmixture
generates variates from a discrete mixture, that is
random variable with a probability density function of the form
f(x) = p_1 f_1(x) + ... + p_n f_n(x),
where f_1, …, f_n are densities and p_1 + … + p_n = 1.
The values in probs
will be internally normalized to be
used as probabilities p_1 + … + p_n.
The specification of simulation models uses the syntax of
rcomphierarc
. Models f_1, …, f_n are expressed in a
semi-symbolic fashion using an object of mode
expression
where each element is a complete call
to a random number generation function, with the number of variates
omitted.
The argument of the random number generation functions for the number
of variates to simulate must be named n
.
If shuffle
is FALSE
, the output vector contains all the
random variates from the first model, then all the random variates
from the second model, and so on. If the order of the variates is
irrelevant, this cuts the time to generate the variates roughly in
half.
A vector of random variates from the mixture with density f(x).
Vincent Goulet vincent.goulet@act.ulaval.ca
rcompound
to simulate from compound models.
rcomphierarc
to simulate from compound hierarchical models.
## Mixture of two exponentials (with means 1/3 and 1/7) with equal ## probabilities. rmixture(10, 0.5, expression(rexp(3), rexp(7))) rmixture(10, 42, expression(rexp(3), rexp(7))) # same ## Mixture of two lognormals with different probabilities. rmixture(10, probs = c(0.55, 0.45), models = expression(rlnorm(3.6, 0.6), rlnorm(4.6, 0.3)))