mixdiff | R Documentation |
Density, cumulative distribution function, quantile function and random number generation for the difference of two mixture distributions.
dmixdiff(mix1, mix2, x)
pmixdiff(mix1, mix2, q, lower.tail = TRUE)
qmixdiff(mix1, mix2, p, lower.tail = TRUE)
rmixdiff(mix1, mix2, n)
mix1 |
first mixture density |
mix2 |
second mixture density |
x |
vector of values for which density values are computed |
q |
vector of quantiles for which cumulative probabilities are computed |
lower.tail |
logical; if |
p |
vector of cumulative probabilities for which quantiles are computed |
n |
size of random sample |
If x_1 \sim f_1(x_1)
and x_2 \sim
f_2(x_2)
, the density of the difference d
\equiv x_1 - x_2
is given by
f_d(d) = \int f_1(u) \, f_2(u - d) \, du.
The cumulative distribution function equates to
F_d(d) = \int f_1(u) \, (1-F_2(u-d)) \, du.
Both integrals are performed over the full support of the
densities and use the numerical integration function
integrate
.
Respective density, quantile, cumulative density or random numbers.
# 1. Difference between two beta distributions, i.e. Pr( mix1 - mix2 > 0)
mix1 <- mixbeta(c(1, 11, 4))
mix2 <- mixbeta(c(1, 8, 7))
pmixdiff(mix1, mix2, 0, FALSE)
# Interval probability, i.e. Pr( 0.3 > mix1 - mix2 > 0)
pmixdiff(mix1, mix2, 0.3) - pmixdiff(mix1, mix2, 0)
# 2. two distributions, one of them a mixture
m1 <- mixbeta( c(1,30,50))
m2 <- mixbeta( c(0.75,20,50),c(0.25,1,1))
# random sample of difference
set.seed(23434)
rM <- rmixdiff(m1, m2, 1E4)
# histogram of random numbers and exact density
hist(rM,prob=TRUE,new=TRUE,nclass=40)
curve(dmixdiff(m1,m2,x), add=TRUE, n=51)
# threshold probabilities for difference, at 0 and 0.2
pmixdiff(m1, m2, 0)
mean(rM<0)
pmixdiff(m1,m2,0.2)
mean(rM<0.2)
# median of difference
mdn <- qmixdiff(m1, m2, 0.5)
mean(rM<mdn)
# 95%-interval
qmixdiff(m1, m2, c(0.025,0.975))
quantile(rM, c(0.025,0.975))
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