![[Stable]](figures/lifecycle-stable.svg)
Density, distribution function, quantile function, and random generation for
a univariate Gaussian mixture.
dmixnorm(x, mean = rep(0, 2), sd = rep(1, 2), alpha = rep(1/2, 2), log = FALSE)
pmixnorm(
q,
mean = rep(0, 2),
sd = rep(1, 2),
alpha = rep(1/2, 2),
lower.tail = TRUE,
log.p = FALSE
)
qmixnorm(
p,
mean = rep(0, 2),
sd = rep(1, 2),
alpha = rep(1/2, 2),
lower.tail = TRUE,
log.p = FALSE
)
rmixnorm(n, mean = rep(0, 3), sd = rep(1, 3), alpha = rep(1/3, 3))Arguments
- x
Numeric vector of quantiles.
- mean
Numeric vector of component means.
- sd
Numeric vector of component standard deviations.
- alpha
Numeric vector of component probabilities.
- log
Logical. If
TRUE,dmixnorm()returns log-densities.- q
Numeric vector of quantiles.
- lower.tail
Logical. If
TRUE, probabilities are \(P[X \le x]\); otherwise, \(P[X > x]\).- log.p
Logical. If
TRUE, probabilities are supplied or returned on the log scale.- p
Numeric vector of probabilities.
- n
Number of observations.
Value
dmixnorm()returns a numeric vector of density values.pmixnorm()returns a numeric vector of probabilities.qmixnorm()returns a numeric vector of quantiles.rmixnorm()returns a tibble with simulated component values, component indicators, and the combined random draw.
Note
Version 1.0.1.
See also
Other distributions:
desscher(),
desscherMixture(),
dgumbel(),
dinvgumbel(),
dkumaraswamy(),
dsnorm(),
dsolarGHI(),
dsolarK(),
dsolarX(),
dsugeno(),
dtnorm()
Examples
mean <- c(-1, 1)
sd <- c(0.5, 1)
alpha <- c(0.4, 0.6)
dmixnorm(c(-1, 0, 1), mean, sd, alpha)
#> [1] 0.3515484 0.1883752 0.2394724
pmixnorm(c(-1, 0, 1), mean, sd, alpha)
#> [1] 0.2136501 0.4860931 0.6999873
qmixnorm(c(0.25, 0.75), mean, sd, alpha)
#> [1] -0.8969722 1.2104368
set.seed(1)
rmixnorm(3, mean, sd, alpha)
#> # A tibble: 3 × 6
#> t X1 X2 B1 B2 X
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 2.33 0 1 2.33
#> 2 2 0 2.27 0 1 2.27
#> 3 3 0 1.41 0 1 1.41