Struct statrs::distribution::LogNormal
source · [−]pub struct LogNormal { /* private fields */ }Expand description
Implements the Log-normal distribution
Examples
use statrs::distribution::{LogNormal, Continuous};
use statrs::statistics::Distribution;
use statrs::prec;
let n = LogNormal::new(0.0, 1.0).unwrap();
assert_eq!(n.mean().unwrap(), (0.5f64).exp());
assert!(prec::almost_eq(n.pdf(1.0), 0.3989422804014326779399, 1e-16));Implementations
Constructs a new log-normal distribution with a location of location
and a scale of scale
Errors
Returns an error if location or scale are NaN.
Returns an error if scale <= 0.0
Examples
use statrs::distribution::LogNormal;
let mut result = LogNormal::new(0.0, 1.0);
assert!(result.is_ok());
result = LogNormal::new(0.0, 0.0);
assert!(result.is_err());Trait Implementations
Calculates the probability density function for the log-normal
distribution at x
Formula
(1 / xσ * sqrt(2π)) * e^(-((ln(x) - μ)^2) / 2σ^2)where μ is the location and σ is the scale
Calculates the cumulative distribution function for the log-normal
distribution
at x
Formula
(1 / 2) + (1 / 2) * erf((ln(x) - μ) / sqrt(2) * σ)where μ is the location, σ is the scale, and erf is the
error function
Due to issues with rounding and floating-point accuracy the default
implementation may be ill-behaved.
Specialized inverse cdfs should be used whenever possible.
Performs a binary search on the domain of cdf to obtain an approximation
of F^-1(p) := inf { x | F(x) >= p }. Needless to say, performance may
may be lacking. Read more
Generate a random value of T, using rng as the source of randomness.
Create an iterator that generates random values of T, using rng as
the source of randomness. Read more
Returns the mean of the log-normal distribution
Formula
e^(μ + σ^2 / 2)where μ is the location and σ is the scale
Returns the variance of the log-normal distribution
Formula
(e^(σ^2) - 1) * e^(2μ + σ^2)where μ is the location and σ is the scale
Returns the entropy of the log-normal distribution
Formula
ln(σe^(μ + 1 / 2) * sqrt(2π))where μ is the location and σ is the scale
Returns the skewness of the log-normal distribution
Formula
(e^(σ^2) + 2) * sqrt(e^(σ^2) - 1)where μ is the location and σ is the scale
Auto Trait Implementations
impl RefUnwindSafe for LogNormal
impl UnwindSafe for LogNormal
Blanket Implementations
Mutably borrows from an owned value. Read more
The inverse inclusion map: attempts to construct self from the equivalent element of its
superset. Read more
Checks if self is actually part of its subset T (and can be converted to it).
Use with care! Same as self.to_subset but without any property checks. Always succeeds.
The inclusion map: converts self to the equivalent element of its superset.