Struct statrs::distribution::NegativeBinomial
source · [−]pub struct NegativeBinomial { /* private fields */ }
Expand description
Implements the negative binomial distribution.
Please note carefully the meaning of the parameters. As noted in the wikipedia article, there are several different commonly used conventions for the parameters of the negative binomial distribution.
The negative binomial distribution is a discrete distribution with two
parameters, r
and p
. When r
is an integer, the negative binomial
distribution can be interpreted as the distribution of the number of
failures in a sequence of Bernoulli trials that continue until r
successes occur. p
is the probability of success in a single Bernoulli
trial.
NegativeBinomial
accepts non-integer values for r
. This is a
generalization of the more common case where r
is an integer.
Examples
use statrs::distribution::{NegativeBinomial, Discrete};
use statrs::statistics::DiscreteDistribution;
use statrs::prec::almost_eq;
let r = NegativeBinomial::new(4.0, 0.5).unwrap();
assert_eq!(r.mean().unwrap(), 4.0);
assert!(almost_eq(r.pmf(0), 0.0625, 1e-8));
assert!(almost_eq(r.pmf(3), 0.15625, 1e-8));
Implementations
sourceimpl NegativeBinomial
impl NegativeBinomial
sourcepub fn new(r: f64, p: f64) -> Result<NegativeBinomial>
pub fn new(r: f64, p: f64) -> Result<NegativeBinomial>
Constructs a new negative binomial distribution with parameters r
and p
. When r
is an integer, the negative binomial distribution
can be interpreted as the distribution of the number of failures in
a sequence of Bernoulli trials that continue until r
successes occur.
p
is the probability of success in a single Bernoulli trial.
Errors
Returns an error if p
is NaN
, less than 0.0
,
greater than 1.0
, or if r
is NaN
or less than 0
Examples
use statrs::distribution::NegativeBinomial;
let mut result = NegativeBinomial::new(4.0, 0.5);
assert!(result.is_ok());
result = NegativeBinomial::new(-0.5, 5.0);
assert!(result.is_err());
Trait Implementations
sourceimpl Clone for NegativeBinomial
impl Clone for NegativeBinomial
sourcefn clone(&self) -> NegativeBinomial
fn clone(&self) -> NegativeBinomial
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for NegativeBinomial
impl Debug for NegativeBinomial
sourceimpl Discrete<u64, f64> for NegativeBinomial
impl Discrete<u64, f64> for NegativeBinomial
sourcefn pmf(&self, x: u64) -> f64
fn pmf(&self, x: u64) -> f64
Calculates the probability mass function for the negative binomial
distribution at x
.
Formula
When r
is an integer, the formula is:
(x + r - 1 choose x) * (1 - p)^x * p^r
The general formula for real r
is:
Γ(r + x)/(Γ(r) * Γ(x + 1)) * (1 - p)^x * p^r
where Γ(x) is the Gamma function.
sourcefn ln_pmf(&self, x: u64) -> f64
fn ln_pmf(&self, x: u64) -> f64
Calculates the log probability mass function for the negative binomial
distribution at x
.
Formula
When r
is an integer, the formula is:
ln((x + r - 1 choose x) * (1 - p)^x * p^r)
The general formula for real r
is:
ln(Γ(r + x)/(Γ(r) * Γ(x + 1)) * (1 - p)^x * p^r)
where Γ(x) is the Gamma function.
sourceimpl DiscreteCDF<u64, f64> for NegativeBinomial
impl DiscreteCDF<u64, f64> for NegativeBinomial
sourcefn cdf(&self, x: u64) -> f64
fn cdf(&self, x: u64) -> f64
Calculates the cumulative distribution function for the
negative binomial distribution at x
.
Formula
I_(p)(r, x+1)
where I_(x)(a, b)
is the regularized incomplete beta function.
sourcefn sf(&self, x: u64) -> f64
fn sf(&self, x: u64) -> f64
Calculates the survival function for the
negative binomial distribution at x
Note that due to extending the distribution to the reals
(allowing positive real values for r
), while still technically
a discrete distribution the CDF behaves more like that of a
continuous distribution rather than a discrete distribution
(i.e. a smooth graph rather than a step-ladder)
Formula
I_(1-p)(x+1, r)
where I_(x)(a, b)
is the regularized incomplete beta function
sourcefn inverse_cdf(&self, p: T) -> K
fn inverse_cdf(&self, p: T) -> K
Due to issues with rounding and floating-point accuracy the default implementation may be ill-behaved Specialized inverse cdfs should be used whenever possible. Read more
sourceimpl DiscreteDistribution<f64> for NegativeBinomial
impl DiscreteDistribution<f64> for NegativeBinomial
sourceimpl Distribution<u64> for NegativeBinomial
impl Distribution<u64> for NegativeBinomial
sourcefn sample<R: Rng + ?Sized>(&self, r: &mut R) -> u64
fn sample<R: Rng + ?Sized>(&self, r: &mut R) -> u64
Generate a random value of T
, using rng
as the source of randomness.
sourcefn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
R: Rng,
fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
R: Rng,
Create an iterator that generates random values of T
, using rng
as
the source of randomness. Read more
sourceimpl Max<u64> for NegativeBinomial
impl Max<u64> for NegativeBinomial
sourceimpl Min<u64> for NegativeBinomial
impl Min<u64> for NegativeBinomial
sourceimpl Mode<Option<f64>> for NegativeBinomial
impl Mode<Option<f64>> for NegativeBinomial
sourceimpl PartialEq<NegativeBinomial> for NegativeBinomial
impl PartialEq<NegativeBinomial> for NegativeBinomial
sourcefn eq(&self, other: &NegativeBinomial) -> bool
fn eq(&self, other: &NegativeBinomial) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
impl Copy for NegativeBinomial
impl StructuralPartialEq for NegativeBinomial
Auto Trait Implementations
impl RefUnwindSafe for NegativeBinomial
impl Send for NegativeBinomial
impl Sync for NegativeBinomial
impl Unpin for NegativeBinomial
impl UnwindSafe for NegativeBinomial
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if self
is actually part of its subset T
(and can be converted to it).
fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.