# [−][src]Struct statrs::distribution::LogNormal

`pub struct LogNormal { /* fields omitted */ }`

Implements the Log-normal distribution

# Examples

```use statrs::distribution::{LogNormal, Continuous};
use statrs::statistics::Mean;
use statrs::prec;

let n = LogNormal::new(0.0, 1.0).unwrap();
assert_eq!(n.mean(), (0.5f64).exp());
assert!(prec::almost_eq(n.pdf(1.0), 0.3989422804014326779399, 1e-16));```

## Methods

### `impl LogNormal`[src]

#### `pub fn new(location: f64, scale: f64) -> Result<LogNormal>`[src]

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

### `impl Univariate<f64, f64> for LogNormal`[src]

#### `fn cdf(&self, x: f64) -> f64`[src]

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

### `impl Continuous<f64, f64> for LogNormal`[src]

#### `fn pdf(&self, x: f64) -> f64`[src]

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

#### `fn ln_pdf(&self, x: f64) -> f64`[src]

Calculates the log probability density function for the log-normal distribution at `x`

# Formula

`ln((1 / xσ * sqrt(2π)) * e^(-((ln(x) - μ)^2) / 2σ^2))`

where `μ` is the location and `σ` is the scale

### `impl Min<f64> for LogNormal`[src]

#### `fn min(&self) -> f64`[src]

Returns the minimum value in the domain of the log-normal distribution representable by a double precision float

# Formula

`0`

### `impl Max<f64> for LogNormal`[src]

#### `fn max(&self) -> f64`[src]

Returns the maximum value in the domain of the log-normal distribution representable by a double precision float

# Formula

`INF`

### `impl Mean<f64> for LogNormal`[src]

#### `fn mean(&self) -> f64`[src]

Returns the mean of the log-normal distribution

# Formula

`e^(μ + σ^2 / 2)`

where `μ` is the location and `σ` is the scale

### `impl Variance<f64> for LogNormal`[src]

#### `fn variance(&self) -> f64`[src]

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

#### `fn std_dev(&self) -> f64`[src]

Returns the standard deviation of the log-normal distribution

# Formula

`sqrt((e^(σ^2) - 1) * e^(2μ + σ^2))`

where `μ` is the location and `σ` is the scale

### `impl Entropy<f64> for LogNormal`[src]

#### `fn entropy(&self) -> f64`[src]

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

### `impl Skewness<f64> for LogNormal`[src]

#### `fn skewness(&self) -> f64`[src]

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

### `impl Median<f64> for LogNormal`[src]

#### `fn median(&self) -> f64`[src]

Returns the median of the log-normal distribution

# Formula

`e^μ`

where `μ` is the location

### `impl Mode<f64> for LogNormal`[src]

#### `fn mode(&self) -> f64`[src]

Returns the mode of the log-normal distribution

# Formula

`e^(μ - σ^2)`

where `μ` is the location and `σ` is the scale

### `impl Clone for LogNormal`[src]

#### `fn clone_from(&mut self, source: &Self)`1.0.0[src]

Performs copy-assignment from `source`. Read more

### `impl Distribution<f64> for LogNormal`[src]

#### `fn sample_iter<R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> where    R: Rng, `[src]

Create an iterator that generates random values of `T`, using `rng` as the source of randomness. Read more

## Blanket Implementations

### `impl<T> ToOwned for T where    T: Clone, `[src]

#### `type Owned = T`

The resulting type after obtaining ownership.

### `impl<T, U> TryFrom<U> for T where    U: Into<T>, `[src]

#### `type Error = Infallible`

The type returned in the event of a conversion error.

### `impl<T, U> TryInto<U> for T where    U: TryFrom<T>, `[src]

#### `type Error = <U as TryFrom<T>>::Error`

The type returned in the event of a conversion error.