# getLognormMoments: Compute summary statistics of a log-normal distribution In lognorm: Functions for the Lognormal Distribution

## Description

Compute summary statistics of a log-normal distribution

## Usage

 ```1 2 3 4 5``` ```getLognormMoments(mu, sigma, m = exp(mu + sigma2/2) - shift, shift = 0) getLognormMedian(mu, sigma, shift = 0) getLognormMode(mu, sigma, shift = 0) ```

## Arguments

 `mu` numeric vector: location parameter `sigma` numeric vector: scale parameter `m` mean at original scale, may override default based on mu `shift` shift for the shifted lognormal distribution

## Value

for `getLognormMoments` a numeric matrix with columns `mean` (expected value at original scale) , `var` (variance at original scale) , and `cv` (coefficient of variation: sqrt(var)/mean). For the other functions a numeric vector of the required summary.

## Functions

• `getLognormMoments`: get the expected value, variance, and coefficient of variation

• `getLognormMedian`: get the median

• `getLognormMode`: get the mode

## References

```Limpert E, Stahel W & Abbt M (2001) Log-normal Distributions across the Sciences: Keys and Clues. Oxford University Press (OUP) 51, 341, 10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2```

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ``` # start by estimating lognormal parameters from moments .mean <- 1 .var <- c(1.3,2)^2 parms <- getParmsLognormForMoments(.mean, .var) # # computed moments must equal previous ones (ans <- getLognormMoments(parms[,"mu"], parms[,"sigma"])) cbind(.var, ans[,"var"]) # getLognormMedian(mu = log(1), sigma = log(2)) getLognormMode(mu = log(1), sigma = c(log(1.2),log(2))) ```