# fit_disc_gamma: Fit discretised distributions using ML In epitrix: Small Helpers and Tricks for Epidemics Analysis

 fit_disc_gamma R Documentation

## Fit discretised distributions using ML

### Description

These functions performs maximum-likelihood (ML) fitting of a discretised distribution. This is typically useful for describing delays between epidemiological events, such as incubation period (infection to onset) or serial intervals (primary to secondary onsets). The function `optim` is used internally for fitting.

### Usage

```fit_disc_gamma(x, mu_ini = NULL, cv_ini = NULL, interval = 1, w = 0, ...)
```

### Arguments

 `x` A vector of numeric data to fit; NAs will be removed with a warning. `mu_ini` The initial value for the mean 'mu', defaulting to the empirically calculated value. `cv_ini` The initial value for the coefficient of variation 'cv', defaulting to the empirically calculated value. `interval` The interval used for discretisation; see `distcrete`. `w` The centering of the interval used for discretisation; see `distcrete`. `...` Further arguments passed to `optim`.

### Value

The function returns a list with human-readable parametrisation of the discretised Gamma distibution (mean, sd, cv), convergence indicators, and the discretised Gamma distribution itself as a `distcrete` object (from the `distcrete` package).

### Author(s)

Thibaut Jombart thibautjombart@gmail.com

Charlie Whittaker charles.whittaker16@imperial.com

The `distcrete` package for discretising distributions, and `optim` for details on available optimisation procedures.

### Examples

```
## generate data

mu <- 15.3 # days
sigma <- 9.3 # days
cv <- sigma / mu
cv
param <- gamma_mucv2shapescale(mu, cv)

if (require(distcrete)) {
w <- distcrete("gamma", interval = 1,
shape = param\$shape,
scale = param\$scale, w = 0)

x <- w\$r(100)
x

fit_disc_gamma(x)
}

```

epitrix documentation built on Jan. 14, 2023, 1:16 a.m.