fit_disc_gamma {epitrix} | 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 |
w |
The centering of the interval used for discretisation; see
|
... |
Further arguments passed to |
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
See Also
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)
}