Probability distributions

Generates a nonparametric distribution.

Usage

LogNormal(meanlog, sdlog, mean, sd, ...)

Gamma(shape, rate, scale, mean, sd, ...)

Normal(mean, sd, ...)

Fixed(value, ...)

NonParametric(pmf, ...)

Arguments

meanlog, sdlog: mean and standard deviation of the distribution on the log scale with default values of 0 and 1 respectively.
mean, sd: mean and standard deviation of the distribution
...: arguments to define the limits of the distribution that will be passed to bound_dist()
shape, scale: shape and scale parameters. Must be positive, scale strictly.
rate: an alternative way to specify the scale.
value: Value of the fixed (delta) distribution
pmf: Probability mass of the given distribution; this is passed as a zero-indexed numeric vector (i.e. the fist entry represents the probability mass of zero). If not summing to one it will be normalised to sum to one internally.

Value

A dist_spec representing a distribution of the given specification.

Details

Probability distributions are ubiquitous in EpiNow2, usually representing epidemiological delays (e.g., the generation time for delays between becoming infecting and infecting others; or reporting delays)

They are generated using functions that have a name corresponding to the probability distribution that is being used. They generated dist_spec objects that are then passed to the models underlying EpiNow2. All parameters can be given either as fixed values (a numeric value) or as uncertain values (a dist_sepc). If given as uncertain values, currently only normally distributed parameters (generated using Normal()) are supported.

Each distribution has a representation in terms of "natural" parameters (the ones used in stan) but can sometimes also be specified using other parameters such as the mean or standard deviation of the distribution. If not given as natural parameters then these will be calculated from the given parameters. If they have uncertainty, this will be done by random sampling from the given uncertainty and converting resulting parameters to their natural representation.

Currently available distributions are lognormal, gamma, normal, fixed (delta) and nonparametric. The nonparametric is a special case where the probability mass function is given directly as a numeric vector.

Examples

LogNormal(mean = 4, sd = 1)
#> - lognormal distribution:
#>   meanlog:
#>     1.4
#>   sdlog:
#>     0.25
LogNormal(mean = 4, sd = 1, max = 10)
#> - lognormal distribution (max: 10):
#>   meanlog:
#>     1.4
#>   sdlog:
#>     0.25
LogNormal(mean = Normal(4, 1), sd = 1, max = 10)
#> Warning: ! Uncertain lognormal distribution specified in terms of parameters that are
#>   not the "natural" parameters of the distribution meanlog and sdlog.
#> ℹ Converting using a crude and very approximate method that is likely to
#>   produce biased results.
#> ℹ If possible it is preferable to specify the distribution directly in terms of
#>   the natural parameters.
#> - lognormal distribution (max: 10):
#>   meanlog:
#>     - normal distribution:
#>       mean:
#>         1.4
#>       sd:
#>         0.41
#>   sdlog:
#>     - normal distribution:
#>       mean:
#>         0.25
#>       sd:
#>         0.18
Gamma(mean = 4, sd = 1)
#> - gamma distribution:
#>   shape:
#>     16
#>   rate:
#>     4
Gamma(shape = 16, rate = 4)
#> - gamma distribution:
#>   shape:
#>     16
#>   rate:
#>     4
Gamma(shape = Normal(16, 2), rate = Normal(4, 1))
#> - gamma distribution:
#>   shape:
#>     - normal distribution:
#>       mean:
#>         16
#>       sd:
#>         2
#>   rate:
#>     - normal distribution:
#>       mean:
#>         4
#>       sd:
#>         1
Gamma(shape = Normal(16, 2), scale = Normal(1/4, 1))
#> Warning: ! Uncertain gamma distribution specified in terms of parameters that are not
#>   the "natural" parameters of the distribution shape and rate.
#> ℹ Converting using a crude and very approximate method that is likely to
#>   produce biased results.
#> ℹ If possible it is preferable to specify the distribution directly in terms of
#>   the natural parameters.
#> - gamma distribution:
#>   shape:
#>     - normal distribution:
#>       mean:
#>         16
#>       sd:
#>         5.8
#>   rate:
#>     - normal distribution:
#>       mean:
#>         4
#>       sd:
#>         2.9
Normal(mean = 4, sd = 1)
#> - normal distribution:
#>   mean:
#>     4
#>   sd:
#>     1
Normal(mean = 4, sd = 1, max = 10)
#> - normal distribution (max: 10):
#>   mean:
#>     4
#>   sd:
#>     1
Fixed(value = 3)
#> - fixed value:
#>   3
Fixed(value = 3.5)
#> - fixed value:
#>   3.5
NonParametric(c(0.1, 0.3, 0.2, 0.4))
#> - nonparametric distribution
#>   PMF: [0.1 0.3 0.2 0.4]
NonParametric(c(0.1, 0.3, 0.2, 0.1, 0.1))
#> - nonparametric distribution
#>   PMF: [0.12 0.37 0.25 0.12 0.12]