EpiNow2 (version 1.3.4)

dist_skel: Distribution Skeleton

Description

This function acts as a skeleton for a truncated distribution defined by model type, maximum value and model parameters. It is designed to be used with the output from get_dist.

Usage

dist_skel(n, dist = FALSE, cum = TRUE, model, params, max_value = 120)

Value

A vector of samples or a probability distribution.

Arguments

n

Numeric vector, number of samples to take (or days for the probability density).

dist

Logical, defaults to FALSE. Should the probability density be returned rather than a number of samples.

cum

Logical, defaults to TRUE. If dist = TRUE should the returned distribution be cumulative.

model

Character string, defining the model to be used. Supported options are exponential ("exp"), gamma ("gamma"), and log normal ("lognorm")

params

A list of parameters values (by name) required for each model. For the exponential model this is a rate parameter and for the gamma model this is alpha and beta.

max_value

Numeric, the maximum value to allow. Defaults to 120. Samples outside of this range are resampled.

Author

Sam Abbott

Examples

## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))

# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))

# probability density
dist_skel(1:10,
  model = "exp", dist = TRUE,
  cum = FALSE, params = list(rate = 1)
)

## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(alpha = 1, beta = 2))

# cumulative prob density
dist_skel(0:10,
  model = "gamma", dist = TRUE,
  params = list(alpha = 1, beta = 2)
)

# probability density
dist_skel(0:10,
  model = "gamma", dist = TRUE,
  cum = FALSE, params = list(alpha = 2, beta = 2)
)

## Log normal model
# sample
dist_skel(10, model = "lognorm", params = list(mean = log(5), sd = log(2)))

# cumulative prob density
dist_skel(0:10,
  model = "lognorm", dist = TRUE,
  params = list(mean = log(5), sd = log(2))
)

# probability density
dist_skel(0:10,
  model = "lognorm", dist = TRUE, cum = FALSE,
  params = list(mean = log(5), sd = log(2))
)