epichains (version 0.1.1)

likelihood: Estimate the log-likelihood/likelihood for observed branching processes

Description

Estimate the log-likelihood/likelihood for observed branching processes

Usage

likelihood(
  chains,
  statistic = c("size", "length"),
  offspring_dist,
  nsim_obs,
  obs_prob = 1,
  stat_threshold = Inf,
  log = TRUE,
  exclude = NULL,
  individual = FALSE,
  ...
)

Value

If log = TRUE

• A joint log-likelihood (sum of individual log-likelihoods), if individual == FALSE (default) and obs_prob == 1 (default), or

• A list of individual log-likelihoods, if individual == TRUE and obs_prob == 1 (default), or

• A list of individual log-likelihoods (same length as nsim_obs), if individual == TRUE and 0 <= obs_prob < 1, or

• A vector of joint log-likelihoods (same length as nsim_obs), if individual == FALSE and 0 <= obs_prob < 1 (imperfect observation).

If log = FALSE, the same structure of outputs as above are returned, except that likelihoods, instead of log-likelihoods, are calculated in all cases. Moreover, the joint likelihoods are the product, instead of the sum, of the individual likelihoods.

Arguments

chains

Vector of chain summaries (sizes/lengths). Can be a <numeric> vector or an object of class <epichains> or <epichains_summary> (obtained from simulate_chains() or simulate_chain_stats()). See examples below.

statistic

The chain statistic to track as the stopping criteria for each chain being simulated when stat_threshold is not Inf; A <string>. It can be one of:

• "size": the total number of cases produced by a chain before it goes extinct.

• "length": the total number of generations reached by a chain before it goes extinct.

offspring_dist

Offspring distribution: a <function> like the ones provided by R to generate random numbers from given distributions (e.g., rpois for Poisson). More specifically, the function needs to accept at least one argument, n, which is the number of random numbers to generate. It can accept further arguments, which will be passed on to the random number generating functions. Examples that can be provided here are rpois for Poisson distributed offspring, rnbinom for negative binomial offspring, or custom functions.

nsim_obs

Number of simulations to be used to approximate the log-likelihood/likelihood if obs_prob < 1 (imperfect observation). If obs_prob == 1, this argument is ignored.

obs_prob

Observation probability. A number (probability) between 0 and 1. A value greater than 0 but less 1 implies imperfect observation and simulations will be used to approximate the (log)likelihood. This will also require specifying nsim_obs. In the simulation, the observation process is assumed to be constant.

stat_threshold

A stopping criterion for individual chain simulations; a positive number coercible to integer. When any chain's cumulative statistic reaches or surpasses stat_threshold, that chain ends. It also serves as a censoring limit so that results above the specified value, are set to Inf. Defaults to Inf. NOTE: For objects of class <epichains> or <epichains_summary>, the stat_threshold used in the simulation is extracted and used here so if this argument is specified, it is ignored and a warning is thrown.

log

Should the log-likelihoods be transformed to likelihoods? Logical. Defaults to TRUE.

exclude

A vector of indices of the sizes/lengths to exclude from the log-likelihood calculation.

individual

Logical; If TRUE, a vector of individual log-likelihood/likelihood contributions will be returned rather than the sum/product.

...

any parameters to pass to simulate_chain_stats

Author

Sebastian Funk, James M. Azam

Examples

# example of observed chain sizes
set.seed(121)
# randomly generate 20 chains of size 1 to 10
chain_sizes <- sample(1:10, 20, replace = TRUE)
likelihood(
  chains = chain_sizes,
  statistic = "size",
  offspring_dist = rpois,
  nsim_obs = 100,
  lambda = 0.5
)
# Example using an  object
set.seed(32)
epichains_obj_eg <- simulate_chains(
  n_chains = 10,
  pop = 100,
  percent_immune = 0,
  statistic = "size",
  offspring_dist = rnbinom,
  stat_threshold = 10,
  generation_time = function(n) rep(3, n),
  mu = 2,
  size = 0.2
)

epichains_obj_eg_lik <- likelihood(
  chains = epichains_obj_eg,
  statistic = "size",
  offspring_dist = rnbinom,
  mu = 2,
  size = 0.2,
  stat_threshold = 10
)
epichains_obj_eg_lik

# Example using a  object
set.seed(32)
epichains_summary_eg <- simulate_chain_stats(
  n_chains = 10,
  pop = 100,
  percent_immune = 0,
  statistic = "size",
  offspring_dist = rnbinom,
  stat_threshold = 10,
  mu = 2,
  size = 0.2
)
epichains_summary_eg_lik <- likelihood(
  chains = epichains_summary_eg,
  statistic = "size",
  offspring_dist = rnbinom,
  mu = 2,
  size = 0.2
)
epichains_summary_eg_lik