Log-Likelihood Functions for Probability Models

Compute the log-likelihood for stationary and nonstationary probability models.

For NS-FFA: To compute the log-likelihood for a nonstationary probability model, include the observation years (ns_years) and the nonstationary model structure (ns_structure).

Usage

utils_log_likelihood(
  data,
  distribution,
  params,
  ns_years = NULL,
  ns_structure = NULL
)

Arguments

data

Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.

distribution

A three-character code indicating the distribution family. Must be "GUM", "NOR", "LNO", "GEV", "GLO", "GNO", "PE3", "LP3", or "WEI".

params

Numeric vector of distribution parameters, in the order (location, scale, shape). The length must be between 2 and 5, depending on the specified distribution and structure.

ns_years

For NS-FFA only: Numeric vector of observation years corresponding to data. Must be the same length as data and strictly increasing.

ns_structure

For NS-FFA only: Named list indicating which distribution parameters are modeled as nonstationary. Must contain two logical scalars:

• location: If TRUE, the location parameter has a linear temporal trend.

• scale: If TRUE, the scale parameter has a linear temporal trend.

Value

Numeric scalar. The log-likelihood value.

See also

utils_generalized_likelihood()

Examples

data <- rnorm(n = 100, mean = 100, sd = 10)
params <- c(100, 1, 10)
ns_years <- seq(from = 1901, to = 2000)
ns_structure <- list(location = TRUE, scale = FALSE)

# Compute the log-likelihood
utils_log_likelihood(data, "NOR", params, ns_years, ns_structure)
#> [1] -384.815