Skip to contents

Calculate log-likelihood for Normally-distributed continuous data

Source: R/calc_log_likelihood_distributions.R
calc_log_likelihood_normal.Rd

Computes the total log-likelihood for continuous data under the Normal distribution. Also performs the Shapiro-Wilk test to check normality of residuals. Each observation can be weighted via weights.

Usage

calc_log_likelihood_normal(observed, estimated, weights = NULL, verbose = TRUE)

Arguments

observed

Numeric vector of observed continuous values.

estimated

Numeric vector of model-predicted means.

weights

Optional numeric vector of non-negative weights, same length as observed. Default is NULL, which sets all weights to 1.

verbose

Logical; if TRUE, prints estimated sigma, Shapiro-Wilk p-value, and log-likelihood.

Value

A single numeric value representing the total log-likelihood.

Details

Weighted log-likelihood is computed across all observations, and the residuals are tested for normality via the Shapiro-Wilk test:

  • If p < 0.05, a warning is issued indicating non-normal residuals.

  • Otherwise, if verbose = TRUE, a message is printed showing the p-value.

Examples

ll <- calc_log_likelihood_normal(c(1.2, 2.8, 3.1), c(1.0, 3.0, 3.2))
#> Shapiro-Wilk p = 0.4633: residuals are consistent with normality.
#> Estimated σ = 0.2082
#> Normal log-likelihood: 0.91
print(ll)
#> [1] 0.9129725