mitey (version 0.2.0)

wt_loglik: Calculate Weighted Negative Log-Likelihood for Gamma Distribution Parameters

Description

Computes the weighted negative log-likelihood for gamma distribution parameters in the M-step of the EM algorithm for serial interval estimation. This function is used as the objective function for numerical optimization when the serial interval distribution is assumed to follow a gamma distribution.

Usage

wt_loglik(par, dat, tau2)

Value

numeric; negative log-likelihood value for minimization. Returns a large penalty value (1e10) if parameters result in invalid gamma distribution parameters (non-positive shape/scale) or non-finite likelihood values

Arguments

par

numeric vector of length 2; parameters to optimize where par[1] is the mean and par[2] is the standard deviation of the serial interval distribution

dat

numeric vector; index case-to-case (ICC) intervals. Zero values are replaced with 0.00001 to avoid gamma distribution issues at zero

tau2

numeric vector; posterior probabilities (weights) that each observation belongs to the primary-secondary transmission component. These are typically derived from the E-step of the EM algorithm

Details

The function converts mean and standard deviation parameters to gamma distribution shape and scale parameters, then calculates the weighted log-likelihood based on the posterior probabilities from the E-step. Returns the negative log-likelihood for minimization by optim.

This function is used internally by si_estim when dist = "gamma". The gamma distribution is parameterized using shape (k) and scale (theta) parameters derived from the mean and standard deviation:

• Shape: \(k = \mu^2 / \sigma^2\)

• Scale: \(\theta = \sigma^2 / \mu\)

The weighted log-likelihood is calculated as: $$\sum_{i} \tau_{2,i} \log f(x_i | k, \theta)$$ where \(f(x_i | k, \theta)\) is the gamma probability density function and \(\tau_{2,i}\) are the weights from the E-step.

See Also

si_estim for the main serial interval estimation function, optim for the optimization routine that uses this function

Examples

# Example usage within optimization context
# Simulate some ICC interval data and weights
set.seed(123)
icc_intervals <- rgamma(50, shape = 2, scale = 3)  # True mean=6, sd=sqrt(18)
weights <- runif(50, 0.1, 1)
initial_params <- c(5, 4)
likelihood_value <- wt_loglik(initial_params, icc_intervals, weights)

# Example of parameter optimization
# \donttest{
optimized <- optim(
  par = initial_params,
  fn = wt_loglik,
  dat = icc_intervals,
  tau2 = weights,
  method = "BFGS"
)

cat("Optimized mean:", optimized$par[1], "\n")
cat("Optimized sd:", optimized$par[2], "\n")
# }