log_likelihood: Calculate log-likelihood

In serocalculator: Estimating Infection Rates from Serological Data

Calculate log-likelihood

Description

Calculates the log-likelihood of a set of cross-sectional antibody response data, for a given incidence rate (lambda) value.

Usage

log_likelihood(
  lambda,
  pop_data,
  curve_params,
  noise_params,
  antigen_isos = get_biomarker_levels(pop_data),
  verbose = FALSE,
  ...
)

Arguments

lambda	a numeric vector of incidence parameters, in events per person-year
pop_data	a data.frame() with cross-sectional serology data by antibody and age, and additional columns
curve_params	a data.frame() containing MCMC samples of parameters from the Bayesian posterior distribution of a longitudinal decay curve model. The parameter columns must be named: antigen_iso: a character() vector indicating antigen-isotype combinations iter: an integer() vector indicating MCMC sampling iterations y0: baseline antibody level at $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration of infection alpha: antibody decay rate (1/days for the current longitudinal parameter sets) r: shape factor of antibody decay
noise_params	a data.frame() (or tibble::tibble()) containing the following variables, specifying noise parameters for each antigen isotype: antigen_iso: antigen isotype whose noise parameters are being specified on each row nu: biological noise eps: measurement noise y.low: lower limit of detection for the current antigen isotype y.high: upper limit of detection for the current antigen isotype
antigen_isos	Character vector listing one or more antigen isotypes. Values must match pop_data.
verbose	logical: if TRUE, print verbose log information to console
...	additional arguments passed to other functions (not currently used).

Value

the log-likelihood of the data with the current parameter values

Examples

library(dplyr)
library(tibble)

# Load cross-sectional data
xs_data <-
  sees_pop_data_pk_100

# Load curve parameters and subset for the purposes of this example
curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

# Load noise params
cond <- tibble(
  antigen_iso = c("HlyE_IgG", "HlyE_IgA"),
  nu = c(0.5, 0.5), # Biologic noise (nu)
  eps = c(0, 0), # M noise (eps)
  y.low = c(1, 1), # low cutoff (llod)
  y.high = c(5e6, 5e6)
) # high cutoff (y.high)

# Calculate log-likelihood
ll_AG <- log_likelihood(
  pop_data = xs_data,
  curve_params = curve,
  noise_params = cond,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  lambda = 0.1
) %>% print()

serocalculator documentation built on April 3, 2025, 7:35 p.m.