Calculate negative log-likelihood (deviance) for one antigen:isotype pair and incidence rate

Usage

f_dev0(lambda, csdata, lnpars, cond)

Arguments

lambda: numeric() incidence parameter, in events per person-year
csdata: cross-sectional sample data containing variables value and age
lnpars: longitudinal antibody decay model parameters alpha, y1, and d
cond: measurement noise parameters nu, eps, y.low, and y.high

Value

a numeric() negative log-likelihood, corresponding to input lambda

Details

interface with C lib serocalc.so

Examples

library(dplyr)
library(tibble)

# load in longitudinal parameters
curve_params = load_curve_params("https://osf.io/download/rtw5k")
xs_data <- "https://osf.io/download//n6cp3/" %>%
load_pop_data()

#Load noise params
noise_params <- 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)

cur_antibody = "HlyE_IgA"

cur_data <-
  xs_data %>%
  dplyr::filter(
   .data$catchment == "dhaka",
   .data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_curve_params <-
  curve_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_noise_params <-
  noise_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody)

if(!is.element('d', names(cur_curve_params)))
{
  cur_curve_params <-
    cur_curve_params %>%
    dplyr::mutate(
      alpha = .data$alpha * 365.25,
      d = .data$r - 1)
}

lambda = 0.1
f_dev0(
    lambda = lambda,
    csdata = cur_data,
    lnpars = cur_curve_params,
    cond = cur_noise_params
  )
#> [1] 390.8284