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Graph log-likelihood of data

Source: R/graph.loglik.R
graph_loglik.Rd

Graph log-likelihood of data

Usage

graph_loglik(
  pop_data,
  curve_params,
  noise_params,
  antigen_isos = pop_data %>% get_biomarker_levels(),
  x = 10^seq(-3, 0, by = 0.1),
  highlight_points = NULL,
  highlight_point_names = "highlight_points",
  log_x = FALSE,
  previous_plot = NULL,
  curve_label = paste(antigen_isos, collapse = " + "),
  ...
)

Arguments

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.

x

sequence of lambda values to graph

highlight_points

a possible highlighted value

highlight_point_names

labels for highlighted points

log_x

should the x-axis be on a logarithmic scale (TRUE) or linear scale (FALSE, default)?

previous_plot

if not NULL, the current data is added to the existing graph

curve_label

if not NULL, add a label for the curve

...

Arguments passed on to log_likelihood

verbose

logical: if TRUE, print verbose log information to console

Value

a ggplot2::ggplot()

Examples


library(dplyr)
library(tibble)

# Load cross-sectional data
xs_data <- load_pop_data("https://osf.io/download//n6cp3/")


# Load curve parameters and subset for the purposes of this example
dmcmc <- load_curve_params("https://osf.io/download/rtw5k/") %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
  slice(1:100, .by = antigen_iso)

# Load noise parameters
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)

# Graph the log likelihood
lik_HlyE_IgA <- # nolint: object_name_linter
  graph_loglik(
    pop_data = xs_data,
    curve_params = dmcmc,
    noise_params = cond,
    antigen_isos = "HlyE_IgA",
    log_x = TRUE
)

lik_HlyE_IgA # nolint: object_name_linter