summary.seroincidence.by {serocalculator}

Summary Method for "seroincidence.by" Objects

Description

Calculate seroincidence from output of the seroincidence calculator est.incidence.by().

Usage

## S3 method for class 'seroincidence.by'
summary(
  object,
  confidence_level = 0.95,
  showDeviance = TRUE,
  showConvergence = TRUE,
  ...
)

Arguments

object

A dataframe containing output of function est.incidence.by().

confidence_level

desired confidence interval coverage probability

showDeviance

Logical flag (FALSE/TRUE) for reporting deviance (-2*log(likelihood) at estimated seroincidence. Default = TRUE.

showConvergence

Logical flag (FALSE/TRUE) for reporting convergence (see help for optim() for details). Default = FALSE.

...

Additional arguments affecting the summary produced.

Value

A summary.seroincidence.by object, which is a tibble::tibble, with the following columns:

• incidence.rate maximum likelihood estimate of lambda (seroincidence)

• CI.lwr lower confidence bound for lambda

• CI.upr upper confidence bound for lambda

• Deviance (included if showDeviance = TRUE) Negative log likelihood (NLL) at estimated (maximum likelihood) lambda)

  • nlm.convergence.code (included if showConvergence = TRUE) Convergence information returned by stats::nlm() The object also has the following metadata (accessible through base::attr()):

• antigen_isos Character vector with names of input antigen isotypes used in est.incidence.by()

• Strata Character with names of strata used in est.incidence.by()

Examples

library(dplyr)

xs_data <- load_pop_data("https://osf.io/download//n6cp3/")%>%
  clean_pop_data()

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

noise <- load_noise_params("https://osf.io/download//hqy4v/")
# estimate seroincidence#'
est2 <- est.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data %>% filter(Country == "Pakistan"),
  curve_params = curve,
  noise_params = noise %>% filter(Country == "Pakistan"),
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  #num_cores = 8 # Allow for parallel processing to decrease run time
)
# calculate summary statistics for the seroincidence object
summary(est2)