summary.seroincidence.by – serocalculator

Summary Method for `“seroincidence.by”` Objects

Description

Calculate seroincidence from output of the seroincidence calculator est_seroincidence_by().

Usage

## S3 method for class 'seroincidence.by'
summary(
  object,
  confidence_level = 0.95,
  show_deviance = TRUE,
  show_convergence = TRUE,
  verbose = FALSE,
  ...
)

Arguments

object A dataframe containing output of est_seroincidence_by().

confidence_level desired confidence interval coverage probability

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

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

verbose a logical scalar indicating whether to print verbose messages to the console

… 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 show_deviance = TRUE) Negative log likelihood (NLL) at estimated (maximum likelihood) lambda)
nlm.convergence.code (included if show_convergence = 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_seroincidence_by()
Strata Character with names of strata used in est_seroincidence_by()

Examples

Code

library("serocalculator")

library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 |>
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

# estimate seroincidence
est2 <- est_seroincidence_by(
  strata = c("catchment"),
  pop_data = xs_data,
  sr_params = curve,
  noise_params = noise,
  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)

Seroincidence estimated given the following setup:
a) Antigen isotypes   : HlyE_IgG, HlyE_IgA 
b) Strata       : catchment 

 Seroincidence estimates:
# A tibble: 2 × 14
  Stratum  catchment     n est.start incidence.rate     SE CI.lwr CI.upr se_type
  <chr>    <chr>     <int>     <dbl>          <dbl>  <dbl>  <dbl>  <dbl> <chr>  
1 Stratum… aku          53       0.1          0.140 0.0216  0.104  0.189 standa…
2 Stratum… kgh          47       0.1          0.200 0.0301  0.149  0.268 standa…
# ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>,
#   antigen.isos <chr>, nlm.convergence.code <ord>

Summary Method for “seroincidence.by” Objects