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Summary Method for "seroincidence.by" Objects

Source: R/summary.seroincidence.by.R
summary.seroincidence.by.Rd

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,
  show_full_input = 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

show_full_input

logical; if TRUE, include metadata columns with noise parameters, observation counts, and input object names. Default is 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 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()

If show_full_input = TRUE, the following columns are also included:

  • measurement.noise.<antigen>, measurement.noise.<antigen>, etc.: measurement noise parameters (eps) for each antigen isotype, where <antigen> is the antigen-isotype name

  • biological.noise.<antigen>, biological.noise.<antigen>, etc.: biological noise parameters (nu) for each antigen isotype, where <antigen> is the antigen-isotype name

  • n.seroresponse.params: number of longitudinal seroresponse parameter observations for each stratum

  • seroresponse.params.stratified: logical indicating whether seroresponse parameters were stratified for each stratum

  • pop_data: name of the population data object passed to est_seroincidence_by()

  • sr_params: name of the seroresponse parameters object passed to est_seroincidence_by()

  • noise_params: name of the noise parameters object passed to est_seroincidence_by()

The object also has the following metadata (accessible through base::attr()):

Examples 

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
)
#> Warning: `curve_params` is missing all strata variables and will be used unstratified.
#> ℹ To avoid this warning, specify the desired set of stratifying variables in
#>   the `curve_strata_varnames` and `noise_strata_varnames` arguments to
#>   `est_seroincidence_by()`.
#> Warning: `noise_params` is missing all strata variables and will be used unstratified.
#> ℹ To avoid this warning, specify the desired set of stratifying variables in
#>   the `curve_strata_varnames` and `noise_strata_varnames` arguments to
#>   `est_seroincidence_by()`.

# 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 × 13
#>   Stratum catchment     n est.start incidence.rate     SE CI.lwr CI.upr coverage
#>   <chr>   <chr>     <int>     <dbl>          <dbl>  <dbl>  <dbl>  <dbl>    <dbl>
#> 1 Stratu… aku          53       0.1          0.140 0.0216  0.104  0.189     0.95
#> 2 Stratu… kgh          47       0.1          0.200 0.0301  0.149  0.268     0.95
#> # ℹ 4 more variables: log.lik <dbl>, iterations <int>, antigen.isos <chr>,
#> #   nlm.convergence.code <ord>