View source: R/summary.seroincidence.by.R
| summary.seroincidence.by | R Documentation | 
"seroincidence.by" ObjectsCalculate seroincidence from output of the seroincidence calculator
est.incidence.by().
## S3 method for class 'seroincidence.by'
summary(
  object,
  confidence_level = 0.95,
  showDeviance = TRUE,
  showConvergence = TRUE,
  ...
)
object | 
 A dataframe containing output of function   | 
confidence_level | 
 desired confidence interval coverage probability  | 
showDeviance | 
 Logical flag (  | 
showConvergence | 
 Logical flag (  | 
... | 
 Additional arguments affecting the summary produced.  | 
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()
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.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data,
  curve_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)
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