BHAI (version 0.99.2)

bhai: Main function of the package to estimation of the burden of healthcare-associated infections

Description

Estimation of the burden of healthcare-associated infections

Usage

bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)
# S4 method for PPS
bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)

Arguments

pps

The PPS object containing the data.

nsim

Number of Monte Carlo simulations, default: 1000.

pop.sampling

Specifying whether parameters of the disease outcome trees should be sampled on population level, default: TRUE.

sample_distr

Distribution used for prevalence sampling, default: "rbetamix".

estimate_loi_fun

Function used for estimation of the length of infection, default: bootstrap_mean_gren (recommended!).

stratified_sampling

Specifying whether stratified sampling should be done.

summarize_strata

Specifying whether stratum-specific summary statistics should be computed.

use_prior

Specifying whether Prior distributions should be used for computations.

Value

A PPS class object.

See Also

'>PPS

Examples

# NOT RUN {
data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
bhai(german_pps_repr, nsim=10)

# }