Package index • serosv

Dataset

Hepatitis A

hav_be_1993_1994: Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated)
hav_be_2002: Hepatitis A serological data from Belgium in 2002 (line listing)
hav_bg_1964: Hepatitis A serological data from Bulgaria in 1964 (aggregated)

Hepatitis B

hbv_ru_1999: Hepatitis B serological data from Russia in 1999 (aggregated)

Hepatitis C

hcv_be_2006: Hepatitis C serological data from Belgium in 2006 (line listing)

Mumps

mumps_uk_1986_1987: Mumps serological data from the UK in 1986 and 1987 (aggregated)

Parvo B19

parvob19_be_2001_2003: Parvo B19 serological data from Belgium from 2001-2003 (line listing)
parvob19_ew_1996: Parvo B19 serological data from England and Wales in 1996 (line listing)
parvob19_fi_1997_1998: Parvo B19 serological data from Finland from 1997-1998 (line listing)
parvob19_it_2003_2004: Parvo B19 serological data from Italy from 2003-2004 (line listing)
parvob19_pl_1995_2004: Parvo B19 serological data from Poland from 1995-2004 (line listing)

Rubella

rubella_uk_1986_1987: Rubella serological data from the UK in 1986 and 1987 (aggregated)

Tuberculosis

tb_nl_1966_1973: Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)

VZV

vzv_be_1999_2000: VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated)
vzv_be_2001_2003: VZV serological data from Belgium from 2001-2003 (line listing)

Multivariate data

rubella_mumps_uk: Rubella - Mumps data from the UK (aggregated)
vzv_parvo_be: VZV and Parvovirus B19 serological data in Belgium (line listing)

Models

polynomial_model(): Polynomial models
farrington_model(): The Farrington (1990) model.
weibull_model(): The Weibull model.
fp_model(): A fractional polynomial model.
lp_model(): A local polynomial model.
hierarchical_bayesian_model(): Hierarchical Bayesian Model
penalized_spline_model(): Penalized Spline model
mixture_model(): Fit a mixture model to classify serostatus
estimate_from_mixture(): Estimate seroprevalence and FOI from a fixed mixture model
age_time_model(): Age-time varying seroprevalence

Plotting functions

plot_corrected_prev(): Plot output for corrected_prevalence
plot_standard_curve(): Visualize standard curves for each plate
plot_titer_qc(): Quality control plot
plot_gcv(): Plotting GCV values with respect to different nn-s and h-s parameters.
plot(<polynomial_model>): plot() overloading for polynomial model
plot(<farrington_model>): plot() overloading for Farrington model
plot(<weibull_model>): plot() overloading for Weibull model
plot(<fp_model>): plot() overloading for fractional polynomial model
plot(<lp_model>): plot() overloading for local polynomial model
plot(<hierarchical_bayesian_model>): plot() overloading for hierarchical_bayesian_model
plot(<penalized_spline_model>): plot() overloading for penalized spline
plot(<mixture_model>): plot() overloading for mixture model
plot(<estimate_from_mixture>): plot() overloading for result of estimate_from_mixture
plot(<age_time_model>): Plot output for age_time_model
set_plot_style(): Helper to adjust styling of a plot
add_thresholds(): Visualize positive threshold at different dilution factors

Other utilities

serosv serosv-package: serosv: model infectious disease parameters
est_foi(): Estimate force of infection
pava(): Monotonize seroprevalence
to_titer(): Convert assay readings to titers
correct_prevalence(): Estimate the true sero prevalence using Frequentist/Bayesian estimation
transform_data(): Aggregate data
standardize_data(): Standardize raw serological test data for titer conversion
compare_models(): Generate table of metrics for model comparison
compute_ci(<default>): Compute confidence interval for a model of serosv
compute_ci(<fp_model>): Compute confidence interval for fractional polynomial model
compute_ci(<lp_model>): Compute confidence interval for local polynomial model
compute_ci(<weibull_model>): Compute confidence interval for Weibull model
compute_ci(<penalized_spline_model>): Compute confidence interval for penalized_spline_model
compute_ci(<mixture_model>): Compute confidence interval for mixture model
compute_ci(<age_time_model>): Compute confidence interval for time age model
compute_ci(<hierarchical_bayesian_model>): Compute 95% credible interval for hierarchical Bayesian model
predict(<age_time_model>): Predict from the age_time_mdoel
predict(<farrington_model>): Prediction for serosv Farrington model
predict(<fp_model>): Prediction for serosv fractional polynomial model
predict(<hierarchical_bayesian_model>): Predict from an hierarchical bayesian model
predict(<lp_model>): Prediction for serosv local polynomial model
predict(<penalized_spline_model>): Prediction for serosv penalized spline model
predict(<weibull_model>): Prediction for serosv Weibull model
predict(<polynomial_model>): Prediction for serosv polynomial model
find_best_fp_powers(): Returns the powers of the fractional polynomial model which has the lowest deviance score.