| sbm_ci-class | R Documentation |
"sbm_ci"The class "sbm_ci" contains the results of the Swash-Backwash Model, confidence intervals for the model estimates, and the related input data as well as additional information. Use summary(sbm_ci) and plot(sbm_ci) for results summary and plotting, respectively.
Objects can be created by the function confint(sbm).
R_0A:Object of class "numeric" Model result: spatial reproduction number R_{0A}
integrals:Object of class "numeric" Model result: integrals S_A, I_A, and R_A
velocity:Object of class "numeric" Model result: velocity measures t_{FE} and t_{LE}
occ_regions:Object of class "data.frame" Model result: Occurence at regional level
cases_by_date:Object of class "data.frame" Total cases by date
cases_by_region:Object of class "data.frame" Cumulative cases by region
input_data:Object of class "data.frame" Input data
data_statistics:Object of class "numeric" Diagnostics of input data
col_names:Object of class "character" Column names in input data
integrals_ci:Object of class "list" Confidence intervals for integrals S_A, I_A, and R_A
velocity_ci:Object of class "list" Confidence intervals for velocity measures t_{FE} and t_{LE}
R_0A_ci:Object of class "numeric" Confidence intervals for spatial reproduction number R_{0A}
iterations:Object of class "data.frame" Results of bootstrap sampling iterations
ci:Object of class "numeric" Lower and upper confidence intervals based on user input
config:Object of class "list" Configuration details for bootstrap sampling
signature(x = "sbm_ci"): Plots the results of bootstrap confidence intervals for the Swash-Backwash Model; one figure with six plots: S_A, I_A, R_A, t_{FE}, t_{LE}, and R_{0A}
signature(x = "sbm_ci"): Prints an sbm_ci object; use summary(sbm_ci) for results
signature(object = "sbm_ci"): Prints an sbm_ci object; use summary(sbm_ci) for results
signature(object = "sbm_ci"): Prints a summary of sbm_ci objects (bootstrap confidence intervals for Swash-Backwash Model estimates)
Thomas Wieland
Swash-Backwash Model:
Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1007/s10109-006-0027-8")}
Smallman-Raynor MR, Cliff AD, Stickler PJ (2022) Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33. Geographical Analysis 54, 197–216. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1111/gean.12272")}
Smallman-Raynor MR, Cliff AD, The COVID-19 Genomics UK (COG-UK) Consortium (2022) Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021. Epidemiology and Infection 150, e145. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1017/S0950268822001285")}.
Bootstrapping und bootstrap confidence intervals:
Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap.
Ramachandran KM, Tsokos CP (2021) Mathematical Statistics with Applications in R (Third Edition). Ch. 13.3.1 (Bootstrap confidence intervals). \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/B978-0-12-817815-7.00013-0")}
showClass("sbm_ci")
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