loggrowth-class: Class '"loggrowth"'

In swash: Swash-Backwash Model for the Single Epidemic Wave

Class "loggrowth"

Description

The class "loggrowth" contains the results of the logistic_growth() function. Use summary(loggrowth) and plot(loggrowth) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by the function logistic_growth.

Slots

LinModel:

Object of class list Results of the OLS helper model

GrowthModel_OLS:

Object of class list Results of the OLS fit (predicted, parameters, first derivative)

GrowthModel_NLS:

Object of class list Results of the NLS fit (predicted, parameters, first derivative)

t:

Object of class numeric Input time points data

y:

Object of class numeric Input infections data

config:

Object of class list Model fit configurations

Methods

plot

signature(x = "loggrowth"): Plots the results of the logistic growth model (observed, predicted, first derivative)

summary

signature(object = "loggrowth"): Prints a summary of loggrowth objects

print

signature(x = "loggrowth"): Prints an loggrowth object; use summary(loggrowth) for results

Author(s)

Thomas Wieland

References

Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://dx.doi.org/10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81")}

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.epidem.2016.11.002")}

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18335/region.v7i2.324")}

Examples

showClass("loggrowth")

swash documentation built on Feb. 15, 2026, 5:07 p.m.