egf_model: Define a Top Level Nonlinear Model

In davidearn/epigrowthfit: Nonlinear Mixed Effects Models of Epidemic Growth

Define a Top Level Nonlinear Model

Description

Sets flags defining a top level nonlinear model of epidemic growth to be estimated by egf.

Usage

egf_model(curve = c("logistic", "richards", "exponential",
                    "subexponential", "gompertz"),
          excess = FALSE,
          family = c("nbinom", "pois"),
          day_of_week = FALSE)

Arguments

curve	a character string specifying a model for expected cumulative incidence as a function of time.
excess	a logical flag. If TRUE, then a constant baseline mortality rate is estimated.
family	a character string specifying a family of discrete probability distributions assigned to observations, which are the first order differences of observed cumulative incidence.
day_of_week	an integer flag. If positive, then day of week effects are estimated as offsets relative to the indicated day of week (1=Sunday, 2=Monday, and so on).

Value

A list inheriting from class egf_model containing the validated arguments.

See Also

simulate.egf_model.

Examples

model <- egf_model()
str(model)

davidearn/epigrowthfit documentation built on Feb. 22, 2025, 12:44 p.m.