Fit exponential, monomolecular, logistic, and Gompertz models to disease progress data using nonlinear regression.
Usage
fit_nlin(time, y, starting_par = list(y0 = 0.01, r = 0.03), maxiter = 50)Arguments
- time
Numeric vector of assessment times.
- y
Numeric vector of disease intensity values.
- starting_par
Named list with starting values for `y0` and `r`. When omitted or partially specified, `epifitter` supplies data-driven fallback values.
- maxiter
Maximum number of iterations. Must be a positive number.
Examples
set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit_nlin(time = epi$time, y = epi$random_y, starting_par = list(y0 = 0.01, r = 0.03))
#> Results of fitting population models
#>
#> Stats:
#> CCC r_squared RSE
#> Gompertz 0.9986 0.9981 0.0217
#> Logistic 0.9986 0.9974 0.0220
#> Exponential 0.9038 0.8493 0.1665
#> Monomolecular NA NA NA
#>
#> Infection rate:
#> Estimate Std.error Lower Upper
#> Gompertz 0.22500335 0.006307726 0.21203764 0.2379691
#> Logistic 0.31571569 0.009477990 0.29623341 0.3351980
#> Exponential 0.06632831 0.007801830 0.05029142 0.0823652
#> Monomolecular NA NA NA NA
#>
#> Initial inoculum:
#> Estimate Std.error Lower Upper
#> Gompertz 1.279015e-08 2.122676e-08 -3.084208e-08 5.642237e-08
#> Logistic 8.926801e-03 1.286286e-03 6.282802e-03 1.157080e-02
#> Exponential 1.560581e-01 3.149510e-02 9.131899e-02 2.207972e-01
#> Monomolecular NA NA NA NA