fit_nlin.RdFits epidemic models (Exponential, Monomolecular, Logistic and Gompertz) using nonlinear approach for estimate parameters.
fit_nlin(time, y, starting_par = list(y0 = 0.01, r = 0.03), maxiter = 50)
| time | Numeric vector which refers to the time steps in the epidemics |
|---|---|
| y | Numeric vector which refers to the disease intensity |
| starting_par | Starting value for initial inoculun (y0) and apparent infection rate (r). Please informe in that especific order |
| maxiter | Maximun number of iterations |
Kaique dos S. Alves
set.seed(1) epi1 <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.5, n = 4) data = data.frame(time = epi1[,2], y = epi1[,4]) fit_nlin(time = data$time, y = data$y, starting_par = list(y0 = 0.001, r = 0.03), maxiter = 1024)#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Warning: NaNs produzidos#> Results of fitting population models #> #> Stats: #> CCC r_squared RSE #> Gompertz 0.9950 0.9909 0.0429 #> Logistic 0.9923 0.9860 0.0529 #> Monomolecular 0.8960 0.8361 0.1762 #> Exponential 0.8795 0.8131 0.1880 #> #> Infection rate: #> Estimate Std.error Lower Upper #> Gompertz 0.26304504 0.015218902 0.23176214 0.23176214 #> Logistic 0.36613264 0.028677439 0.30718532 0.30718532 #> Monomolecular 0.06822917 0.008441786 0.05087684 0.05087684 #> Exponential 0.06438233 0.008534818 0.04683876 0.04683876 #> #> Initial inoculum: #> Estimate Std.error Lower Upper #> Gompertz 1.017193e-12 5.947383e-12 -1.120783e-11 1.324221e-11 #> Logistic 5.304364e-03 2.219734e-03 7.416344e-04 9.867093e-03 #> Monomolecular -1.487705e-01 7.778021e-02 -3.086500e-01 1.110901e-02 #> Exponential 1.658502e-01 3.643013e-02 9.096701e-02 2.407334e-01