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Fit epidemic models using linearization

Source: R/fit_lin.R
fit_lin.Rd

Fit exponential, monomolecular, logistic, and Gompertz models to disease progress data using linearized forms of each model.

Usage

fit_lin(time, y)

Arguments

time

Numeric vector of assessment times.

y

Numeric vector of disease intensity values.

Value

A list with fit statistics, parameter estimates, and prediction data.

Examples

set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit_lin(time = epi$time, y = epi$random_y)
#> Results of fitting population models 
#> 
#> Stats:
#>                  CCC r_squared    RSE
#> Logistic      0.9982    0.9964 0.1848
#> Gompertz      0.9786    0.9581 0.4304
#> Exponential   0.9326    0.8737 0.5983
#> Monomolecular 0.9318    0.8723 0.5743
#> 
#>  Infection rate:
#>                Estimate   Std.error     Lower     Upper
#> Logistic      0.2963172 0.003491710 0.2891399 0.3034946
#> Gompertz      0.1984260 0.008134087 0.1817061 0.2151458
#> Exponential   0.1516750 0.011307165 0.1284328 0.1749172
#> Monomolecular 0.1446423 0.010852569 0.1223345 0.1669500
#> 
#>  Initial inoculum:
#>                    Estimate Linearized     lin.SE         Lower        Upper
#> Logistic       0.0108173681 -4.5157260 0.06294769  0.0095169644  0.012293254
#> Gompertz       0.0002387652 -2.1210668 0.14663934  0.0000127004  0.002091927
#> Exponential    0.0231954412 -3.7637995 0.20384281  0.0152556644  0.035267457
#> Monomolecular -1.1210822598 -0.7519265 0.19564748 -2.1711224375 -0.418737384