The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Model fitting workflows

Kaique S. Alves

2026-04-12

library(epifitter)
library(dplyr)
library(ggplot2)
library(cowplot)
theme_set(cowplot::theme_half_open(font_size = 12))

Overview

epifitter offers three complementary fitting workflows:

Compare models quickly with fit_lin()

set.seed(1)
epi <- sim_logistic(N = 60, y0 = 0.01, dt = 5, r = 0.12, alpha = 0.2, n = 4)

fit_lin_out <- fit_lin(time = epi$time, y = epi$random_y)
knitr::kable(fit_lin_out$stats_all, digits = 4)
best_model model r r_se r_ci_lwr r_ci_upr v0 v0_se r_squared RSE CCC y0 y0_ci_lwr y0_ci_upr
1 Logistic 0.1202 0.0011 0.1180 0.1224 -4.5730 0.0394 0.9957 0.1504 0.9979 0.0102 0.0095 0.0111
2 Gompertz 0.0694 0.0022 0.0649 0.0739 -1.9893 0.0792 0.9505 0.3021 0.9746 0.0007 0.0002 0.0020
3 Exponential 0.0780 0.0029 0.0722 0.0839 -4.0617 0.1032 0.9346 0.3938 0.9662 0.0172 0.0140 0.0212
4 Monomolecular 0.0422 0.0028 0.0366 0.0478 -0.5113 0.0983 0.8215 0.3751 0.9020 -0.6674 -1.0315 -0.3687 
plot_fit(fit_lin_out, models = c("Logistic", "Gompertz"))

Faceted plot comparing logistic and Gompertz fitted curves against observed disease intensity values.

Nonlinear fitting with starting values

fit_nlin_out <- fit_nlin(
  time = epi$time,
  y = epi$random_y,
  starting_par = list(y0 = 0.01, r = 0.03)
)

fit_nlin_out$stats_all

Estimate K when the epidemic plateaus below 1

epi_partial <- epi %>%
  mutate(random_y = random_y * 0.8)

fit_k <- fit_nlin2(
  time = epi_partial$time,
  y = epi_partial$random_y,
  starting_par = list(y0 = 0.01, r = 0.03, K = 0.7)
)

fit_k$stats_all

Grouped fitting with fit_multi()

epi1 <- sim_gompertz(N = 50, y0 = 0.001, dt = 5, r = 0.08, alpha = 0.2, n = 3)
epi2 <- sim_gompertz(N = 50, y0 = 0.002, dt = 5, r = 0.11, alpha = 0.2, n = 3)

multi_epi <- bind_rows(epi1, epi2, .id = "curve")

multi_fit <- fit_multi(
  time_col = "time",
  intensity_col = "random_y",
  data = multi_epi,
  strata_cols = "curve"
)

knitr::kable(head(multi_fit$Parameters), digits = 4)
curve best_model model r r_se r_ci_lwr r_ci_upr v0 v0_se r_squared RSE CCC y0 y0_ci_lwr y0_ci_upr
1 1 Gompertz 0.0813 0.0016 0.0780 0.0846 -1.9382 0.0480 0.9878 0.1475 0.9939 0.0010 0.0005 0.0018
1 2 Logistic 0.1623 0.0091 0.1438 0.1808 -5.1753 0.2685 0.9116 0.8244 0.9537 0.0056 0.0033 0.0097
1 3 Monomolecular 0.0449 0.0026 0.0396 0.0502 -0.3618 0.0769 0.9058 0.2360 0.9506 -0.4359 -0.6796 -0.2276
1 4 Exponential 0.1174 0.0111 0.0948 0.1400 -4.8135 0.3276 0.7838 1.0058 0.8788 0.0081 0.0042 0.0158
2 1 Gompertz 0.1126 0.0015 0.1095 0.1157 -1.8561 0.0448 0.9944 0.1377 0.9972 0.0017 0.0009 0.0029
2 2 Monomolecular 0.0802 0.0035 0.0729 0.0874 -0.5657 0.1050 0.9427 0.3224 0.9705 -0.7606 -1.1810 -0.4212 
knitr::kable(head(multi_fit$Data), digits = 4)
curve time y model linearized predicted residual
1 0 0.0011 Exponential -6.8418 0.0081 -0.0071
1 0 0.0011 Monomolecular 0.0011 -0.4359 0.4370
1 0 0.0011 Logistic -6.8408 0.0056 -0.0046
1 0 0.0011 Gompertz -1.9231 0.0010 0.0001
1 5 0.0076 Exponential -4.8831 0.0146 -0.0070
1 5 0.0076 Monomolecular 0.0076 -0.1473 0.1549

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.