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