Estimate EC50 with one model

Kaique S Alves

2026-04-26

Introduction

Use estimate_EC50() when you already know which dose-response model should be fitted to each isolate. The function runs the same drc model across isolates and optional strata, then returns a single data frame with the EC estimates.

Packages

library(ec50estimator)
library(drc)
library(ggplot2)
library(cowplot)
library(ggridges)

Data

The package includes multi_isolate, a simulated dataset of mycelial growth under increasing fungicide doses for 50 fungal isolates, two field systems, and two fungicides.

data(multi_isolate)
head(multi_isolate)

##   isolate   field   fungicida  dose     growth
## 1       1 Organic Fungicide A 0e+00 20.2082399
## 2       1 Organic Fungicide A 1e-05 20.1168279
## 3       1 Organic Fungicide A 1e-04 19.2479678
## 4       1 Organic Fungicide A 1e-03 15.8123455
## 5       1 Organic Fungicide A 1e-02  7.3206757
## 6       1 Organic Fungicide A 1e-01  0.6985264

Inspect the raw dose-response data before fitting models.

ggplot(multi_isolate, aes(dose, growth, color = growth)) +
  geom_jitter(width = 0.1) +
  facet_grid(field ~ fungicida) +
  scale_x_log10() +
  scale_color_viridis_c(option = "B", direction = 1) +
  theme_light()

## Warning in scale_x_log10(): log-10 transformation introduced
## infinite values.

Estimate EC50

estimate_EC50() needs the same model ingredients you would pass to drc::drm(): a formula, a data frame, and a model function such as drc::LL.3(). Use isolate_col to identify isolates and strata_col for grouping columns such as field, fungicide, year, or location.

df_ec50 <- estimate_EC50(
  growth ~ dose,
  data = multi_isolate,
  isolate_col = "isolate",
  strata_col = c("field", "fungicida"),
  interval = "delta",
  fct = drc::LL.3()
)

head(df_ec50)

##      ID        field   fungicida    Estimate   Std..Error       Lower
## 1     1      Organic Fungicide A 0.006072082 0.0005740341 0.004902813
## 1751  1      Organic Fungicide B 0.006072082 0.0005740341 0.004902813
## 36    2 Conventional Fungicide A 0.101455765 0.0076364691 0.085900787
## 1786  2 Conventional Fungicide B 0.005297468 0.0005233600 0.004231418
## 71    3      Organic Fungicide A 0.003776957 0.0002432571 0.003281459
## 1821  3      Organic Fungicide B 0.003776957 0.0002432571 0.003281459
##            Upper
## 1    0.007241351
## 1751 0.007241351
## 36   0.117010744
## 1786 0.006363517
## 71   0.004272456
## 1821 0.004272456

The result keeps the isolate identifier in ID, then returns the strata columns and the effective-dose estimate columns from drc::ED().

Visualize Results

Plot EC50 estimates and confidence intervals for all isolates.

plot_ec50 <- df_ec50
plot_ec50$ID <- as.numeric(plot_ec50$ID)

ggplot(plot_ec50, aes(ID, Estimate, color = field)) +
  geom_point(size = 2) +
  geom_errorbar(aes(ymin = Lower, ymax = Upper, color = field), width = 0) +
  facet_wrap(~fungicida, scales = "free_x", ncol = 2) +
  scale_y_log10() +
  scale_x_continuous(breaks = 1:50) +
  scale_color_manual(values = c("darkred", "steelblue")) +
  labs(x = "Isolates", y = "EC50") +
  theme_minimal_vgrid(font_size = 10) +
  coord_flip() +
  theme(
    axis.text.x = element_text(size = 10),
    legend.position = "bottom"
  )

Density plots can help compare EC50 distributions between strata.

ggplot(df_ec50, aes(Estimate, field, fill = after_stat(x))) +
  geom_density_ridges_gradient(alpha = 0.3) +
  scale_x_log10() +
  scale_fill_viridis_c(option = "C") +
  facet_wrap(~fungicida, nrow = 2) +
  theme_minimal_hgrid() +
  labs(x = "EC50", y = "Field") +
  theme(legend.position = "none")

## Picking joint bandwidth of 0.0328

## Picking joint bandwidth of 0.0285