## Log-logistic fit
deguelin.m1 <- drm(r/n ~ dose, weights=n, data = deguelin, fct = LL.2(), type = "binomial")
modelFit(deguelin.m1)
summary(deguelin.m1)
## Loess fit
deguelin.m2 <- loess(r/n ~ dose, data = deguelin, degree = 1)
## Plot of data with fits superimposed
plot(deguelin.m1, ylim = c(0.2, 1))
lines(1:60, predict(deguelin.m2, newdata = data.frame(dose = 1:60)), col = 2, lty = 2)
pred1 <- predict(deguelin.m1, newdata = data.frame(dose = 1:60), se = FALSE)
pred2 <- predict(deguelin.m2, newdata = data.frame(dose = 1:60))
lines(1:60, 0.05 * pred1 + 0.95 * pred2, col = 3, lty = 3)
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