Predict Doses for Binomial Assay model

Calibrate binomial assays, generalizing the calculation of LD50.

dose.p(obj, cf = 1:2, p = 0.5)

Arguments

obj

A fitted model object of class inheriting from "glm".
cf

The terms in the coefficient vector giving the intercept and coefficient of (log-)dose
p

Probabilities at which to predict the dose needed.

Value

An object of class "glm.dose" giving the prediction (attribute "p" and standard error (attribute "SE") at each response probability.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Springer.

Examples

ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)

dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4)
#>               Dose        SE
#> p = 0.25: 2.231265 0.2499089
#> p = 0.50: 3.263587 0.2297539
#> p = 0.75: 4.295910 0.2746874
dose.p(update(budworm.lg0, family = binomial(link=probit)),
       cf = c(1,3), p = 1:3/4)
#>               Dose        SE
#> p = 0.25: 2.191229 0.2384478
#> p = 0.50: 3.257703 0.2240685
#> p = 0.75: 4.324177 0.2668745