# dose.p: Predict Doses for Binomial Assay model In MASS: Support Functions and Datasets for Venables and Ripley's MASS

## Description

Calibrate binomial assays, generalizing the calculation of LD50.

## Usage

 `1` ```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

 ```1 2 3 4 5 6 7 8 9``` ```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.p(update(budworm.lg0, family = binomial(link=probit)), cf = c(1,3), p = 1:3/4) ```

### Example output

```              Dose        SE
p = 0.25: 2.231265 0.2499089
p = 0.50: 3.263587 0.2297539
p = 0.75: 4.295910 0.2746874
Dose        SE
p = 0.25: 2.191229 0.2384478
p = 0.50: 3.257703 0.2240685
p = 0.75: 4.324177 0.2668745
```

MASS documentation built on May 3, 2021, 5:08 p.m.