R: Confidence Intervals for model parameters

confint.drc {drc} | R Documentation

## Confidence Intervals for model parameters

### Description

Computes confidence intervals for one or more parameters in a model of class 'drc'.

### Usage

      ## S3 method for class 'drc'
    confint(object, parm, level = 0.95, pool = TRUE, ...)

### Arguments

`object` | a model object of class 'drc'.
`parm` | a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
`level` | the confidence level required.

`pool` | logical. If TRUE curves are pooled. Otherwise they are not. This argument only works for models with independently fitted curves as specified in `drm`.
`...` | additional argument(s) for methods. Not used.

### Details

For binomial and Poisson data the confidence intervals are based on the normal distribution, whereas t distributions are used of for continuous/quantitative data.

### Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in

### Author(s)

Christian Ritz

### Examples

    ## Fitting a four-parameter log-logistic model
    ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = LL.4())

    ## Confidence intervals for all parameters
    confint(ryegrass.m1)

    ## Confidence interval for a single parameter
    confint(ryegrass.m1, "e")

[Package drc version 3.0-1 Index]