# confint.dr4pl: Fit a 4 parameter logistic (4PL) model to dose-response data. In dr4pl: Dose Response Data Analysis using the 4 Parameter Logistic (4pl) Model

## Description

Compute the approximate confidence intervals of the parameters of a 4PL model based on the asymptotic normality of least squares estimators.

## Usage

 ```1 2``` ```## S3 method for class 'dr4pl' confint(object, parm = NULL, level = 0.95, ...) ```

## Arguments

 `object` An object of the dr4pl class `parm` Parameters of a 4PL model `level` Confidence level `...` Other parameters to be passed

## Details

This function computes the approximate confidence intervals of the true parameters of a 4PL model based on the asymptotic normality of the least squares estimators in nonlinear regression. The Hessian matrix is used to obtain the second order approximation to the sum-of-squares loss function. Please refer to Subsection 5.2.2 of Seber and Wild (1989).

## Value

A matrix of the confidence intervals in which each row represents a parameter and each column represents the lower and upper bounds of the confidence intervals of the corresponding parameters.

\insertRef

Seber1989dr4pl

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```obj.dr4pl <- dr4pl(Response ~ Dose, data = sample_data_1) # Fit a 4PL model to data ## Use the data 'sample_data_1' to obtain confidence intervals. confint(obj.dr4pl) # 95% confidence intervals confint(obj.dr4pl, level = 0.99) # 99% confidence intervals theta <- FindInitialParms(x = sample_data_1\$Dose, y = sample_data_1\$Response) # Use the same data 'sample_data_1' but different parameter estimates to obtain # confidence intervals. confint(obj.dr4pl, parm = theta) ```

dr4pl documentation built on Oct. 7, 2019, 5:05 p.m.