confint.dr4pl {dr4pl}

Fit a 4 parameter logistic (4PL) model to dose-response data.

Description

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

Usage

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

Arguments

object

An object of the dr4pl class

parm

parameters of the dr4pl object. Usually made with [dr4pl_theta]

level

Confidence level

...

Other parameters to be passed to vcov

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.

References

Seber GAF, Wild CJ (1989). Nonlinear regression, Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics. John Wiley \& Sons, Inc., New York. ISBN 0-471-61760-1, doi: 10.1002/0471725315, http://dx.doi.org.libproxy.lib.unc.edu/10.1002/0471725315.

Examples

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)