confint.dr4pl: Fit a 4 parameter logistic (4PL) model to dose-response data.

Description Usage Arguments Details Value References Examples

View source: R/auxiliary.R

Description

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

Usage

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## 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.

References

\insertRef

Seber1989dr4pl

Examples

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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.