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

In jtlandis/dr4pl.dev: 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

## 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

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)

jtlandis/dr4pl.dev documentation built on May 25, 2019, 12:43 a.m.