eadrm.ci {eadrm}

Computes confidence intervals for an eadrm model fit

Description

Calculates confidence intervals for an eadrm model fit by repeatedly fitting the model to the same data set and examining the distribution of the coefficients.

Usage

eadrm.ci(obs, xvals, model = "h4", ..., B = 1000)

Arguments

obs

A vector of response values (y-values).

xvals

A vector of doses (x-values).

model

Type of dose-response model to fit. Possible values include "h3", "h4", and "h5" (corresponding to 3-parameter, 4-parameter, and 5-parameter log-logistic models, respectively) and "e" (corresponding to an exponential model). Defaults to "h4".

...

Additional parameters for the eadrm function.

B

Number of replicate models to fit. Defaults to 1000.

Value

A list containing the following elements:

med.est:

A vector of the median values of the coefficients across the B iterations

l95.est,u95est:

Vectors of the lower/upper 95% confidence bounds for the coefficients across the B iterations

replicate.mat:

A p x B matrix, where p is the number of coefficients in the model. Each column of B corresponds to the coefficients for one fitted model.

Details

This function calls the eadrm function B times with the same parameters and records the model coefficients for each iteration of the model. Confidence intervals for the coefficients are calculated by examining the quantiles of the distribution of the coefficients over the B iterations. A matrix of the coefficients for each iteration is also calculated. This matrix can be used to compute confidence intervals for predicted values and estimates of EC50.

See Also

eadrm, predict.eadrm, calc.ec, calc.ed

Examples

ea.ci <- eadrm.ci(CarboA$y, CarboA$x)