drmHetVar: Dose response modeling with heterogeneous variance

In DoseResponse/bmd: Benchmark dose estimation for dose-response data

Dose response modeling with heterogeneous variance

Description

Fit a dose-response model with heterogeneous variance dependending on dose level.

Usage

drmHetVar(formula, var.formula, data, fct, curveStart = NULL)

Arguments

formula	formula for the dose-response relationship
var.formula	one-sided formula specifying the dependance of the dose values and/or the fitted values on the point-wise standard error
data	data.frame containing the observations
fct	a list with three or more elements specifying the non-linear function, the accompanying self starter function, the names of the parameter in the non-linear function and, optionally, the first and second derivatives as well as information used for calculation of ED values. Currently available functions include, among others, the four- and five-parameter log-logistic models LL.4, LL.5 and the Weibull model W1.4. Use drc::getMeanFunctions for a full list.
curveStart	numerical of length equal to the number of parameters for the curve. Starting values for the curve parameters (optional).

Details

The aim to provide an R package calculating the benchmark dose (BMD) and the lower limit of the corresponding 95% confidence interval (BMDL) for continuous and quantal dose-response data for a range of dose-response models based on the available definitions of the benchmark dose concepts.

REFERENCES TO BE ADDED/WRITTEN

Value

dose-response model with a heterogeneous variance structure of class drcHetVar.

The primary objective is to use this model for benchmark dose estimation based on the hybrid method with a heterogeneous variance structure. This can be done using the bmdHetVar function.

A plot method is available, which can be useful for assessing the fit of the variance structure.

Author(s)

Signe M. Jensen and Jens Riis Baalkilde

Examples

library(drc)
library(drcData)
library(bmd)
# install.packages("gridExtra") # OPTIONAL - USED FOR PLOTTING A drcHetVar OBJECT.

# ryegrass data
set.seed(123)
ryegrass.LL.4.hetVar <- drmHetVar(rootl ~ conc, ~ fitted + I(fitted^2),
                                  data = ryegrass, fct = LL.4())
plot(ryegrass.LL.4.hetVar)
bmdHetVar(ryegrass.LL.4.hetVar, bmr = 0.1, backgType = "hybridPercentile", backg = 0.1,
          def = "hybridExc", R = 50, level = 0.95, progressInfo = TRUE, display = TRUE)
bmdHetVar(ryegrass.LL.4.hetVar, bmr = 0.1, backgType = "hybridPercentile", backg = 0.1, 
          def = "hybridExc", R = 50, level = 0.95, 
          bootType = "parametric", progressInfo = TRUE, display = TRUE) # parametric bootstrap

# barley data
set.seed(123)
barley.LL.4.hetVar <- drmHetVar(weight ~ Dose, ~ fitted + I(fitted^2), data = barley, fct = LL.4())
plot(barley.LL.4.hetVar)

# GiantKelp data
set.seed(123)
GiantKelp.LL.4.hetVarSq <- drmHetVar(tubeLength ~ dose, ~ fitted + I(fitted^2), 
                                     data = GiantKelp, fct = LL.4())
plot(GiantKelp.LL.4.hetVarSq)

GiantKelp.LL.4.hetVarLogSq <- drmHetVar(tubeLength ~ dose, ~ log(dose+1) + I(log(dose+1)^2), 
                                        data = GiantKelp, fct = LL.4())
plot(GiantKelp.LL.4.hetVarLogSq)

DoseResponse/bmd documentation built on July 4, 2025, 11:20 p.m.