fitlinear: Determine Linear Regression Coefficients from Dose-Effect...

In JVAdams/LW1949: An Automated Approach to Evaluating Dose-Effect Experiments Following Litchfield and Wilcoxon (1949)

Description

Determine coefficients (intercept and slope) from dose-effect data using simple linear regression on the log10 dose vs. probit effect scale.

Usage

fitlinear(DEdata, constr = c(5e-04, 0.9995))

Arguments

DEdata	A data frame of dose-effect data (typically, the output from dataprep) containing at least three variables: log10dose, bitpfx, and LWkeep.
constr	A numeric vector of length two, indicating the constraints (see constrain) applied to the proportional effects, default c(0.0005, 0.9995). These numbers are used, rather than c(0.001, 0.999), as a way to ensure that effects that would be rounded (up to 0.1% or down to 99.9%) are still included in true Litchfield and Wilcoxon (1949) fashion.

Value

A numeric vector of length two, the estimated intercept and slope.

References

Litchfield, JT Jr. and F Wilcoxon. 1949. A simplified method of evaluating dose-effect experiments. Journal of Pharmacology and Experimental Therapeutics 96(2):99-113. [link].

Examples

conc <- c(0.0625, 0.125, 0.25, 0.5, 1)
numtested <- rep(8, 5)
nalive <- c(1, 4, 4, 7, 8)
mydat <- dataprep(dose=conc, ntot=numtested, nfx=nalive)
fitlinear(mydat)

JVAdams/LW1949 documentation built on May 7, 2019, 10:14 a.m.