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

The R package LW1949 automates the steps taken in Litchfield and Wilcoxon's (1949) manual approach to evaluating dose-effect experiments (Adams et al. 2016). Letting the computer do the work saves time and yields the best fit possible using the Litchfield Wilcoxon approach (by minimizing the chi-squared statistic). You can also try a brief demonstration of LW1949 in this web app.

Install

Install

install.packages("LW1949")

and load the LW1949 package.

library(LW1949)

Prepare data

Use the dataprep function to create a data frame with the results of a dose-effect experiment. Provide information on three key input variables,

• chemical concentration (dose),
• total number tested (ntot), and
• number affected (nfx).

conc <- c(0.0625, 0.125, 0.25, 0.5, 1, 2, 3)
numtested <- rep(8, 7)
numaffected <- c(1, 4, 4, 7, 8, 8, 8)
mydat <- dataprep(dose=conc, ntot=numtested, nfx=numaffected)

The dataprep function puts the input variables into a data frame along with several new variables,

• record number (rec),
• proportional effects (pfx),
• log10 transformed dose (log10dose),
• probit transformed effects (bitpfx),
• an effects category (fxcateg) identifying none (0), partial (50), and complete (100) effects, and
• a column (LWkeep) to identify observations to keep when applying Litchfield and Wilcoxon's (1949) method (their step A).

mydat

Fit model

Use the fitLWauto and LWestimate functions to fit a dose-effect relation following Litchfield and Wilcoxon's (1949) method.

intslope <- fitLWauto(mydat)
fLW <- LWestimate(intslope, mydat)

The output from fitLWauto is a numeric vector of length two, the estimated intercept and slope of the best fitting line on the log10-probit scale..

intslope

The output from LWestimate is a list with three elements,

• chi, the chi-squared test comparing observed and expected effects, including the expected effects, the "corrected" expected effects (step B in Litchfield and Wilcoxon 1949), and the contribution to the chi-squared statistic (their step C);
• params, the estimated intercept and slope on the log10-probit scale; and
• LWest, additional estimates calculated in the process of using Litchfield and Wilcoxon's (1949) method (their steps D and E).

fLW

Predict

Use the predlinear function and the fitted Litchfield and Wilcoxon model to estimate the effective doses for specified percent effects (with 95% confidence limits).

pctaffected <- c(25, 50, 99.9)
predlinear(pctaffected, fLW)

Plot

Use the plotDELP and plotDE functions to plot the raw data on the log10-probit and arithmetics scales. Observations with no or 100% affected are plotted using white filled circles (at 0.1 and 99.9% respectively in the log10-probit plot).

Use the predLinesLP and predLines functions to add the L-W predicted relations to both plots, with 95% horizontal confidence intervals for the predicted dose to elicit a given percent affected.

plotDELP(mydat)
predLinesLP(fLW)

plotDE(mydat)
predLines(fLW)

References

Adams, J. V., K. S. Slaght, and M. A. Boogaard. 2016. An automated approach to Litchfield and Wilcoxon's evaluation of dose-effect experiments using the R package LW1949. Environmental Toxicology and Chemistry 35(12):3058-3061. DOI 10.1002/etc.3490

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

LW1949. An automated approach (R package) to Litchfield and Wilcoxon's (1949) evaluation of dose-effect experiments. Available on Cran, with the latest development version on GitHub.

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