LW1949 (version 1.1.0)

predlinear: Determine the Effective Dose from a Linear Regression Fit

Description

Determine the effective dose for a specified percent effect from the intercept and slope of a linear regression.

Usage

predlinear(pct, LWmod, simple = FALSE)

Arguments

pct

A numeric vector of effects (in percents) for which to estimate the effective dose(s).

LWmod

If simple=TRUE, a numeric vector of length two giving the intercept and slope of the linear relation between the dose (x, the concentration of the applied chemical on the log10 scale), and the proportion of affected individuals (y, on the probit scale, with 0s converted to 0.1% and 1s converted to 99.9%). If simple=FALSE, a list with the results of fitting a Litchfield and Wilcoxon model to dose-effect data, the output from LWestimate.

simple

A logical scalar indicating whether to carry out a simple estimation of effective doses from the intercept and slope (TRUE), or an estimation of effective doses with confidence intervals from the Litchfield and Wilcoxon model (default, FALSE).

Value

If simple=TRUE, a numeric vector the same length as pct with the estimated effective doses. If simple=FALSE, an n*4 numeric matrix with the given effects (pct), the effective doses (ED), and Litchfield and Wilcoxon's (1949) 95% confidence intervals for the effective doses (lower and upper). The number of rows of the matrix, n, is the length of pct.

Details

Follows methods outlined in Litchfield and Wilcoxon (1949). Specifically, for the 95% confidence intervals, see page 105, and equation 13 in the Appendix (corresponding to Nomograph 4).

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. http://jpet.aspetjournals.org/content/96/2/99.abstract.

Examples

predlinear(c(16, 50, 84, 99.9), c(1.700875, 2.199559), simple=TRUE)

dose <- c(0.0625, 0.125, 0.25, 0.5, 1)
ntested <- rep(8, 5)
nalive <- c(1, 4, 4, 7, 8)
mydat <- dataprep(dose=dose, ntot=ntested, nfx=nalive)
fLW <- LWestimate(fitLWauto(mydat), mydat)
predlinear(c(25, 50, 99.9), fLW)