# assessfit: Assess Fit of Dose-Response Curve In LW1949: An Automated Approach to Evaluating Dose-Effect Experiments Following Litchfield and Wilcoxon (1949)

## Description

Assess the fit of a dose-response curve using the chi-squared statistic. The curve is described by the intercept and slope of a straight line in the log dose vs. probit effect scale.

## Usage

 `1` ```assessfit(params, DEdata, fit = gamtable1(), simple = TRUE) ```

## Arguments

 `params` A numeric vector of length two, with the estimated intercept and slope of the dose-effect relation on the log10 and probit scale. These parameters define the dose-response curve. `DEdata` A data frame of dose-effect data (typically, the output from `dataprep`) containing at least these four variables: dose, ntot, pfx, fxcateg. `fit` A model object that can be used to predict the corrected values (as proportions) from `distexpprop5`, the distance between the expected values (as proportions) and 0.5, default `gamtable1()`. `simple` A logical scalar indicating if the output should be restricted to just the P value, default TRUE.

## Details

This function is used to find the dose-response curve that minimizes the chi-squared statistic measuring the distance between the observed and expected values of the response (the proportion affected). Following Litchfield and Wilcoxon (1949, steps B1 and B2), records with expected effects < 0.01% or > 99.99% are deleted, and other expected effects are "corrected" using the `correctval` function.

## Value

If `simple=FALSE`, a list of length two. The first element, `chi`, is a numeric vector of length three: `chistat`, chi-squared statistic; `df`, degrees of freedom; and `pval`, P value. The second element, `contrib`, is a matrix of three numeric vectors the same length as `obsn`: `exp`, expected effects; `obscorr`, observed effects corrected; and `contrib`, contributions to the chi-squared.

If `simple=TRUE`, a numeric scalar, the chi-squared statistic (see details).

## 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].

## See Also

`LWchi2` and `chisq.test`.

## Examples

 ```1 2 3 4 5 6``` ```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) gamfit <- gamtable1() assessfit(log10(c(0.125, 0.5)), mydat, simple=FALSE) ```

LW1949 documentation built on May 2, 2019, 6:11 a.m.