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.
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.
A data frame of dose-effect data (typically, the output from
A model object that can be used to predict the corrected values
(as proportions) from
A logical scalar indicating if the output should be restricted to just the P value, default TRUE.
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
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
exp, expected effects;
obscorr, observed effects corrected; and
contrib, contributions to the chi-squared.
simple=TRUE, a numeric scalar, the chi-squared statistic
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].
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