Limiting Dilution Analysis
Fit single-hit model to a dilution series using complementary log-log binomial regression.
- numeric of integer counts of positive cases, out of
- numeric vector of expected number of cells in assay
- numeric vector giving number of trials at each dose
- logical, is the actual number of cells observed?
- numeric level for confidence interval
- logical, should the adequacy of the single-hit model be tested?
A binomial generalized linear model is fitted with cloglog link and offset
observed=FALSE, a classic Poisson single-hit model is assumed, and the Poisson frequency of the stem cells is the
exp of the intercept.
observed=TRUE, the values of
dose are treated as actual cell numbers rather than expected values.
This doesn't changed the generalized linear model fit but changes how the frequencies are extracted from the estimated model coefficient.
- List with components
CI numeric vector giving estimated frequency and lower and upper limits of Wald confidence interval test.unit.slope numeric vector giving chisquare likelihood ratio test statistic and p-value for testing the slope of the offset equal to one
Bonnefoix T, Bonnefoix P, Verdiel P, Sotto JJ. (1996). Fitting limiting dilution experiments with generalized linear models results in a test of the single-hit Poisson assumption. J Immunol Methods 194, 113-119.
Dose <- c(50,100,200,400,800) Responses <- c(2,6,9,15,21) Tested <- c(24,24,24,24,24) limdil(Responses,Dose,Tested,test.unit.slope=TRUE)