limdil: Limiting Dilution Analysis

Description

Fit single-hit model to a dilution series using complementary log-log binomial regression.

Usage

limdil(response,dose,tested=rep(1,length(response)),observed=FALSE,confidence=0.95,test.unit.slope=FALSE)

Arguments

response

numeric of integer counts of positive cases, out of tested trials

dose

numeric vector of expected number of cells in assay

tested

numeric vector giving number of trials at each dose

observed

logical, is the actual number of cells observed?

confidence

numeric level for confidence interval

test.unit.slope

logical, should the adequacy of the single-hit model be tested?

Value

List with components
CInumeric vector giving estimated frequency and lower and upper limits of Wald confidence interval
test.unit.slopenumeric vector giving chisquare likelihood ratio test statistic and p-value for testing the slope of the offset equal to one

Details

A binomial generalized linear model is fitted with cloglog link and offset log(dose). If observed=FALSE, a classic Poisson single-hit model is assumed, and the Poisson frequency of the stem cells is the exp of the intercept. If 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.

References

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.

Examples

Run this code

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)

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