limdil {statmod}R Documentation

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?

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.

Value

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

Author(s)

Gordon Smyth

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

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)

[Package statmod version 1.2.4 Index]