limdil

0th

Percentile

Limiting Dilution Analysis

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

Keywords
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
• 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

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.

• limdil
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)
Documentation reproduced from package statmod, version 1.3.0, License: LGPL version 2 or newer

Community examples

Looks like there are no examples yet.