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

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]