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]