fitTirm | R Documentation |
Fits the Two Innate Rates Model (TIRM) to count data to obtain the MLE for population size
fitTirm(data, max.pop, max.iter = 20)
data |
A two-column data frame with the first column specifiying the capture class (i.e. individuals in class i were caught i times) and the second column specifying the number of individuals in each class |
max.pop |
The maximum population size |
max.iter |
The maximum number of iterations to acheive convergence. The default will be more than enough for most cases |
The TIRM model fit by this function assumes that individuals can be assigned to two classes. Class A represent the frequently captured individuals. Class B represents the infrequently captured individuals.
The value is specified for max.pop
is not likely to matter as long as it is much greater than the maximum likelihood estimate for population size.
Note that if the data contains only singletons, the data is not informative and the maximum likelihood estimate for population size will be equal to max.pop
an object of class capfit
containing the follow elements:
The model specified
The likelihood of the model
The maximum likelihood estimate for population size
The maximum likelihood estimate for the number of individuals in class A
The maximum likelihood estimate for the number of individuals in class B
The ratio of the rates of captures between class A and class B individuals
The mean number of captures per individual
The total number of individuals in the sample
Total number of samples in the data set
The maximum population size specified by max.pop
Matthew W. Pennell
Miller C. R., P. Joyce and L.P. Waits. 2005. A new method for estimating the size of small populations from genetic mark-recapture data. Molecular Ecology 14:1991-2005.
Pennell, M. W., C. R. Stansbury, L. P. Waits and C. R. Miller. 2013. Capwire: a R package for estimating population census size from non-invasive genetic sampling. Molecular Ecology Resources 13:154-157.
simTirm
## Simulate data under Two Innate Rates Model data <- simTirm(na=20, nb=15, a=4, s=150) ## Fit Two Innate Rates Model to Data res <- fitTirm(data=data, max.pop=200) res
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