fitlogDist: Fit a Logarithmic Distribution to Forensic Data

In fitPS: Fit Zeta Distributions to Forensic Data

Fit a Logarithmic Distribution to Forensic Data

Description

This function uses maximum likelihood estimation (MLE) to estimate the shape parameter of a logarithmic distribution from a set of observed counts for either the number of groups/sources of forensically interesting material (mostly glass or paint) recovered from clothing, or the number of fragments/particles in each group. This, in turn, allows the estimation of the P and S probabilities, as described by Evett and Buckleton (1990), which used in computing the likelihood ratio (LR) for activity level propositions. The data itself arises from clothing surveys. The logarithmic distribution has probability mass function

p(k) = \frac{\pi^k}{k\log_e(1-\pi)},0<\pi<1.

Usage

fitlogDist(x, nterms = 10, start = 0.5, ...)

fitLogdist(x, nterms = 10, start = 0.5, ...)

fitlogdist(x, nterms = 10, start = 0.5, ...)

Arguments

x	an object of type psData, usually obtained from readData.
nterms	the number of terms to compute the probability distribution for.
start	a starting value for the optimiser.
...	other parameters - not currently used.

Details

The function returns an object of class psFit which is a list contains seven elements:

psData

– an object of class psData–see readData,

fit

– the fitted object from optim,

pi

- the maximum likelihood estimate of the shape parameter,

var

– the estimated variance for the shape parameter,

fitted

– a named vector containing the first nterms of the fitted distribution.

Value

an object of class psFit–see Details.

References

Evett, I. W. and Buckleton, J. S., "The interpretation of glass evidence. A practical approach", Journal of the Forensic Science Society 1990: 30(4): 215–223.

See Also

plot.psFit, print.psFit, probfun.

Examples

data(Psurveys)
roux = Psurveys$roux
fit = fitlogDist(roux)
fit

fitPS documentation built on June 24, 2024, 9:09 a.m.