optimisation.params: likeLTD::optimisation.params
In likeLTD: Tools to Evaluate DNA Profile Evidence

Description Usage Arguments Details Value Examples

Creates a list of parameters to use with DEoptim::DEoptim.

optimisation.params(hypothesis, verbose=FALSE, fixed=NULL,
                           logObjective=TRUE, logDegradation=TRUE,
                           arguments=NULL, zero=0, throwError=FALSE,
                           withPenalties=TRUE, doLinkage=TRUE,objective=NULL, iterMax=75, 
                           likeMatrix=FALSE,...)

`hypothesis`	Hypothesis from which to perform maximization
`verbose`	Wether to print likelihood each and every time the objective function is called
`fixed`	Names of the parameters to keep fixed
`logObjective`	If `TRUE` (default), the objective function is the log10-likelihood.
`logDegradation`	If `TRUE` (default), the degradation parameters are entered as 10^x
`arguments`	Initial parameters from which to start the maximization. If `NULL`, calls `initial.arguments`.
`zero`	Epsilon to indicate lower and upper bounds as alpha +/- epsilon that exclude the bound itself
`throwError`	If TRUE, throws an error if the result is infinite
`withPenalties`	If TRUE, then penalties are evaluated and used
`doLinkage`	Logical indicating whether or not to apply a correction for linked loci. This correction is only applied when Q and X are assumed to be siblings i.e. `hypothesis$relatedness=c(0.5,0.25)`. This multiplies the prosecution likelihood by IMP_L/IMP_U, where IMP_L is the inverse match probability with linkage taken into account, and IMP_U is the same but without linkage taken into account.
`objective`	Objective function produced from create.likelihood.vectors
`iterMax`	Number of iterations to run the optimisation for
`likeMatrix`	Whether to return likelihoods for every genotype combination, or a likelihood summed over all genotypes after optimisation. Set to TRUE for individual genotype likelihoods. This is used for `get.likely.genotypes`.
`...`	Any named parameter to modify the hypothesis, e.g. `nUnknowns`

Starting from the hypothesis, it creates an list of arguments which can be applied to DEoptim::DEoptim to obtain the maximum (log-)likelihood of that hypothesis.

It accepts a number of customization:

The optimisation can be performed for the likelihood or the log of the likelihood. The latter is recommended.
wether the degradation parameter should be inputs as x or as an exponent 10^x. The latter seems to be more numerically stable, likely because degradations (in first form) are factors of an exponent in any case.
whether to keep some nuisance parameters fixed

In any case, the value returned can always be modified prior to calling DEoptim::DEoptim.

`fn`	The objective function
`lower`	Lower bounds for the parameters
`upper`	Upper bounds for the parameters
`control`	Control parameters for `DEoptim::DEoptim`

## Not run: 
# datapath to example files
datapath = file.path(system.file("extdata", package="likeLTD"),"hammer")

# File paths and case name for allele report
admin = pack.admin.input(
            cspFile = file.path(datapath, 'hammer-CSP.csv'),
            refFile = file.path(datapath, 'hammer-reference.csv'),
            caseName = "hammer",
	    kit= "SGMplus"
             )

# Enter arguments
args = list(
        nUnknowns = 1,
        doDropin = FALSE,
        ethnic = "EA1",
        adj = 1,
        fst = 0.02,
        relatedness = c(0,0)
        )

# Create hypotheses
hypP = do.call(prosecution.hypothesis, append(admin,args))
hypD = do.call(defence.hypothesis, append(admin,args))

# Get parameters for optimisation
paramsP = optimisation.params(hypP)
paramsD = optimisation.params(hypD)

## End(Not run)