Description Usage Arguments Details Value See Also Examples
View source: R/maximizepeaks.R
Creates a list of the most likely genotypes at each locus, and the most likely wholeprofile genotype for peak height data.
1 2  get.likely.genotypes.peaks(hypothesis,params,results,
posterior=FALSE,joint=FALSE,prob=ifelse(joint==FALSE,0.1,0.05))

hypothesis 
Hypothesis object created by either

params 
Parameters object created by

results 
Either prosecution or defence results returned by

posterior 
Logical indicating whether to return all genotype probabilities, rather than just the most likely. 
joint 
Logical indicating whether or not to return joint genotypes and probabilities. If FALSE, marginal genotypes and probabilities are returned instead. 
prob 
Probability threshold for singlelocus genotype probabilities. Defaults to 0.1 if returning marginal probabilities, and 0.05 if returning joint probabilities. 
Either joint or marginal genotypes and genotype probabilities
are given. Locusspecific genotypes are only given if their probabilty
exceeds prob
. The most likely wholeprofile genotype is given,
regardless of the probability threshold at each locus. Joint
probabilities give the probability of a multicontributor genotype,
whereas marginal probabilities give the probability of a single
contributor, summing over all the possible genotypes for all other
contributors.
locusSpecific 
Locus genotypes and probabilities which are
greater than 
topGenotype 
Most likely wholeprofile genotype for all
contributors if 
defence.hypothesis.peaks, prosecution.hypothesis.peaks, optimisation.params.peaks,evaluate.peaks
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  ## Not run:
# datapath to example files
datapath = file.path(system.file("extdata", package="likeLTD"),"laboratory")
# File paths and case name for allele report
admin = pack.admin.input.peaks(
peaksFile = file.path(datapath, 'laboratoryCSP.csv'),
refFile = file.path(datapath, 'laboratoryreference.csv'),
caseName = "Laboratory",
detectionThresh = 20
)
# Enter arguments
args = list(
nUnknowns = 1
)
# Create hypotheses
hypP = do.call(prosecution.hypothesis.peaks, append(admin,args))
hypD = do.call(defence.hypothesis.peaks, append(admin,args))
# Get parameters for optimisation
paramsP = optimisation.params.peaks(hypP)
paramsD = optimisation.params.peaks(hypD)
# reduce number of iterations for demonstration purposes
paramsP$control$itermax=25
paramsD$control$itermax=25
# Run optimisation
# n.steps and converge set for demonstration purposes
results = evaluate.peaks(paramsP, paramsD, n.steps=1,
converge=FALSE)
# get most likely marginal genotypes under defence
get.likely.genotypes.peaks(hypD,paramsD,
results$Def)
# get most likely joint genotypes under defence
gensJoint = get.likely.genotypes.peaks(hypD,paramsD,
results$Def,joint=TRUE)
# get posterior likelihoods for all genotype combinations
gensPosterior = get.likely.genotypes.peaks(hypD,paramsD,
results$Def,posterior=TRUE)
## End(Not run)

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