calcMLE: calcMLE

In oyvble/euroformix: A Quantitative Model for Interpreting DNA Mixtures

calcMLE

Description

Optimizes the likelihood function of the DNA mixture model

Usage

calcMLE(
  nC,
  samples,
  popFreq,
  refData = NULL,
  condOrder = NULL,
  knownRef = NULL,
  kit = NULL,
  DEG = TRUE,
  BWS = TRUE,
  FWS = TRUE,
  AT = 50,
  pC = 0.05,
  lambda = 0.01,
  fst = 0,
  knownRel = NULL,
  ibd = NULL,
  minF = NULL,
  normalize = TRUE,
  steptol = 1e-04,
  nDone = 3,
  delta = 1,
  difftol = 0.01,
  seed = NULL,
  verbose = FALSE,
  priorBWS = NULL,
  priorFWS = NULL,
  maxThreads = 0,
  adjQbp = FALSE
)

Arguments

nC	Number of contributors in model.
samples	A List with samples which for each samples has locus-list elements with list elements adata and hdata. 'adata' is a qualitative (allele) data vector and 'hdata' is a quantitative (peak heights) data vector.
popFreq	A list of allele frequencies for a given population.
refData	Reference objects has locus-list element [[i]] with a list element 'r' which contains a 2 long vector with alleles for each references.
condOrder	Specify conditioning references from refData (must be consistent order). For instance condOrder=(0,2,1,0) means that we restrict the model such that Ref2 and Ref3 are respectively conditioned as 2. contributor and 1. contributor in the model.
knownRef	Specify known non-contributing references from refData (index). For instance knownRef=(1,2) means that reference 1 and 2 is known non-contributor in the hypothesis. This affectes coancestry correction.
kit	shortname of kit: Obtained from getKit()
DEG	Boolean of whether Degradation model should be used
BWS	Boolean of whether back-stutter model should be used
FWS	Boolean of whether for-stutter model should be used
AT	The analytical threshold given. Used when considering probability of allele drop-outs.
pC	A numeric for allele drop-in probability. Default is 0.
lambda	Parameter in modeled peak height shifted exponential model. Default is 0.
fst	is the coancestry coeffecient. Default is 0.
knownRel	gives the index of the reference which the 1st unknown is related to.
ibd	the identical by decent coefficients of the relationship (specifies the type of relationship)
minF	The freq value included for new alleles (new alleles as potential stutters will have 0). Default NULL is using min.observed in popFreq.
normalize	Whether normalization should be applied or not. Default is FALSE.
steptol	Argument used in the nlm function for faster return from the optimization (tradeoff is lower accuracy).
nDone	Number of optimizations required providing equivalent results (same logLik value obtained)
delta	Scaling of variation of normal distribution when drawing random startpoints. Default is 1.
difftol	Tolerance for being exact in log-likelihood value (relevant when nDone>1)
seed	The user can set seed if wanted
verbose	Whether printing limits to integrate over. Printing progress if maxEval>0. Default is TRUE.
priorBWS	Prior function for BWS-parameter. Flat prior on [0,1] is default.
priorFWS	Prior function for FWS-parameter. Flat prior on [0,1] is default.
maxThreads	Maximum number of threads to be executed by the parallelization
adjQbp	Indicate whether fragmenth length of Q-allele is based on averaged weighted with frequencies

Value

Fitted maximum likelihood object

Author(s)

Oyvind Bleka

Examples

## Not run: 
kit = "ESX17"
AT = 50 #analytical threshold
sep0 = .Platform$file.sep
popfn = paste(path.package("euroformix"),"FreqDatabases",paste0(kit,"_Norway.csv"),sep=sep0)
evidfn = paste(path.package("euroformix"),"examples",paste0(kit,"_3p.csv"),sep=sep0)
reffn = paste(path.package("euroformix"),"examples",paste0(kit,"_refs.csv"),sep=sep0)
popFreq = freqImport(popfn)[[1]] #population frequencies
samples = sample_tableToList(tableReader(evidfn)) #evidence samples
refData = sample_tableToList(tableReader(reffn)) #reference sample
plotEPG2(samples,kit,refData,AT)
condOrder = c(1,2,0) #assuming C1=ref1,C2=ref2
mlefit = contLikMLE(3,samples,popFreq,refData,condOrder,kit=kit)
plotTopEPG2(mlefit)

## End(Not run)

oyvble/euroformix documentation built on Aug. 25, 2023, 11:14 a.m.