| Title: | Inference About Relationships from DNA Mixtures |
|---|---|
| Description: | Methods for inference about relationships between contributors to a DNA mixture and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>. |
| Authors: | Peter Green [aut, cre] |
| Maintainer: | Peter Green <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.1.1 |
| Built: | 2024-12-09 06:34:02 UTC |
| Source: | CRAN |
Methods for inference about relationships between contributors to a DNA mixture and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.
This package is a toolkit for inference about mixtures and familial relationships, either between contributors or between a contributor and other typed individuals. It extends the functionality proposed in Green and Mortera (2017) by allowing more general relationships, specified in general by an IBD pattern distribution - the generalisation to more than two individuals of the coefficients of identity of Jacquard (1974). Details are in the paper by Green and Mortera (2021). KinMixLite extends the capability of the DNAmixturesLite package, and intimately relies on that package; as with that package, instead of the RHugin package, it uses gRaven and gRain for Bayes Net calculations. This version implements the ALN, MBN and WLR as well as RPT methods; see Green and Mortera (2017).
See formats for formats of the various data objects used in this package.
Maintainer: Peter Green <[email protected]>
Green, P. J. and Mortera, J. (2017). Paternity testing and other inference about relationships from DNA mixtures. Forensic Science International: Genetics. <doi:10.1016/j.fsigen.2017.02.001>.
Green, P. J. and Mortera, J. (2021). Inference about complex relationships using peak height data from DNA mixtures. Applied Statistics. <doi:10.1111/rssc.12498>.
Jacquard, A. (1974) The genetic structure of populations. Springer-Verlag.
require(ribd) data(test2data) data(NGMDyes) C<-50 ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model in which contributor 1 is parent of a typed individual Cgt mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,'parent',list(c=Cgt),targets=c('f','c'),contrib='f') log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture, where contributors are siblings mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.6,U2=0.3,U3=0.1))) baseline<-logL(mixD)(pars) mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,'sibs',targets=c('b1','b2'),contribs=c('b1','b2')) log10LR<-(protected(logL(mixD)(pars))-baseline)/log(10) cat('log10 LR',log10LR,'\n')require(ribd) data(test2data) data(NGMDyes) C<-50 ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model in which contributor 1 is parent of a typed individual Cgt mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,'parent',list(c=Cgt),targets=c('f','c'),contrib='f') log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture, where contributors are siblings mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.6,U2=0.3,U3=0.1))) baseline<-logL(mixD)(pars) mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,'sibs',targets=c('b1','b2'),contribs=c('b1','b2')) log10LR<-(protected(logL(mixD)(pars))-baseline)/log(10) cat('log10 LR',log10LR,'\n')
loop over markers, and alleles within markers to create nodes for child allele count nodes, for paternity model with only Child genotyped then compile all domains. Implements method MBN.
add.child.meiosis.nodes(mixture,aca,ind=1)add.child.meiosis.nodes(mixture,aca,ind=1)
mixture |
A compiled DNAmixture object |
aca |
Child's genotype profile as an allele count array |
ind |
Index of contributor regarded as Parent (or Child): which ‘unknown’ contributor are we modelling by amending his/her CPTs? |
To calculate the likelihood of this model, conditional on the child's genotype, a call to this function should be followed by (a) setting the finding of the child's genotype by defining extra.findings, (b) evaluating the loglikelihood using logLX, and (c) correcting the result by subtracting the log probability of the child's genotype, all as in the example below. Without (c), the value returned is the likelihood for the peak heights and the child's genotype.
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) log10LR<-(logLX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )) )(pars)-attr(cgtcaca,'logGt')-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) log10LR<-(logLX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )) )(pars)-attr(cgtcaca,'logGt')-baseline)/log(10) cat('log10 LR',log10LR,'\n')
loop over markers, and alleles within markers to create node Rlikd for relative likelihood for individual i, for paternity model with Mother and Child genotyped then compile all domains. Implements method ALN.
add.motherchild.likd.node(mixture,Cgt,Mgt,db,ind=1)add.motherchild.likd.node(mixture,Cgt,Mgt,db,ind=1)
mixture |
A DNAmixture object |
Cgt |
Child's genotype profile as a data frame containing variables |
Mgt |
Mother's genotype profile as a data frame containing variables |
db |
Allele frequency database |
ind |
Index of contributor regarded as Father: which ‘unknown’ contributor are we modelling by amending his CPTs? |
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixD3<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixD3,Cgt) add.motherchild.likd.node(mixD3,Cgt,Mgt,db,1) log10LR<-(logLX(mixD3, expr.make.findings(list( list('Male',ind=1), list('Rlikd',aca='cgtcaca',cgt='Cgt',evid='Revid') )) )(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixD3<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixD3,Cgt) add.motherchild.likd.node(mixD3,Cgt,Mgt,db,1) log10LR<-(logLX(mixD3, expr.make.findings(list( list('Male',ind=1), list('Rlikd',aca='cgtcaca',cgt='Cgt',evid='Revid') )) )(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')
loop over markers, and alleles within markers to create node Rlikd for relative likelihood for individual i, for paternity model with only Child genotyped then compile all domains. Implements method ALN.
add.relative.likd.node(mixture,aca,ind=1)add.relative.likd.node(mixture,aca,ind=1)
mixture |
A compiled DNAmixture object |
aca |
Child's genotype profile as an allele count array |
ind |
Index of contributor regarded as Parent (or Child): which ‘unknown’ contributor are we modelling by amending his/her CPTs? |
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixALN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixALN,Cgt) add.relative.likd.node(mixALN,cgtcaca,1) log10LR<-(logLX(mixALN, expr.make.findings(list( list('Male',ind=1), list('Rlikd',aca='cgtcaca',cgt='Cgt',evid='Revid') )) )(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixALN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixALN,Cgt) add.relative.likd.node(mixALN,cgtcaca,1) log10LR<-(logLX(mixALN, expr.make.findings(list( list('Male',ind=1), list('Rlikd',aca='cgtcaca',cgt='Cgt',evid='Revid') )) )(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')
Extract genotype profile for a single contributor from rGTs output
as.gt(res,ind)as.gt(res,ind)
res |
Output from rGTs |
ind |
Integer, which individual's genotype profile should be extracted |
Data frame, genotype profile for selected individual, for format see formats.
Peter Green ([email protected])
Check whether database has positive frequencies for all peaks/alleles observed in epg and genotype profiles, and optionally modify db by addition of small positive frequencies so that it does, followed by renormalisation of frequencies for each allele to sum to 1.
checkpeaks(x,db,fix=0.003)checkpeaks(x,db,fix=0.003)
x |
data frame, the epg or genotype profile; see |
db |
data frame, the db; see |
fix |
numeric: if positive, increment to db frequency for each identified discrepant peak |
(possibly modified) db
Peter Green ([email protected])
data(test2data) db<-checkpeaks(epg,db) db<-checkpeaks(Cgt,db) Xgt<-data.frame(marker=c('D10','D12'),allele1=c(8,13),allele2=c(13,10)) db<-checkpeaks(Xgt,db) dbdata(test2data) db<-checkpeaks(epg,db) db<-checkpeaks(Cgt,db) Xgt<-data.frame(marker=c('D10','D12'),allele1=c(8,13),allele2=c(13,10)) db<-checkpeaks(Xgt,db) db
Construct IBD pattern distribution from one of several alternative representations of multi-person condensed coefficients of identity
as.IBD(x='sibs', targets=NULL, ped=FALSE) convertIBD(x='sibs', targets=NULL, ped=FALSE)as.IBD(x='sibs', targets=NULL, ped=FALSE) convertIBD(x='sibs', targets=NULL, ped=FALSE)
x |
A string, a vector of length 3 or 9, a list with components |
targets |
character vector of individual tags |
ped |
logical, should complete pedigree be added as an attribute to the output, if available? |
Possible formats for the input x are:
certain verbal mnemonics; currently one of the following (or an unambiguous partial match): c('sibs','parent-child','half-sibs', 'cousins','half-cousins','second-cousins', 'double-first-cousins', 'quadruple-half-first-cousins', '3cousins-cyclic','3cousins-star','trio')
a vector of 3 kappas
a vector of 9 Deltas
a list with matrix or vector valued component patt, with or without component pr
a list with 2 components, the first being a pedigree in the sense of the pedtools package, the second a vector of target id's
a 3-column character matrix of individual tags, each row denoting a child/mother/triple - an alternative compact representation of a pedigree
IBD pattern distribution as a list with components pr and patt
Peter Green ([email protected])
data(test2data) IBD<-convertIBD('parent-child') IBD<-convertIBD(c(0.5,0.5,0.0))data(test2data) IBD<-convertIBD('parent-child') IBD<-convertIBD(c(0.5,0.5,0.0))
Delete D and Q dummy nodes and associated edges from all Bayes nets in mixture, to save space; these nodes would only be needed for specific follow-up analyses
delete.DQnodes(mixture,which="DQ")delete.DQnodes(mixture,which="DQ")
mixture |
A compiled DNAmixture object |
which |
character string |
The function removes the D and/or Q nodes from the DNAmixture object, depending on whether which includes "D", "Q" or both
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) delete.DQnodes(mixD) replace.tables.for.UAF(mixD,40) size(mixD)data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) delete.DQnodes(mixD) replace.tables.for.UAF(mixD,40) size(mixD)
IBD pattern distribution in the Iulius-Claudius pedigree
data("emperors")data("emperors")
IBD object
data(emperors)data(emperors)
Returns an expression that will be evaluated in logL.UKX whenever the likelihood of the model is calculated using the current method, and encodes the additional findings needed to implement the method; the details of the model and the extra information needed are held in the list z
expr.make.findings(z)expr.make.findings(z)
z |
A list specifying the additional findings; for the format, see Details |
Each component of the list z is a list encoding a particular type of additional finding: the first component of this (sub-)list being a character string specifying the type of finding, and the remainder of its components being named parameters giving details of the finding. The types of finding and the valid parameters of each are as follows:
Maleind: index of relevant contributor: which ‘unknown’ contributor are we modelling by amending his CPTs?
Femaleind: index of relevant contributor
Rlikdaca: allele count array, cgt: character string naming genotype profile data frame, evid: character string naming list with one component for each marker, whose value is the evidence
Acaind: index of relevant contributor, aca: allele count array
Cacaind: index of relevant contributor, aca: allele count array
Denomno parameters
If z is NULL, then there are no additional findings.
Expression encoding the additional findings.
Peter Green ([email protected])
Formats for data objects in KinMix and KinMixLite
An allele frequency database is a data frame containing variables marker, allele and frequency (character, numeric and numeric respectively).
A mixture profile is a data frame containing variables marker, allele and height (character, numeric and numeric respectively).
A genotype profile is a data frame containing variables marker, allele1 and allele2 (character, numeric and numeric respectively).
Examples of these 3 data formats are objects db, epg and Cgt, respectively, in test2data.
A allele count array is an alternative format for a genotype as a named list of vectors, one for each marker. Each vector gives the number of each allele in the genotype, with the alleles listed in the order in which they appear in the data component of the relevant mixture object.
An IBD pattern distribution or IBD object is a list with components pr (a numerical vector) and patt (an integer matrix with nrow(patt)==length(pr) and an even number of columns). The elements of pr are the probabilities of the IBD patterns in the corresponding rows of patt. Adjacent pairs of columns encode the genotypes of different individuals; equal elements in any row determine equality of the alleles; different elements denote independent draws from the gene pool. If the component pr is missing, functions rpt.IBD and rpt.typed.relatives assume the probabilities are equal.
Peter Green ([email protected])
Returns list of vectors of allele counts corresponding to genotype profile in gt
gt2aca(mixture,gt,eps=0)gt2aca(mixture,gt,eps=0)
mixture |
A compiled DNAmixture object |
gt |
Genotype profile as a data frame containing variables |
eps |
If non-zero, the function creates the output allele count arrays in a different format, that mitigates subsequent propagation errors in some situations. Instead of a vector of allele counts, each element of the list is a matrix with 3 columns, corresponding to allele counts 0, 1 and 2, with entries 1 or |
Returns list of vectors of allele counts. The log probability for the genotype is returned in its attribute 'logGt'.
Peter Green ([email protected])
data(test2data) data(NGMDyes) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,dyes=list(NGMDyes)) cgtcaca<-gt2aca(mixD,Cgt) print(Cgt) print(cgtcaca)data(test2data) data(NGMDyes) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db,dyes=list(NGMDyes)) cgtcaca<-gt2aca(mixD,Cgt) print(Cgt) print(cgtcaca)
Edit output from rGTs to omit individuals with NA amounts of DNA
intoMix(res)intoMix(res)
res |
Output from |
The edited data structure
Peter Green ([email protected])
Replacement for logL.UK in DNAmixtures that calls extra.findings immediately before
propagating all findings and returning the normalising constant for the network.
logL.UKX(mixture, expr.extra.findings, initialize = FALSE)logL.UKX(mixture, expr.extra.findings, initialize = FALSE)
mixture |
Compiled DNAmixture object. |
expr.extra.findings |
expression containing the extra findings |
initialize |
should all entered evidence be removed from the networks in |
The log likelihood.
Peter Green ([email protected])
See also logL.UK.
data(test2data) # set threshold C C<-0.001 pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.9,U2=0.1))) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) logL.UKX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )))(pars)data(test2data) # set threshold C C<-0.001 pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.9,U2=0.1))) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) logL.UKX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )))(pars)
Replacement for logL in DNAmixtures that calls calls LogL.UKX instead of logL.UK.
logLX(mixture, expr.extra.findings, presence.only = FALSE, initialize = FALSE)logLX(mixture, expr.extra.findings, presence.only = FALSE, initialize = FALSE)
mixture |
Compiled DNAmixture object. |
expr.extra.findings |
expression containing the extra findings |
presence.only |
Set to TRUE to ignore peak heights and evaluate the likelihood function considering peak presence and absence (heights above and below threshold) only. Defaults to FALSE |
initialize |
should all entered evidence be removed from the networks in |
The log likelihood.
Peter Green ([email protected])
See also logL.
data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) log10LR<-(logLX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )) )(pars)-attr(cgtcaca,'logGt')-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) mixMBN<-DNAmixture(list(epg),k=2,C=list(C),database=db,triangulate=FALSE,compile=FALSE) cgtcaca<-gt2aca(mixMBN,Cgt) add.child.meiosis.nodes(mixMBN,cgtcaca,1) log10LR<-(logLX(mixMBN, expr.make.findings(list( list('Male',ind=1), list('Caca',aca='cgtcaca') )) )(pars)-attr(cgtcaca,'logGt')-baseline)/log(10) cat('log10 LR',log10LR,'\n')
Analysis of DNA mixtures with familial relationships, by looping over traces, markers, and IBD patterns, to reduce total BN table size, at some price in execution time
loop.rpt.IBD(listdata, pars, IBD, typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets = NULL, contribs, quiet=FALSE, verbose=FALSE, presence.only=FALSE, ...)loop.rpt.IBD(listdata, pars, IBD, typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets = NULL, contribs, quiet=FALSE, verbose=FALSE, presence.only=FALSE, ...)
listdata |
as in call to DNAmixture |
pars |
parameter structure, in |
IBD |
multi-person coefficients of identity, in any of the formats accepted by |
typed.gts, inds, jtyped, jcontr, targets, contribs, quiet
|
as in call to rpt.IBD |
verbose |
should per-marker and overall log10LR's be reported? |
presence.only |
Set to TRUE to ignore peak heights and evaluate the likelihood function considering peak presence and absence (heights above and below threshold) only. Defaults to FALSE. |
... |
other arguments to DNAmixture, particularly including |
The value of the overall log10 LR, and the contributions of individual markers in the form of a vector-valued attribute 'log10LR', are returned invisibly; individual marker/pattern values are also printed out.
Peter Green ([email protected])
data(test2data) data(NGMDyes) C<-0.001 ## Fit 3-person mixture - baseline model mixD<-DNAmixture(list(epg),k=3,C=rep(list(C),length(list(epg))),database=db) pars3<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.6,U2=0.3,U3=0.1))) baseline3<-logL(mixD)(pars3) size(mixD) ## Fit 3-person mixture - in which U1 and U2 have a parent-child relationship mixD<-DNAmixture(list(epg),k=3,C=rep(list(C),length(list(epg))),database=db, triangulate=FALSE,compile=FALSE) delete.DQnodes(mixD) rpt.IBD(mixD,IBD=c(0,1,0),typed.gts=list(),inds=1:2,jtyped=NULL) size(mixD) log10LR<-(logL(mixD)(pars3)-baseline3)/log(10) cat('log10 LR',log10LR,'\n') ## the same analysis by loop.rpt.IBD listdata<-list(epg) print(loop.rpt.IBD(listdata,pars3,IBD=c(0,1,0), k=3,C=rep(list(C),length(listdata)),database=db, typed.gts=list(),inds=1:2,jtyped=NULL))data(test2data) data(NGMDyes) C<-0.001 ## Fit 3-person mixture - baseline model mixD<-DNAmixture(list(epg),k=3,C=rep(list(C),length(list(epg))),database=db) pars3<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.6,U2=0.3,U3=0.1))) baseline3<-logL(mixD)(pars3) size(mixD) ## Fit 3-person mixture - in which U1 and U2 have a parent-child relationship mixD<-DNAmixture(list(epg),k=3,C=rep(list(C),length(list(epg))),database=db, triangulate=FALSE,compile=FALSE) delete.DQnodes(mixD) rpt.IBD(mixD,IBD=c(0,1,0),typed.gts=list(),inds=1:2,jtyped=NULL) size(mixD) log10LR<-(logL(mixD)(pars3)-baseline3)/log(10) cat('log10 LR',log10LR,'\n') ## the same analysis by loop.rpt.IBD listdata<-list(epg) print(loop.rpt.IBD(listdata,pars3,IBD=c(0,1,0), k=3,C=rep(list(C),length(listdata)),database=db, typed.gts=list(),inds=1:2,jtyped=NULL))
Convert genotype profile to reference profile format
make.profile(gt,name='K')make.profile(gt,name='K')
gt |
genotype profile |
name |
character string used to name profile in output data frame |
data frame containing reference profile
Peter Green ([email protected])
data(test2data) S1prof<-make.profile(S1gt,'S1') C<-0.001 mixD<-DNAmixture(list(epg),k=3,K='S1',reference.profile=S1prof,C=list(C),database=db)data(test2data) S1prof<-make.profile(S1gt,'S1') C<-0.001 mixD<-DNAmixture(list(epg),k=3,K='S1',reference.profile=S1prof,C=list(C),database=db)
Replacement for mixML in DNAmixtures that calls logLX instead of logL.
mixMLX(mixture, expr.extra.findings, pars, constraints = NULL, phi.eq = FALSE, val = NULL, trace = FALSE, order.unknowns = TRUE, initialize = FALSE, ...)mixMLX(mixture, expr.extra.findings, pars, constraints = NULL, phi.eq = FALSE, val = NULL, trace = FALSE, order.unknowns = TRUE, initialize = FALSE, ...)
mixture |
Compiled DNAmixture object. |
expr.extra.findings |
expression containing the extra findings |
pars |
Parameters, in |
constraints |
as in |
phi.eq |
as in |
val |
as in |
trace |
as in |
order.unknowns |
as in |
initialize |
should all entered evidence be removed from the networks in |
... |
as in |
A list containing
The (suggested) MLE.
The log of the likelihood (log e).
as well as the output from the optimisation.
Peter Green ([email protected])
See also mixML.
data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) # find MLE's and maximised likelihood # adding evidence individual 1 is Male expr.extra.findings<-expr.make.findings(list(list('Male',ind=1))) startpar<-mixpar(rho=list(60),eta=list(24),xi=list(0.16),phi=list(c(U1=0.75,U2=0.25))) mlDM<-mixMLX(mixD,expr.extra.findings,startpar,trace=FALSE) pars<-mlDM$mle cat('\nBaseline model maximised likelihood:',mlDM$lik,'\n') cat('and MLEs:\n') print(mlDM$mle)data(test2data) # set threshold C C<-0.001 mixD<-DNAmixture(list(epg),k=2,C=list(C),database=db) # find MLE's and maximised likelihood # adding evidence individual 1 is Male expr.extra.findings<-expr.make.findings(list(list('Male',ind=1))) startpar<-mixpar(rho=list(60),eta=list(24),xi=list(0.16),phi=list(c(U1=0.75,U2=0.25))) mlDM<-mixMLX(mixD,expr.extra.findings,startpar,trace=FALSE) pars<-mlDM$mle cat('\nBaseline model maximised likelihood:',mlDM$lik,'\n') cat('and MLEs:\n') print(mlDM$mle)
Construct IBD pattern distribution from a pedigree and a target list of individuals
pedigreeIBD(x, targets, cond = TRUE, ped=FALSE, quiet = TRUE, verbose = FALSE)pedigreeIBD(x, targets, cond = TRUE, ped=FALSE, quiet = TRUE, verbose = FALSE)
x |
A pedigree in |
targets |
Character vector, some or all of the individual identifiers in the pedigree |
cond |
should IBD pattern be condensed? |
ped |
logical, should complete pedigree be added as an attribute to the output, if available? |
quiet |
should resulting IBD pattern distribution be printed? |
verbose |
should trace information be printed? |
This function computes the multi-person condensed coefficients of identity for an arbitrary set of individuals, in the sparse notation of the IBD pattern distribution of Green and Vigeland (2019).
IBD pattern distribution as a list with components pr and patt
Peter Green ([email protected])
Multi-person condensed coefficients of identity, by Peter J. Green and Magnus Dehli Vigeland, University of Bristol technical report, 2019.
require(ribd) id<-c('gf','gm','b1','b2','m','c') fid<-c(0,0,'gf','gf',0,'b1') mid<-c(0,0,'gm','gm',0,'m') sex<-c(1,2,1,1,2,0) x<-ped(id,fid,mid,sex) IBD<-pedigreeIBD(x,c('m','c','b1','b2')) kappaIBD(x,c('m','c','b1','b2'))require(ribd) id<-c('gf','gm','b1','b2','m','c') fid<-c(0,0,'gf','gf',0,'b1') mid<-c(0,0,'gm','gm',0,'m') sex<-c(1,2,1,1,2,0) x<-ped(id,fid,mid,sex) IBD<-pedigreeIBD(x,c('m','c','b1','b2')) kappaIBD(x,c('m','c','b1','b2'))
Plot IBD patterns and pattern distributions
## S3 method for class 'IBD' plot(x,labels=NULL,probs=NULL,order=NULL,colrs=c('black','red','blue'), digits=3,nr=ceiling(sqrt(np)),...)## S3 method for class 'IBD' plot(x,labels=NULL,probs=NULL,order=NULL,colrs=c('black','red','blue'), digits=3,nr=ceiling(sqrt(np)),...)
x |
A matrix whose rows are IBD patterns, or a list whose components are |
labels |
Vector of numerical or character labels for the patterns, if |
probs |
Vector of probabilities of the patterns, if not provided as a component of |
order |
A character string, partially matched using |
colrs |
A vector of colours: ties in the ordering variable are indicated by coloured groups, with colours chosen cyclically from this vector. |
digits |
Integer, overwriting default number of significant digits for |
nr |
Integer, overwriting default number of rows for plotted array, default a rounding up of the square root of the number of patterns. |
... |
additional arguments to |
No value is returned, the function is called for its side effect, a plot on the current display device.
Peter Green ([email protected])
require(ribd) data(emperors) plot.IBD(convertIBD('3cousins-star'),order='probs',col=c('blue','red','black')) plot(attr(emperors,'ped')) o<-order(emperors$pr)[1:12] plot.IBD(emperors$patt[o,],probs=emperors$pr[o],labels=NA,order='probs')require(ribd) data(emperors) plot.IBD(convertIBD('3cousins-star'),order='probs',col=c('blue','red','black')) plot(attr(emperors,'ped')) o<-order(emperors$pr)[1:12] plot.IBD(emperors$patt[o,],probs=emperors$pr[o],labels=NA,order='probs')
Attempts to catch numerical erros in evaluating the expression x, delivering a default result instead of NaN or other failures
protected(x,default=-Inf)protected(x,default=-Inf)
x |
expression to be evaluated, typically the log-likelihood of a modified mixture model |
default |
value to be delivered if numerical errors are encountered |
Returns -Inf in case of error, otherwise the value of x
Peter Green ([email protected])
Attempts to catch numerical errors in maximum likelihood computation, by replacing logL values by a default value instead of NaN or other failures
protected.mixML(mixture, pars, constraints = NULL, phi.eq = FALSE, val = NULL, trace = FALSE, order.unknowns = TRUE, default=-999999, ...)protected.mixML(mixture, pars, constraints = NULL, phi.eq = FALSE, val = NULL, trace = FALSE, order.unknowns = TRUE, default=-999999, ...)
mixture |
A DNAmixture object. |
pars |
A mixpar parameter used as a starting value for the optimisation. |
constraints |
Equality constraint function as function of an array of parameters. |
phi.eq |
Should the mixture proportions be the same for all traces? Defaults to FALSE. |
val |
Vector of values to be satisfied for the equality constraints. |
trace |
Print the evaluations of the likelihood-function during optimisation? |
order.unknowns |
Should unknown contributors be ordered according to decreasing contributions? Defaults to TRUE. |
... |
Further arguments to be passed on to solnp. |
default |
value of logL to be used if numerical errors are encountered |
A list containing
mle |
The (suggested) MLE. |
lik |
The log of the likelihood (log e). |
as well as the output from the optimisation.
Peter Green ([email protected])
Scan all Bayes nets in mixture, and compile any that are not already compiled
require.compiled(mixture)require.compiled(mixture)
mixture |
A DNAmixture object |
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) replace.tables.for.UAF(mixD,40,compile=FALSE) require.compiled(mixD)data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) replace.tables.for.UAF(mixD,40,compile=FALSE) require.compiled(mixD)
Simulate random genotype profiles and DNA samples for arbitrarily related individuals
rGTs(nreps,IBD,db,DNA,sex=rep(0,ncontr),nU=0)rGTs(nreps,IBD,db,DNA,sex=rep(0,ncontr),nU=0)
nreps |
Integer, number of replicates |
IBD |
Specification of relationships, as in |
db |
Data frame, database of alleles and their frequencies, for each marker; for format, see |
DNA |
Integer vector, numbers of DNA cells for the respective individuals, can be NA |
sex |
Integer vector, sex of the respective contributors: 1=male, 2=female, 0=unspecified |
nU |
Integer, include also this number of unrelated individuals |
Genotype profiles are generated randomly, using the allele frequency database db, under the relationships specified by the IBD argument. In accordance with the underlying biology, allele values for the AMEL marker (if this is one of the markers included) are not influenced by relationships with other individuals; however they are influenced by the sex of the individuals, where this is known. Information on sex can be specified by the optional argument sex: a male is given the profile X-Y, a female X-X, and an individual with unspecified sex X-X or X-Y with equal probabilities.
Data frame with variables Sim, Sample.name, Marker, Allele, and DNA, suitable for input to simExtraction, etc. See package pcrsim.
Peter Green ([email protected])
data(test2data) data(NGMDyes)data(test2data) data(NGMDyes)
Random number initialiser supporting spontaneous replication
rni(seed=0)rni(seed=0)
seed |
Integer, seed |
This is a convenience front end to set.seed. A non-zero value of seed is passed directly to set.seed. Given a zero value (the default), the function calls Sys.time to generate an unpredictable starting value – but the value ultimately passed to set.seed is both output using cat and returned invisibly, so can be used for unanticipated replica runs of a simulation.
Non-zero seed value that can be used to reproduce run subsequently
Peter Green ([email protected])
rni(0) runif(6) rni(0) runif(6) rni(3456) runif(6) rni(3456) runif(6) keep<-rni(0) print(keep) runif(6) rni(keep) runif(6)rni(0) runif(6) rni(0) runif(6) rni(3456) runif(6) rni(3456) runif(6) keep<-rni(0) print(keep) runif(6) rni(keep) runif(6)
Used after a call to DNAmixture with compile=FALSE,triangulate=FALSE, this function replaces the CPTs for the genotype allele count arrays for the AMEL marker in a DNA mixture to specify sex of contributors
rpt.AMEL(mixture,sex,compile=TRUE)rpt.AMEL(mixture,sex,compile=TRUE)
mixture |
A DNAmixture object |
sex |
Integer vector, sex of each contributor |
compile |
Logical, should BN be compiled after modification? |
The sex of each contributor is coded as in pedtools, namely 0=unspecified, 1=male, 2=female.
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=3,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) rpt.AMEL(mixD,c(1,2,0)) # the 3 contributors are male, female, and unspecified,respectively.data(test2data) data(NGMDyes) mixD<-DNAmixture(list(epg),k=3,C=list(0.001),database=db,dyes=list(NGMDyes), triangulate=FALSE,compile=FALSE) rpt.AMEL(mixD,c(1,2,0)) # the 3 contributors are male, female, and unspecified,respectively.
Used after a call to DNAmixture with compile=FALSE,triangulate=FALSE, this function replaces the CPTs for the genotype allele count arrays for specified mixture contributors by those representing the specified relationship with each other and typed relatives
rpt.IBD(mixture, IBD="parent-child", typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets=attr(IBD,'targets'), contribs=NULL, quiet=FALSE, all.freq = NULL, compile = TRUE) rpt.typed.relatives(mixture, IBD="parent-child", typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets=attr(IBD,'targets'), contribs=NULL, quiet=FALSE, all.freq = NULL, compile = TRUE) rpt.typed.child(mixture, aca, ind=1) replace.Ui.tables(mixture, aca, ind=1) rpt.typed.parents(mixture, Mgt, Fgt, ind=1, compile=TRUE) rpt.typed.relative(mixture, Rgt, IBD=c(0.25,0.5,0.25), ind=1, compile=TRUE)rpt.IBD(mixture, IBD="parent-child", typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets=attr(IBD,'targets'), contribs=NULL, quiet=FALSE, all.freq = NULL, compile = TRUE) rpt.typed.relatives(mixture, IBD="parent-child", typed.gts = NULL, inds = 1, jtyped = ncol(IBD$patt)/2 - length(typed.gts) + seq_along(typed.gts), jcontr = seq_along(inds), targets=attr(IBD,'targets'), contribs=NULL, quiet=FALSE, all.freq = NULL, compile = TRUE) rpt.typed.child(mixture, aca, ind=1) replace.Ui.tables(mixture, aca, ind=1) rpt.typed.parents(mixture, Mgt, Fgt, ind=1, compile=TRUE) rpt.typed.relative(mixture, Rgt, IBD=c(0.25,0.5,0.25), ind=1, compile=TRUE)
mixture |
DNAmixtures object created by previous call to |
IBD |
relationships between the specified individuals, as multi-person condensed coefficients of identity, in one of several representation; see Details. |
typed.gts |
list of 0 or more genotypes of relatives; the components of this list must
be named (with the id's of the relevant individuals) if |
inds |
vector of 1 or more integers: which ‘unknown’ contributors are we modelling by
amending their CPTs? The elements should be listed in the same order as the corresponding
pairs of columns of the IBD patterns in |
jtyped |
indices of pairs of columns of |
jcontr |
indices of pairs of columns of |
targets |
Character vector of the tags of the individuals referred to in |
contribs |
Character vector of the tags of the individuals included in the mixture, in order |
quiet |
should calculated values of inds, jtyped and jcontr be reported? |
all.freq |
alternative allele frequency database(s), see Details. |
compile |
logical flag: should mixture object be compiled on exit? |
ind |
as |
aca, Mgt, Fgt, Rgt
|
individual genotypes, as allele count arrays |
In using rpt.IBD or rpt.typed.relatives (which is identical), the correspondence between mixture contributors, specified relationships, and typed genotype profiles should be specified
either (preferably)
using targets, contribs and through the names of the components in typed.gts,
or (to be deprecated)
with inds, jcontr and jtyped:
the two representations should not be mixed up. If either targets or contribs specified, the former representation is assumed.
Special cases are treated slightly more efficiently:
rpt.typed.child: single contributor, single typed relative, parent or child;
rpt.typed.parents: single contributor, both parents typed;
rpt.typed.relative: single contributor, single typed relative.
Note that IBD$patt always has an even number of columns, two for each individual
in the joint relationship specified; jtyped and jcontr are vectors of
indices of these individuals, i.e. to pairs of adjacent columns of IBD$patt.
Multiple functions in this group can validly be called sequentially (with all but the
last having compile=FALSE) providing they reference different sets of
contributors among the targets, and that these sets are conditionally
independent given the typed genotypes specified.
There are multiple valid representations for relationships in the argument IBD –
as an IBD pattern distribution, via a pedigree, or. in the case of just two individuals.
via either a vector of 3 kappas or 9 Deltas (Jacquard's condensed coefficients of
identity). For full details, see convertIBD.
If IBD is missing, the default value represents parent-child.
In the interests of upward compatibility, in rpt.typed.child and
replace.Ui.tables (which are identical), the argument Cgt can be given as
either a genotype profile data frame, or an allele count array.
By default, the allele frequency database used for the founding genes is that
used when the mixture object is created, in an earlier call to DNAmixture.
A non-null value for the all.freq argument allows the
user to specify alternative database (s) for the founding genes. If its value
is an allele frequency database (in the format specified in
formats) then that database is used for all founding genes; if the
value of the argument is a list of such databases, then component k of the
list is used for allele frequencies for the founding gene labelled k in the
IBD argument. Note that this option allows modelling of mixtures where
different contributors are drawn from different populations, whether or not
there are relationships among individuals.
Vector of marker-specific probabilities of the typed genotypes.
Peter Green ([email protected])
data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model in which contributor 1 is parent of a typed individual Cgt mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,,list(Cgt)) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture model in which contributor 1 is father of a typed individual Cgt ## with mother Mgt mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,,list(Mgt,Cgt)) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture model in which contributors are two parents of a child with ## genotype Cgt, and a parent of one of them has genotype Rgt. Note the encoding of allele ## labels to reduce the complexity of the IBD pattern distribution IBD. IBD<-list(patt=rbind(c(1,3,2,4,1,2,1,5),c(1,3,2,4,1,2,3,5))) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,IBD,list(Cgt,Rgt),1:2) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## the same, with individuals and relationships denoted by character tags mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,IBD,list(c=Cgt,gf=Rgt),targets=c('f','m','c','gf'),contribs=c('f','m')) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model in which contributor 1 is parent of a typed individual Cgt mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,,list(Cgt)) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture model in which contributor 1 is father of a typed individual Cgt ## with mother Mgt mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,,list(Mgt,Cgt)) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## Fit 2-person mixture model in which contributors are two parents of a child with ## genotype Cgt, and a parent of one of them has genotype Rgt. Note the encoding of allele ## labels to reduce the complexity of the IBD pattern distribution IBD. IBD<-list(patt=rbind(c(1,3,2,4,1,2,1,5),c(1,3,2,4,1,2,3,5))) mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,IBD,list(Cgt,Rgt),1:2) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n') ## the same, with individuals and relationships denoted by character tags mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) rpt.IBD(mixD,IBD,list(c=Cgt,gf=Rgt),targets=c('f','m','c','gf'),contribs=c('f','m')) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')
Replace CPTs in a DNA mixture to model uncertainty in allele frequencies
replace.tables.for.UAF(mixture, M, compile = TRUE) rpt.UAF(mixture, M, compile = TRUE)replace.tables.for.UAF(mixture, M, compile = TRUE) rpt.UAF(mixture, M, compile = TRUE)
mixture |
DNAmixtures object created by previous call to |
M |
Size of allele frequency database |
compile |
logical flag: should mixture object be compiled on exit? |
No value is returned, the function is called for its side effect
Peter Green ([email protected])
data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model under assumption that database size was only 40 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) replace.tables.for.UAF(mixD,40) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3))) baseline<-logL(mixD)(pars) ## Fit 2-person mixture model under assumption that database size was only 40 mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db,triangulate=FALSE,compile=FALSE) replace.tables.for.UAF(mixD,40) log10LR<-(logL(mixD)(pars)-baseline)/log(10) cat('log10 LR',log10LR,'\n')
Calculate and display total size of BN tables for a DNA mixture
size(mixture)size(mixture)
mixture |
A compiled DNAmixture object |
Returns total size, typically to be printed by bespoke method
Peter Green ([email protected])
data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) size(mixD)data(test2data) data(NGMDyes) ## Fit 2-person mixture - baseline model mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db) size(mixD)
Small test data set (2 markers with 4 or 5 alleles each, plus AMEL), for demonstrating some capabilities of KinMix and KinMixLite
data("test2data")data("test2data")
Data objects for demonstrating KinMix:
epg (DNAmixtures peak height data),
db (DNAmixtures allele frequency database),
and Cgt, Fgt, Mgt, Rgt, S1gt, S2gt potential relative genotype data frames.
data(test2data)data(test2data)
Computes overall LR from Ugt-specific LR's using estimated Ugt genotype
profile in sep corresponding to contributor i in the mixture as Father; uses
Child genotype information in Cgt data.frame and optionally Mother's genotype in Mgt.
Implements method WLR.
wlr(sep, Cgt, db, ind=1, Mgt=NULL)wlr(sep, Cgt, db, ind=1, Mgt=NULL)
sep |
Separation, a list of configurations of genotypes for some or all unknown contributors, output by |
Cgt |
Child's genotype profile as a data frame containing variables |
db |
Allele frequency database |
ind |
Index of contributor regarded as Father |
Mgt |
(optionally) Mother's genotype profile as a data frame containing variables |
Returns LR for paternity
Peter Green ([email protected])
See also map.genotypes.
data(test2data) data(NGMDyes) # set threshold C C<-0.001 pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.9,U2=0.1))) mixWLR<-DNAmixture(list(epg),k=2,C=list(C),database=db,dyes=list(NGMDyes)) setPeakInfo(mixWLR,pars) sepWLR<-map.genotypes(mixWLR,type="all",pmin=0.001,U=1) LR<-wlr(sepWLR,Cgt,db) cat('\nWLR LR:',LR,'; log10(LR):',log10(LR),'\n')data(test2data) data(NGMDyes) # set threshold C C<-0.001 pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.9,U2=0.1))) mixWLR<-DNAmixture(list(epg),k=2,C=list(C),database=db,dyes=list(NGMDyes)) setPeakInfo(mixWLR,pars) sepWLR<-map.genotypes(mixWLR,type="all",pmin=0.001,U=1) LR<-wlr(sepWLR,Cgt,db) cat('\nWLR LR:',LR,'; log10(LR):',log10(LR),'\n')