A model object for analysis of one or more DNA mixtures. For a
brief overview of the package functionality, see
in particular DNAmixtures.
IMPORTANT: This is the DNAmixturesLite package, which is intended as a service to enable users to try DNAmixtures without purchasing a commercial licence for Hugin. When at all possible, we strongly recommend the use of DNAmixtures rather than this lite-version. See https://dnamixtures.r-forge.r-project.org/ for details on both packages.
While the lite-version seeks to provide the full functionality of DNAmixtures, note that computations are much less efficient and that there are some differences in available functionality. Be aware that the present documentation is copied from DNAmixtures and thus may not accurately describe the implementation of this lite-version.
DNAmixture(
data,
k,
C,
database,
K = character(0),
reference.profiles = NULL,
dir = character(0),
domainnamelist = NULL,
load = FALSE,
write = FALSE,
dyes = NULL,
triangulate = TRUE,
compile = TRUE,
compress = TRUE,
use.order = TRUE
)# S3 method for DNAmixture
print(x, ...)
An object of class DNAmixture. This contains amongst other things
The joint set of markers used for the mixtures specified.
For models involving unknown contributors,
a list containing one Bayesian network (hugin.domain) per marker;
see buildMixtureDomains for details on the networks
A list containing for each marker the combined allele frequencies,
peak heights, and reference profiles as produced by DNAmixtureData.
A list containing one data.frame for each DNA
mixture. Note, that in the special case of analysing just one
mixture, this still has to be specified as list(data). Each
dataset should contain variables marker, allele, and
frequency. Optionally, also a column for each reference
profile specified in K.
Number of contributors.
A list of thresholds, one for each mixture.
A data.frame containing at least variables marker, allele, frequency.
Names of reference profiles; these can be chosen freely, but should match (possibly only a subset of) the names specified by the reference profiles.
A data.frame containing allele counts for each reference profile, if not specified in data.
Location of network files if loading or saving the networks.
Names of marker-wise network files (without hkb-extension). Default is the set of markers.
Read networks from disk?
Save networks as hkb files?
A list containing a list of dyes indexed by markers
Triangulate the networks? Default is to triangulate the network using a good elimination order.
Compile the networks?
Compress the network? Defaults to TRUE and is
strongly recommended for models with a large number of
contributors.
Should the default elimination order be used for triangulation? Otherwise the "total.weight" heuristic for triangulation in Hugin is used.
An object of class DNAmixture.
not used.
The names for known contributors can be chosen freely, whereas
unknown contributors are always termed U1,U2, ....
We allow for stutter to an allele one repeat number shorter. The
range of alleles at a marker is defined by the union of alleles
specified though the peak heights, the reference profiles, and the
allele frequencies. Any alleles that are included, but not found
in the database, will be assigned frequency NA, and it is then up
to the user to decide on further actions. If a particular mixture
has no observations at a marker, the heights are set to NA, but if
the mixture has some peaks at that marker, then missing heights
are all set to 0. Note that we hereby cover the possibility that
mixtures are analysed with different kits, and so are observed at
different markers. We do not (readily) allow kits to have
different ranges of possible alleles at one marker.
If amelogenin is included in the analysis, the marker should be
named "AMEL" and an integer coding such as X=0, Y=1, where X is
assigned a lower number than Y, should be used. Note that in terms
of amelogenin, the allele frequencies have a slighly different
interpretation to that for other markers, in that they denote the
probability of having an additional X or Y to the X that
all people have. Thus, a natural choice will be \(p(X)=p(Y)=0.5\),
denoting equal probability of a male or female contributor.
data(MC15, MC18, USCaucasian)
DNAmixture(list(MC15, MC18), C = list(50,50), k = 3, K = c("K1", "K2"),
database = USCaucasian)
DNAmixture(list(MC15, MC18), C = list(50,50), k = 3, K = c("K3", "K1", "K2"),
database = USCaucasian)
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