citation("hybridclock")).classify_hybrids(error = "estimate") warns when the estimated genotyping
error rate reaches the top of its range (0.1). With many linked markers,
as in F2 families, extra variation in heterozygosity can be taken for
genotyping error; a fixed rate (the default, 0.005) is then safer.First release: diagnostic markers and hybrid scores, individual classification of recent hybrids, forward simulation of hybrid populations, and estimates of hybrid age (both species and rare species), backcrosses and arrival rate with split-conformal intervals.
New hybridization is estimated in two parts: the probability that the
rarer species is still arriving (arriving), and the rate if it is
(arrival_rate). Reference histories include arrivals that stop or start
partway, and simulate_hybrid_zone() accepts arrival schedules.
The two ages are named hybrid_age_both and hybrid_age_rare.
simulate_hybrid_zone() and build_reference() gain founders (pure,
F1 or first-backcross founders, for experimental populations) and map
(linkage, from a marker map or a range of chromosome numbers and map
lengths; see random_map()).
classify_hybrids() can use the reference allele frequencies of each
marker and estimate the genotyping error rate; classification_power()
gives the expected accuracy of individual classes.
Class shares from classify_hybrids() are part of the population
summaries, and the arrival probability is recalibrated by estimated age;
young swarms are flagged.
estimate_history() gives one answer per population from two tiers:
individual classes when most hybrids are F1s and backcrosses, otherwise
the population clock (column method).
effective_markers() and the effective_markers argument of
classify_hybrids() and estimate_history() correct the classifier for
linked markers.
Heterozygote dropout (het_dropout) and mating by ancestry (assort) in
the simulator and reference; sibling mating now ranges up to 0.9.
With a fixed marker map, genotype switches along chromosomes (junctions)
are summarized; map_scale allows for an uncertain recombination rate.
Markers that are not fixed: hybrid_scores(method = "likelihood")
estimates S and H from reference allele frequencies (Fitzpatrick 2012),
simulate_hybrid_zone() and build_reference() gain freq, and
marker_information() summarizes how informative a marker set is.
Microsatellites: microsat_markers(), hybrid_scores_microsat(),
classify_hybrids_microsat(), and microsat in the simulator and
reference.
The share of early-class hybrids needed for the individual-classes answer is learned from the reference without a fixed cap; with weak markers the classes are never used.
founder_impurity in simulate_hybrid_zone() and build_reference():
founders that carry some of the other species' alleles at the diagnostic
markers, such as cross parents with old introgression.
Intervals of class-based answers include the error of class-based ages seen in the reference, not only the resampling of animals.
The individual-classes answer is chosen by the classes' large errors (90th percentile) on held-out reference histories, needs at least 80% of hybrids in early classes, and its intervals are at least +/-10% wide.
estimate_history() reports information (how much narrower the
hybrid-age interval is than the reference's age range) and flags answers
that mostly reflect the reference's age range; it gains het_dropout for
the individual classes.
founder_samples (build_reference()) and sample_founders
(simulate_hybrid_zone()): references for lab families of F1s or first
backcrosses.
check_markers() flags markers that are not diagnostic in the animals that
actually bred, using likely F1s.
Samples made up mostly of F1s may be dated by their classes even with weak markers, if the reference shows this is accurate (F1 rule).
Intervals are calibrated separately for samples made up mostly of F2-like animals and for all others.
Tutorial: keeping reference ranges generous, breeding design, map length uncertainty, checking markers, and the number of markers needed to date F2 families.
Intervals are now normalized split-conformal by default
(train_clock(intervals = "normalized")): centred on the estimate, scaled
by a forest's prediction of each sample's typical error, and calibrated on
held-out runs. Coverage is even across ages and sample types (F2 families
included). "binned" (earlier behavior, now also by sample type) and
"cqr" (conformalized quantile regression) remain available.
class_settings(): settings of the classes behind the class shares
(error, heterozygote dropout, linked markers, markers that are not fixed),
given to build_reference(), stored with the reference and reused by
estimate_history(), so that the reference and the data are classified
the same way. The markers, effective_markers, het_dropout and
classes arguments of estimate_history() no longer change its answer.
estimate_history() sets aside animals without scores, with a warning.
build_reference() draws heterozygote dropout in every run when the given
range excludes zero.
Example datasets: wolves, chickens, bettongs, possums, carp.