Description Usage Arguments Details Value Author(s) Source References Examples
Estimates the trace-suspect match probability for a lineage haplotype of STR markers using coalescent theory.
1 2 3 4 5 6 7 8 9 10 11 12 | coalmatchprob(database, haplotype,
reps = 10, burnin = 0, treebetN = 10, Nbetsamp = 10,
muprior = "constant(0.003)", Nprior = "lognormal(9, 1)", alphaprior = NULL,
progress = TRUE, trace = FALSE)
batwing(database,
reps = 10, burnin = 0, treebetN = 10, Nbetsamp = 10,
muprior = "constant(0.003)", Nprior = "lognormal(9, 1)", alphaprior = NULL,
progress = TRUE, trace = FALSE)
## S3 method for class 'batwing'
print(x, ...)
## S3 method for class 'forensicbatwing'
plot(x, ...)
|
database |
Reference STR database. |
haplotype |
Haplotype of the suspect. |
reps |
Number of output lines. |
burnin |
Number of reps to take before starting recording data. |
treebetN |
The number of times that changes to the genealogical tree are attempted before any changes to the hyperparameters are attempted. Thus BATWING outputs are separated by treebetN * Nbetsamp attempted tree updates. |
Nbetsamp |
The number of times that changes to hyperparameters are attempted between outputs. |
muprior |
Either a single prior distribution for the mutation rate or a vector of prior distributions (one for each locus). If only one prior is supplied, the same mutation rate is used for all loci. If one prior per locus is supplied, each locus has its own chain of mutation rates. |
Nprior |
Prior distribution of the effective population size. |
alphaprior |
If NULL, there is no growth (constant population size). If a prior distribution is specified, this gives exponential growth at rate alpha at all times. |
progress |
Whether to print progress or not. |
trace |
Whether to print extra trace information or not. |
x |
A |
... |
Not used |
Note that the batwing
function runs a standard coalescent inference as described in I.J. Wilson (1999, 2003).
Note that, in contrast to the original BATWING program, migration is not supported. Neither is BATWING's sizemodel=2
(constant-sized population up to a time from where there is exponential growth).
Valid prior distributions:
uniform(v1, v2)
uniform on the interval (v1, v2)
.
constant(v1)
constant value v1
.
normal(v1, v2)
Normal distribution with mean = v1
and sd = v2
.
lognormal(v1, v2)
If X
has this distribution then log(X)
has the normal(v1, v2)
distribution.
gamma(v1, v2)
Gamma distributinon with shape v1
and rate v2
giving mode = (v1-1)/v2
and mean = v1/v2
.
beta(v1, v2)
Beta distribution with shape parameters v1
and v2
giving mean = v1/(v1 + v2)
and the variance is (v1*v2)/((v1 + v2)^2 * (v1 + v2 + 1))
coalmatchprob |
An object of type |
batwing |
An object of type |
batwing-object |
|
Mikkel Meyer Andersen and Ian Wilson
BATWING at Ian Wilson's homepage
I.J. Wilson, D.J. Balding, Genealogical inference from microsatellite data, Genetics 150 (1998) 499-510.
I.J. Wilson, M.E. Weale, D.J. Balding, Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities, J. R. Stat. Soc. Ser. A 166 (2003) 155-201.
M.M. Andersen, A. Caliebe, A. Jochens, S. Willuweit, M. Krawczak, Estimating trace-suspect match probabilities for singleton Y-STR haplotypes using coalescent theory, Forensic Sci. Int. Genet. (In Press, Corrected Proof 10.1016/j.fsigen.2012.11.004.).
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 | ## Not run:
database <- matrix(c(1, 1, 2, 2, 1, 3), 3, 2)
haplotype <- c(1, 1)
# coalmatchprob:
coalmp <- coalmatchprob(database, haplotype,
reps = 1000, burnin = 0, treebetN = 10, Nbetsamp = 10,
muprior = c("normal(0.003, 0.001)", "normal(0.005, 0.001)"),
Nprior = "lognormal(9, 1)",
alphaprior = NULL,
progress = TRUE, trace = FALSE)
coalmp
murange <- range(c(coalmp$result$mu1, coalmp$result$mu2))
par(mfrow = c(2, 2))
plot(coalmp)
plot(coalmp$result$N, type = "l", ylab = "N")
plot(coalmp$result$mu1, type = "l", col = "red", ylim = murange, ylab = "mu")
points(coalmp$result$mu2, type = "l", col = "blue")
hist(coalmp$result$mu1, col = "#FF000066",
xlim = murange, ylim = c(0, 250), main = NULL, xlab = "mu")
hist(coalmp$result$mu2, add = TRUE, col = "#0000FF66")
par(mfrow = c(1, 1))
# batwing:
bw <- batwing(database,
reps = 10000, burnin = 1000, treebetN = 10, Nbetsamp = 10,
muprior = "normal(0.003, 0.001)",
Nprior = "lognormal(9, 1)",
alphaprior = NULL,
progress = TRUE, trace = FALSE)
bw
## End(Not run)
|
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