uml {mixstock} | R Documentation |
Find the unconditional maximum likelihood estimate (jointly estimating marker frequencies in sources) of the contributions of different sources to a mixed stock, by either a direct-search or an expectation-maximization method
uml(x, method="direct",optmethod="L-BFGS-B",...) uml.ds(x,grad=uml.grad,start.type="lsolve",fuzz=0,bounds=1e-4,ndepfac=1000,method="L-BFGS-B",debug=FALSE,control=NULL, transf=c("part","full","none"),...) uml.em(x,prec=1e-8,prior=1)
x |
a list with elements mixsamp (a vector of the sampled
markers in the mixed stock) and sourcesamp (a matrix,
with markers in rows and sources in columns, of markers in the
source samples) |
optmethod |
to be passed to optim |
grad |
function giving the gradient of the likelhood |
start.type |
starting values to use: equal (equal
contributions from each source); random (multinomial sample
with equal probabilities); rand2 (sample out of a transformed
normal distribution); a number between 1 and the number of sources;
that source starts with 0.95 contribution and the rest start with
0.05/(R-1); default lsolve , the linear solution to the problem |
fuzz |
min. value (1-min is the max.) for starting contributions |
bounds |
(bounds,1-bounds) are the lower and upper bounds for mle calculations |
ndepfac |
factor for computing numerical derivatives; numerical derivative stepsize is computed as bounds/ndepfac [OBSOLETE with gradient function?] |
method |
optimization method, to be passed to optim |
transf |
transformation |
debug |
produce debugging output? |
control |
other control arguments to optim |
... |
other arguments to mle or optim
(e.g. hessian=FALSE to suppress (slow) hessian calculation,
etc.) |
prec |
precision for determining convergence of EM algorithm |
prior |
prior for EM algorithm |
uml
uses either a direct-search algorithm or an EM
algorithm to find the ML estimate
an object of class mixstock.est
, with elements
fit |
information on the ML fit |
resample |
bootstrap information, if any |
data |
original data used for estimate |
R |
number of sources |
H |
number of markers |
contin |
estimation done on transformed proportions? |
method |
optimization method |
boot.method |
resampling method |
boot.data |
raw resampling information |
gandr.diag |
Gelman-Rubin diagnostic information for MCMC estimates |
prior |
Prior for MCMC estimates |
em |
estimation done by EM algorithm? |
Ben Bolker
true.freq <- matrix(c(0.65,0.33,0.01,0.01, 0.33,0.65,0.01,0.01),ncol=2) true.contrib <- c(0.9,0.1) x <- simmixstock0(true.freq,true.contrib,50,100,1004) uml.est <- uml(x) uml.est uml.emest <- uml.em(x) uml.emest