MCMC {Geneland} | R Documentation |
Markov Chain Monte-Carlo inference under various models
MCMC( coordinates=NULL, genotypes,## ploidy=2, dominance="Codominant", allele.numbers, # path to output directory path.mcmc, # hyper-prior parameters rate.max,delta.coord=0,shape1=2,shape2=20, npopmin=1,npopinit,npopmax, # dimensions nb.nuclei.max, # options in mcmc computations nit,thinning=1,freq.model="Uncorrelated", varnpop=TRUE, spatial=TRUE, jcf=TRUE, filter.null.alleles=TRUE, prop.update.cell=0.1, write.rate.Poisson.process=FALSE, write.number.nuclei=TRUE, write.number.pop=TRUE, write.coord.nuclei=TRUE, write.color.nuclei=TRUE, write.freq=TRUE, write.ancestral.freq=TRUE, write.drifts=TRUE, write.logposterior=TRUE, write.loglikelihood=TRUE, write.true.coord=TRUE, write.size.pop=FALSE, miss.loc)
coordinates |
Spatial coordinates of individuals. A matrix with 2 columns and one line per individual. |
genotypes |
Genotypes of individuals. A matrix with dimension as
described below.
For diploid organisms at codominant markers: a matrix with one line per individual and two columns per locus. For diploid organisms at dominant markers: a matrix with one line per individual and one column per locus. Presence/absence of a band should be coded as 0/1 (0 for absence / 1 for presence). For haploid organisms: a matrix with one line per individual and one column per locus. |
ploidy |
An integer equal to 1 for haploid data and 2 for diploid data. |
dominance |
A character string that should be either "Codominant" (default) for codominant markers such as microsatellites and SNPs or "Dominant" for AFLPs. |
allele.numbers |
Vector of integers giving the number of observed alleles at each locus. Only for debugging. Should be left unspecified. |
path.mcmc |
Path to output files directory. It seems that the path should be given in the Unix style even under Windows (use / instead of \). This path *has to* end with a slash (/) (e.g. path.mcmc="/home/me/Geneland-stuffs/") |
rate.max |
Maximum rate of Poisson process (real number >0).
Setting rate.max equal to the number of individuals in the
dataset has proved to be efficient in many cases. |
delta.coord |
Parameter prescribing the amount of unctertainty attached
to spatial coordinates. If delta.coord =0 spatial coordinates are
consiered as true coordinates, if delta.coord >0 it is assumed that observed
coordinates are true coordinates blurred by an additive noise uniform
on a square of side delta.coord centered on 0. |
shape1 |
First parameter in the Beta(shape1,shape2) prior distribution of the drift parameters in the Correlated model. |
shape2 |
Second parameter in the Beta(shape1,shape2) prior distribution of the drift parameters in the Correlated model. |
npopmin |
Minimum number of populations (integer >=1) |
npopinit |
Initial number of populations
( integer sucht that
npopmin =< npopinit =< npopmax ) |
npopmax |
Maximum number of populations (integer >=
npopinit ).
There is no obvious rule to select npopmax ,
it should be set to a value larger than any value that
you can reasonably expect for your data. |
nb.nuclei.max |
Integer: Maximum number of nuclei in the
Poisson-Voronoi tessellation. A good guess consists in setting this
value equal to 3*rate.max . Lower values
can also be used in order to speed up computations. The relevance of
the value set can be
checked by inspection of the MCMC run. The number of tiles should not
go too close to nb.nuclei.max . If it does, you should re-run your
chain with a larger value for nb.nuclei.max . In case of use
of the option SPATIAL=FALSE , nb.nuclei.max should be
set equal to the number of individuals. |
nit |
Number of MCMC iterations |
thinning |
Number of MCMC iterations between two writing steps (if thinning =1, all
states are saved whereas if e.g. thinning =10 only each 10 iteration is saved) |
freq.model |
Character: "Correlated" or "Uncorrelated" (model for
frequencies).
See also details in detail section of Geneland help page. |
varnpop |
Logical: if TRUE the number of class is treated as
unknown and will vary along the MCMC inference, if FALSE it will be
fixed to the initial value npopinit .
varnpop = TRUE *should not* be used in conjunction with
freq.model = "Correlated" as in this case it seems that large numbers
of populations are not penalized enough and there is a serious risk
of inferring spurious sub-populations. |
spatial |
Logical: if TRUE the colored Poisson-Voronoi tessellation is used as a prior for the spatial organisation of populations. If FALSE, all clustering receive equal prior probability. In this case spatial information (i.e coordinates) are not used and the locations of the nuclei are initialized and kept fixed at the locations of individuals. |
jcf |
Logical: if true update of c and f are performed jointly |
filter.null.alleles |
Logical: if TRUE, tries to filter out null
alleles. An extra fictive null allele is created at each locus coding
for all putative null allele. Its frequency is estimated and can be
viewed with function PlotFreq . This option is available only
with freq.model="Uncorrelated" . |
prop.update.cell |
Integer between 0 and 1. Proportion of cell updated. For debugging only. |
write.rate.Poisson.process |
Logical: if TRUE (default) write rate of Poisson process simulated by MCMC |
write.number.nuclei |
Logical: if TRUE (default) write number of nuclei simulated by MCMC |
write.number.pop |
Logical: if TRUE (default) write number of populations simulated by MCMC |
write.coord.nuclei |
Logical: if TRUE (default) write coordinates of nuclei simulated by MCMC |
write.color.nuclei |
Logical: if TRUE (default) write color of nuclei simulated by MCMC |
write.freq |
Logical: if TRUE (default is FALSE) write allele frequencies simulated by MCMC |
write.ancestral.freq |
Logical: if TRUE (default is FALSE) write ancestral allele frequencies simulated by MCMC |
write.drifts |
Logical: if TRUE (default is FALSE) write drifts simulated by MCMC |
write.logposterior |
Logical: if TRUE (default is FALSE) write logposterior simulated by MCMC |
write.loglikelihood |
Logical: if TRUE (default is FALSE) write loglikelihood simulated by MCMC |
write.true.coord |
Logical: if TRUE (default is FALSE) write true spatial coordinates simulated by MCMC |
write.size.pop |
Logical: if TRUE (default is FALSE) write size of populations simulated by MCMC |
miss.loc |
A matrix with nindiv lines and nloc
columns of 0 or 1. For each individual, at each locus it says if the
locus is genuinely missing (no attempt to measure it). This info is
used under the option filterNA=TRUE do decide how a double
missing value should be treated (genuine missing data
or double null allele). |
Successive states of all blocks of parameters are written in files
contained in path.mcmc
and named after the type of parameters they contain.
All parameters processed by function MCMC
are
written in the directory specified by ‘path.mcmc’ as follows:
nit
lines, one line per iteration of the MCMC algorithm)
nb.nuclei.max
coding the class membership of each Voronoi tile.
Vectors of class membership for successive states of the chain are
concatenated in one column. Some entries of the vector containing
clas membership for a current state may have missing values as the
actual number of polygon may be smaller that the maximum number allowed
nb.nuclei.max
.
This file has nb.nuclei.max*chain/thinning
lines
nb.nuclei.max*chain/thinning
lines
and two columns (hor. and vert. coordinates).
nit
lines.
In each line, values of allele frequencies are stored by increasing
allele index and and locus index (allele index varying first).
nallmax*nloc*nit/thinning
lines where nallmax
is the maximum number of alleles over all loci.
delta.coord
to a non zero value).
Gilles Guillot
G. Guillot, Estoup, A., Mortier, F. Cosson, J.F. A spatial statistical model for landscape genetics. Genetics, 170, 1261-1280, 2005.
G. Guillot, Mortier, F., Estoup, A. Geneland : A program for landscape genetics. Molecular Ecology Notes, 5, 712-715, 2005.
Gilles Guillot, Filipe Santos and Arnaud Estoup, Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface Bioinformatics 2008 24(11):1406-1407.