Description Usage Arguments Value Storage format Author(s) References See Also
Markov Chain MonteCarlo inference of clusters from genotpype data
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 38 39 40 41 42 43 44 45 46 47 48  MCMC(
## input data
coordinates=NULL, # spatial coordinates
geno.dip.codom=NULL, # diploid codominant markers
# one line per indiv.
# two column per marker
geno.dip.dom=NULL, # diploid dominant markers
# one line per indiv.
# one column per marker
geno.hap=NULL, # haploid
# one line per indiv.
# one column per marker
qtc, # quantitative continuous variables
qtd, # quantitative discrete variables
ql, # qualitative variables
## path to output directory
path.mcmc,
## hyperprior parameters
rate.max,delta.coord=0,shape1=2,shape2=20,
npopmin=1,npopinit,npopmax,
## dimensions
nb.nuclei.max,
## mcmc computations options
nit,
thinning=1,
freq.model="Uncorrelated",
varnpop=TRUE,
spatial=TRUE,
jcf=TRUE,
filter.null.alleles=TRUE,
prop.update.cell=0.1,
## writing mcmc output files options
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,
write.mean.quanti=TRUE,
write.sd.quanti=TRUE,
write.betaqtc=FALSE,
miss.loc=NULL)

coordinates 
Spatial coordinates of individuals. A matrix with 2 columns and one line per individual. 
geno.dip.codom 
Genotypes for diploid data with codominant markers.
A matrix with one line per individual and two columns per locus.
Note that the object has to be of type matrix not table. This can be
forced by function 
geno.dip.dom 
Genotypes for diploid data with 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). Dominant and
codominant markers can be analyzed jointly by passing variables to arguments
geno.dip.codom and geno.dip.dom.
Haploid data and diploid dominant data can not be analyzed jointly in
the current version.
Note that the object has to be of type matrix not table. This can be
forced by function 
geno.hap 
Genotypes of haploid data.
A matrix with one line per individual and one column per
locus. Dominant diploid data and haploid data
can be analyzed jointly (e.g. to analyse microsatelite data or SNP
data together with mtDNA.
Haploid data and diploid dominant data can not be analyzed jointly in
the current version.
Note that the object has to be of type matrix not table. This can be
forced by function 
qtc 
A matrix of continuous quantitative phenotypic variables. One line per individual and one column per
phenotypic variable.
Note that the object has to be of type matrix not table. This can be
forced by function 
qtd 
A matrix of discrete quantitative phenotypic variables. NOT IMPLEMENTED YET 
ql 
A matrix of categorical phenotypic variables. NOT IMPLEMENTED YET 
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/Genelandstuffs/") 
rate.max 
Maximum rate of Poisson process (real number >0).
Setting 
delta.coord 
Parameter prescribing the amount of uncertainty attached
to spatial coordinates. If 
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

npopmax 
Maximum number of populations (integer >=

nb.nuclei.max 
Integer: Maximum number of nuclei in the
PoissonVoronoi tessellation. A good guess consists in setting this
value equal to 
nit 
Number of MCMC iterations 
thinning 
Number of MCMC iterations between two writing steps (if 
freq.model 
Character: "Correlated" or "Uncorrelated" (model for
frequencies).
See also details in detail section of 
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 
spatial 
Logical: if TRUE the colored PoissonVoronoi 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 
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 
write.mean.quanti 
Logical: if TRUE (default is FALSE) write means of quantitatives variables in the various groups simulated by MCMC 
write.sd.quanti 
Logical: if TRUE (default is FALSE) write standard deviations of quantitatives variables in the various groups simulated by MCMC 
write.betaqtc 
Logical: if TRUE (default is FALSE) write hyperparameter beta of distribution of quantitatives variables simulated by MCMC 
miss.loc 
A matrix with 
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:
File ‘population.numbers.txt’ contains values of the number of
populations (nit
lines, one line per iteration of the MCMC
algorithm).
File ‘population.numbers.txt’ contains values of the number of
populations (nit
lines, one line per iteration of the MCMC algorithm).
File ‘nuclei.numbers.txt’ contains the number of points in the Poisson point process generating the Voronoi tessellation.
File ‘color.nuclei.txt’ contains vectors of integers of
length 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.
File ‘coord.nuclei.txt’ contains coordinates of points in the Poisson
point process generating the Voronoi tessellation. It has
nb.nuclei.max*chain/thinning
lines
and two columns (hor. and vert. coordinates).
File ‘drifts.txt’ contains the drift factors for each population, (one column per population).
File ‘ancestral.frequencies.txt’ contains allele frequencies in ancestral
population. Each line contains all frequencies of the current state.
The file has nit
lines.
In each line, values of allele frequencies are stored by increasing
allele index and and locus index (allele index varying first).
File ‘frequencies.txt’contains allele frequencies of present time
populations. Column xx contains frequencies of population numer xx.
In each column values of allele frequencies are stored by increasing
allele index and and locus index (allele index varying first), and
values of successive iterations are pasted.
The file has nallmax*nloc*nit/thinning
lines where nallmax
is the maximum number of alleles over all loci.
File ‘Poisson.process.rate.txt’ contains rates of Poisson process.
File ‘hidden.coord.txt’ contains the coordinates of each
individual as updated along the chain if those given as input are not
considered as exact coordinates (which is specified by
delta.coord
to a non zero value).
File ‘log.likelihood.txt’ contains loglikelihood of data for the current state of parameters of the Markov chain.
File ‘log.posterior.density.txt’ contains log of posterior probability (up to marginal density of data) of the current state of parameters in the Markov chain.
Gilles Guillot
G. Guillot, Estoup, A., Mortier, F. Cosson, J.F. A spatial statistical model for landscape genetics. Genetics, 170, 12611280, 2005.
G. Guillot, Mortier, F., Estoup, A. Geneland : A program for landscape genetics. Molecular Ecology Notes, 5, 712715, 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):14061407.
G. Guillot. Inference of structure in subdivided populations at low levels of genetic differentiation. The correlated allele frequencies model revisited. Bioinformatics, 24:22222228, 2008
G. Guillot and F. Santos A computer program to simulate multilocus genotype data with spatially autocorrelated allele frequencies. Molecular Ecology Resources, 2009
G. Guillot, R. Leblois, A. Coulon, A. Frantz Statistical methods in spatial genetics, Molecular Ecology, 2009.
B. Guedj and G. Guillot. Estimating the location and shape of hybrid zones. Molecular Ecology Resources, 11(6) 11191123, 2011
G. Guillot, S. Renaud, R. Ledevin, J. Michaux and J. Claude. A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data. Systematic Biology, to appear, 2012.
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