Geneland-package          package:Geneland          R Documentation

_S_i_m_u_l_a_t_i_o_n _a_n_d _i_n_f_e_r_e_n_c_e _o_f _a 
_s_p_a_t_i_a_l _s_t_a_t_i_s_t_i_c_a_l _m_o_d_e_l _f_o_r _l_a_n_d_s_c_a_p_e _g_e_n_e_t_i_c_s.

_D_e_s_c_r_i_p_t_i_o_n:

     The main function 'mcmcFmodel'  takes geo-referenced individual
     multilocus genetic data and tries to detect population structure,
     i.e sub-populations making use of both spatial and genetic
     information.

_D_e_t_a_i_l_s:


       Package:  Geneland
       Type:     Package
       Version:  1.0.5
       Date:     2006-01-10
       License:  GPL

     The overall population is viewed as a co-existence of
     sub-populations at  Hardy-Weiberg equilibrium. The sub-populations
     are supposed to be spatially  organised through the so-called 
     colored Poisson-Voronoi tessellation.  Allele frequencies are
     assumed to be drawn from the F-model  as described in (Falush
     2003) although  the particular case of independent Dirichlet
     allele frequencies,  as described by Pritchard (2000),  is also
     handled.  Individuals within populations are assumed to be
     randomly  located and  Hardy-Weinberg equilibrium  and linkage
     equilibrium are assumed.

     The main purpose of the program is to perform Bayesian inference
     of all the parameters involved  through Markov Chain Monte-Carlo
     simulation. This is achievied by the function   'mcmcFmodel'.
     Function 'PostProcessChain' read some output files of 'mcmcFmodel'
     and computes some statistics suitable to print maps of inferred
     populations.

     See 'Storage format' section in 'mcmcFmodel help page.'

     The following functions are provided by the package:

     'simFmodel': simulation from the prior of the spatial F-model

     'mcmcFmodel': Full Bayesian Markov Chain Monte Carlo inference of
     parameters in the spatial F-model

     'PostProcessChain': Post-procesing of MCMC output for maps of
     posterior probability of populations subdomains

     'PlotTessellation': Graphical display of inferred sub-domains

     The following  functions are very basic and are only intended to
     be an aid for those not familiar with R. Most probably you may
     want to use directly the output files of 'mcmcFmodel' and
     'PostProcessChain' to print your own figures.

     'PlotDrift': Graphical display of drift factors along MCMC run

     'PlotFreqA': Graphical display of allele frequencies in the
     ancestral population along MCMC run

     'PlotFreq': Graphical display of allele frequencies in the present
     time population along MCMC run

     'Plotnpop': Graphical display of number of populations along MCMC
     run

     'Plotntile': Graphical display of number of tiles along MCMC run

     'PosteriorMode': Computation and/or graphical display of mode in
     the posterior distribution of class membership at each pixel

     'Fstat': Computations of pairwise F statistics between inferred
     subpopulations 

     'FormatGenotypes': Internal function.  Transform a file of
     genotypes into a format suitable for function 'mcmcFmodel'

     'setplot': Internal function

_A_u_t_h_o_r(_s):

     Gilles Guillot

     <URL:
     www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/welcome.html
     >

     See also

     <URL:
     www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/Geneland.htm
     l>

_R_e_f_e_r_e_n_c_e_s:

     On the implementation of mixture models in population genetics:

     - J.K. Pritchard, M. Stephens and P. Donnelly, Inference of
     population structure using multilocus genotype data, Genetics, pp
     945-959 vol. 155, 2000

     - Falush D., M. Stephens and  J.K. Pritchard,  Inference of
     population structure using multilocus genotype data: Linked loci
     and correlated allele frequencies, Genetics, pp 1567-1587, vol
     164, 2003

     On the implementation of variable dimension MCMC algorihtm in
     population genetics:

     - Corander, J.C., Waldmann, P. and Sillanpaa, M.J., Bayesian
     analysis of genetic differentiation between populations, Genetics,
     2003, 163, 367-374

     - Corander, J.C.,  P. Waldmann, P. Martinen and  M.J. Sillanpaa, ,
     BAPS2: Enhanced possibilities for the analysis of genetic
     population structure, Bioinformatics, vol. 20,number 15, 2004

     On the use of Voronoi tessellations in population genetics :

     - Dupanloup, I., Schneider, S. and Excoffier, L., A simulated
     annealing approach to define genetic structure of populations,
     Molecular Ecology, 2002, 11, 2571-2581

     On the model (and sub-models) implemented in 'Geneland'

     - 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.

_S_e_e _A_l_s_o:

_E_x_a_m_p_l_e_s:

