startPed             package:MasterBayes             R Documentation

_s_t_a_r_t_P_e_d _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     An object containing the starting parameterisation of a model, and
     logical variables indicating wether parameters should be estimated
     or fixed at the starting parameterisation.  By default the
     starting parameterisation is obtained through a mixture of Maximum
     Likelihood and heuristic techniques.

_U_s_a_g_e:

     startPed(id=NULL, G=NULL, estG=TRUE, A=NULL, estA=TRUE, E1=NULL, 
        estE1=TRUE, E2=NULL, estE2=TRUE, dam=NULL,sire=NULL, estP=TRUE, 
        beta=NULL, estbeta=TRUE,  USdam=NULL, estUSdam=TRUE, 
        USsire=NULL, estUSsire=TRUE, ...)

_A_r_g_u_m_e_n_t_s:

      id: vector of indivual id's for 'G', 'dam' and 'sire'

       G: list of genotype objects

    estG: logical; should genotypes be estimated?

       A: list of allele frequencies

    estA: logical; should base-population allele frequencies be
          estimated? 

      E1: vector of allelic dropout rates (default=0.005)

   estE1: logical; should allelic dropout rates be estimated?

      E2: vector of stochastic genotyping error rates (default=0.005)

   estE2: logical; should stochastic error rates be estimated?

     dam: vector of dam's.  If the dam is unknown use 'NA'. All dam's
          must be in 'id'

    sire: vector of sire's.  If the sire is unknown use 'NA'. All
          sire's must be in 'id'

    estP: logical; should the pedigree be estimated?

    beta: vector of population-level parameters

 estbeta: logical; should the population-level parameters be estimated?

   USdam: vector of unsampled female population sizes

estUSdam: logical; should the female population sizes be estimated?

  USsire: vector of unsampled male population sizes

estUSsire: logical; should the male population sizes be estimated?

     ...: 

_D_e_t_a_i_l_s:

     If 'estG=FALSE' an approximation is used for genotyping error.  In
     this case error rates and allele frequencies are not estimated but
     fixed at the starting parameterisation.  If indivdiuals have been
     typed more than once, then the approxiamtion only uses the
     genotype that first appears in the 'GdP$G' object passed to
     'MCMCped'.  If 'A' is not specified estimates are taken directly
     from 'GdP$G' using 'extractA'.  If 'E1' and 'E2' are not specified
     they are set to 0.005.  Note that if the approximation for
     genotyping error is used Wang's (2005) model is not used, and the
     CEVUS model (Marshall 1998) is adopted.  In this case 'E2' is the
     per-allele error rate and 'E2'(2-'E2') is the per-genotype error
     rate used by CERVUS.  If 'dam' and 'sire' are not specified the
     most likely set of parents given the genetic data are used (see
     'MLE.ped').  The starting value of 'beta', if not given, is the
     MLE of beta given the starting pedigree (see 'MLE.beta').   The
     starting values of 'USdam' and 'USsire', if not given, are the MLE
     based on the genotype data (see 'MLE.popsize').

_V_a_l_u_e:

     list containing the arguments passed

_A_u_t_h_o_r(_s):

     Jarrod Hadfield j.hadfield@sheffield.ac.uk

_S_e_e _A_l_s_o:

     'MCMCped'

_E_x_a_m_p_l_e_s:

     # In this example we simulate a pedigree and then fix the
     # pedigree and estimate the population level paarmeters

     data(WarblerP)

     var1<-expression(varPed(c("lat", "long"), gender="Male", 
       relational="OFFSPRING"))

     # paternity is to be modelled as a function of distance 
     # between offspring and male territories

     res1<-expression(varPed("offspring", restrict=0))

     # indivdiuals from the offspring generation are excluded as parents

     res2<-expression(varPed("terr", gender="Female", relational="OFFSPRING",
       restrict=TRUE))

     # mothers not from the offspring territory are excluded
      
     PdP<-PdataPed(formula=list(var1,res1,res2), data=WarblerP, USsire=FALSE)
     simped<-simpedigree(PdP, beta=-0.25)

     # simulate a pedigree where paternity drops with distance (beta=-0.25)

     sP<-startPed(dam=simped$ped[,2], sire=simped$ped[,3], estP=FALSE)
     model1<-MCMCped(PdP=PdP, sP=sP, nitt=3000, thin=2, burnin=1000) 
     plot(model1$beta)

     # The true underlying value is -0.25

