mia                   package:gap                   R Documentation

_m_u_l_t_i_p_l_e _i_m_p_u_t_a_t_i_o_n _a_n_a_l_y_s_i_s _f_o_r _h_a_p

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

     This command reads outputs from hap session that uses multiple
     imputations, i.e. -mi# option. To simplify matters it assumes -ss
     option is specified together with -mi option there.

     This is a very naive version of MIANALYZE, but can produce results
     for PROC MIANALYZE of SAS

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

     mia(hapfile,assfile,miafile,so,ns,mi,allsnps,sas)

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

 hapfile: hap haplotype output file name

 assfile: hap assignment output file name

 miafile: mia output file name

      so: to generate results according to subject order

      ns: do not sort in subject order

      mi: number of multiple imputations used in hap

 allsnps: all loci are SNPs

     sas: produce SAS data step program

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

     It simply extracts outputs from hap

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

     The returned value is a list containing:

_R_e_f_e_r_e_n_c_e_s:

     Zhao JH and W Qian (2003) Association analysis of unrelated
     individuals using polymorphic genetic markers. RSS 2003, Hassalt,
     Belgium

     Clayton DG (2001) SNPHAP.
     http://www-gene.cimr.cam.ac.uk/clayton/software

_N_o_t_e:

     adapted from hap, in fact cline.c and cline.h are not used

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

     'hap'

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

     ## Not run: 
     # 4 SNP example, to generate hap.out and assign.out alone
     data(fsnps)
     hap(id=fsnps[,1],gdata=fsnps[,3:10],nloci=4)

     # to generate results of imputations
     hap(id=fsnps[,1],gdata=fsnps[,3:10],nloci=4,ss=1,mi=5)

     # to extract information from the second run above
     mia(so=1,ns=1,mi=5)

     ## commands to check out where the output files are as follows:
     ## Windows
     # system("command.com")
     ## Unix
     # system("csh")
     ## End(Not run)

