qb.varcomp              package:qtlbim              R Documentation

_V_a_r_i_a_n_c_e _c_o_m_p_o_n_e_n_t_s _f_o_r _B_a_y_e_s_i_a_n _m_u_l_t_i_p_l_e _Q_T_L

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

     These routines extract and summarize variance components for
     Bayesian multiple QTL. Variance components are averaged over
     genome loci. Covariates and GxE may be included.

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

     qb.varcomp(qbObject, scan, aggregate = TRUE)
     ## S3 method for class 'qb.varcomp':
     summary(object, ...)
     ## S3 method for class 'qb.varcomp':
     print(x, ...)
     ## S3 method for class 'qb.varcomp':
     plot(x, log = TRUE, percent = 5, cex, ...)

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

qbObject: Object of class 'qb'.

  object: Object of class 'qb.varcomp'.

       x: Object of class 'qb.varcomp'.

    scan: Aggregated terms to include in created object (see below).

aggregate: Sum over individual components of aggregated terms if
          'TRUE'.

     log: Use log10 of variances in plot if 'TRUE'.

 percent: Percentile between 0 and 100 for summaries.

     cex: Character expansion for plot symbols. Default shrinks with
          number of MCMC iterations.

     ...: Arguments to pass along.

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

     Variance components are organized as "main" ("add" and "dom"),
     "epistasis" ("aa", etc.), "fixcov" (for all fixed covariate
     terms), "rancov" (random covariates), and "GxE" (genotype by
     environment, including additive and dominance terms). Any subset
     of these may be chosen.

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

     'qb.varcomp' creates a matrix with columns of samples for the
     variance components. Each row represents an MCMC iteration. Values
     are averaged over loci.

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

     Brian S. Yandell

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

     http://www.qtlbim.org

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

     'qb.mcmc'

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

     temp <- qb.varcomp(qbExample)
     summary(temp)
     plot(temp)

