qb.meancomp              package:qtlbim              R Documentation

_E_x_a_m_i_n_e _g_r_a_n_d _m_e_a_n _a_n_d _c_o_v_a_r_i_a_t_e _M_C_M_C _s_a_m_p_l_e_s.

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

     Examine grand mean and covariate Monte Carlo samples to glean
     estimates of data center and importance of covariates.

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

     qb.meancomp(qbObject, adjust.covar)
     ## S3 method for class 'qb.meancomp':
     summary(object, percent = 5, ...)
     ## S3 method for class 'qb.meancomp':
     print(x, ...)
     ## S3 method for class 'qb.meancomp':
     plot(x, covar, percent = 5, cex, ...)

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

qbObject: Object of class 'qb'.

adjust.covar: Adjustments to covariates. Default is 'NA', which adjusts
          by covariate mean values. Values are assumed to be in order
          of fixed covariates.

  object: Object of class 'qb.meancomp'.

       x: Object of class 'qb.meancomp'.

 percent: Percentile between 0 and 100 for summaries.

   covar: Sequence of covariate identifiers for plot.

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

     ...: Extra parameters passed along.

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

     Grand mean is adjusted to mean level of covariates. Diagonal of
     scatterplot matrix includes density plot. Setting 'covar = 0'
     yields a density plot for the grand mean alone.

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

     'qb.meancomp' is a matrix with columns for the grand mean and for
     each fixed covariate. Summaries show mean and upper and lower
     percentiles.

_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.meancomp(qbExample)
     summary(temp)
     plot(temp)

