plot.qb.diag             package:qtlbim             R Documentation

_M_a_r_g_i_n_a_l _a_n_d _m_o_d_e_l-_c_o_n_d_i_t_i_o_n_a_l _s_u_m_m_a_r_i_e_s _o_f _B_a_y_e_s_i_a_n _i_n_t_e_r_v_a_l
_m_a_p_p_i_n_g _d_i_a_g_n_o_s_t_i_c_s

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

     A density histogram is drawn for model-averaged summary
     diagnostics such as LOD, variance, or heritability.

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

     qb.diag(qbObject, items= c("mean","envvar","var","herit"), ...)
     ## S3 method for class 'qb.diag':
     plot(x, ... )
     ## S3 method for class 'qb.diag':
     print(x, ... )
     ## S3 method for class 'qb.diag':
     summary(object, digits = 5, ... )

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

qbObject: Object of class 'qb'.

  object: Object of class 'qb.diag'.

       x: Object of class 'qb.diag'.

   items: Diagnostics to be summarized; must be name of a column in
          'element'.

  digits: Number of significant digits.

     ...: Parameters to methods. Not used for 'qb.diag'.

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

     Model-averaged density is smooth kernel estimate similar to
     ordinary histogram. A 'boxplot' (without outliers) is overlaid for
     comparison   with conditional boxplots. Conditional boxplots by
     number of QTL may   show   indication of model bias for small
     number of QTL. This and  'qb.BayesFactor' can help suggest the
     minimal model. Diagnostic   items that make sense to plot are
     '"LOD"', '"envvar"'   (environmental variance), '"herit"'
     (heritability), '"mean"' (grand mean), '"addvar"' (variance of
     'add'), '"domvar"' (variance of 'add'). Marginal and conditional
     medians are printed.

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

     Brian S. Yandell, yandell@stat.wisc.edu

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

     <URL: http://www.qtlbim.org>

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

     'plot.qb', 'density', 'boxplot', 'qb.BayesFactor'

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

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

