step.fitqtl              package:qtlbim              R Documentation

_S_t_e_p_w_i_s_e _b_a_c_k_w_a_r_d _e_l_i_m_i_n_a_t_i_o_n _a_n_d _a_n_o_v_a _c_o_m_p_a_r_i_s_o_n.

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

     These functions mimic 'step' and 'anova' but have reduced
     functionality. They are not truly methods, but can help study qtl
     model fits.

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

     step.fitqtl(cross, qtl, pheno.col = 1, arch, cutoff = 0.05,
       trace = 1, steps = 100)
     ## S3 method for class 'step.fitqtl':
     anova(object, object2, ...)

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

   cross: Object of class 'cross'.

     qtl: Object of class 'qtl', as output of 'makeqtl'.

pheno.col: Column of phenotype (numeric).

    arch: Object of class 'qb.arch' from 'qb.arch'.

  cutoff: Significance cutoff for dropping terms.

   trace: If positive, information is printed during the run. Values 1,
          2, 3 give gradually more detailed information.

   steps: Maximum number of steps to be considered.

  object: Object of class 'step.fitqtl' from 'step.fitqtl'.

 object2: Object of class 'step.fitqtl' from 'step.fitqtl'.

     ...: Currently not used.

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

     'step.fitqtl' is analogous to 'step' applied to analysis with
     'fitqtl'. 'anova.step.fitqtl' is an S3 method for 'anova'.
     'anova.step.fitqtl' with one argument calls 'summary.fitqtl'; with
     two arguments it attempts to conduct a general F comparison of
     anova fits.

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

     'step.fitqtl' returns an object of class 'step.fitqtl' with 

     fit: Object of class 'fitqtl'.

    arch: Object of class 'qb.arch'.

_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:

     'qb.arch', 'fitqtl', 'summary.fitqtl', 'makeqtl'

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

     cross <- sim.geno(cross, n.draws = 8, step = 2, error = 0.01)
     qtl <- makeqtl(cross, chr = c(1,1,2,3), pos = c(15,45,12,15))
     cross.step <- step.fitqtl(cross, qtl, pheno.col = 3, arch = cross.arch)
     anova(cross.step)
     cross.step$arch

