effectscan                package:qtl                R Documentation

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

     This function is used to plot the estimated QTL effects along
     selected chromosomes. For a backcross, there will be only one
     line, representing the additive effect. For an intercross, there
     will be two lines, representing the additive and dominance
     effects.

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

     effectscan(cross, pheno.col=1, chr, get.se=FALSE, draw=TRUE,
                gap=25, ylim, mtick=c("line","triangle"),
                add.legend=TRUE, alternate.chrid=FALSE, ...)

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

   cross: An object of class 'cross'.

pheno.col: Column number in the phenotype matrix which to be drawn in
          the plot.  One may also give a character string matching a
          phenotype name.

     chr: Optional vector indicating the chromosomes to be drawn in the
          plot. This should be a vector of character strings referring
          to chromosomes by name; numeric values are converted to
          strings.  Refer to chromosomes with a preceding '-' to have
          all chromosomes but those considered.  A logical (TRUE/FALSE)
          vector may also be used.

  get.se: If TRUE, estimated standard errors are calculated.

    draw: If TRUE, draw the figure.

     gap: Gap separating chromosomes (in cM).

    ylim: Y-axis limits (optional).

   mtick: Tick mark type for markers.

add.legend: If TRUE, add a legend.

alternate.chrid: If TRUE and more than one chromosome is plotted,
          alternate the placement of chromosome  axis labels, so that
          they may be more easily distinguished.

     ...: Passed to the function 'plot' when it is called.

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

     The results of 'sim.geno' are required for taking account of
     missing genotype information.

     For a backcross, the additive effect is estimated as the
     difference between the phenotypic averages for heterozygotes and
     homozygotes.

     For recombinant inbred lines, the additive effect is estimated as
     half the difference between the phenotypic averages for the two
     homozygotes.

     For an intercross, the additive and dominance effects are
     estimated from linear regression on a and d with a = -1, 0, 1, for
     the AA, AB and BB genotypes, respectively, and d = 0, 1, 0, for
     the AA, AB and BB genotypes, respectively.

     As usual, the X chromosome is a bit more complicated.  We estimate
     separate additive effects for the two sexes, and for the two
     directions within females.

     There is a function 'plot.effectscan' that creates the actual plot
     by calling 'plot.scanone'.  In the case 'get.se=TRUE', colored
     regions indicate +/- 1 SE.

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

     The results are returned silently, as an object of class
     '"effectscan"', which is the same as the form returned by the
     function 'scanone', though with estimated effects where LOD scores
     might be.  That is, it is a data frame with the first two columns
     being chromosome ID and position (in cM), and subsequent columns
     being estimated effects, and (if 'get.se=TRUE') standard errors.

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

     Karl W. Broman, kbroman@biostat.wisc.edu

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

     Sen, \'S. and Churchill, G. A. (2001) A statistical framework for
     quantitative trait mapping.  _Genetics_ *159*, 371-387.

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

     'effectplot', 'plot.pxg', 'sim.geno'

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

     data(fake.f2)

     fake.f2 <- sim.geno(fake.f2, step=2.5, n.draws=16)

     # allelic effect on whole genome
     effectscan(fake.f2)

     # on chromosome 13, include standard errors
     effectscan(fake.f2, chr="13", mtick="triangle", get.se=TRUE)

