regressionplot          package:globaltest          R Documentation

_R_e_g_r_e_s_s_i_o_n _p_l_o_t _f_o_r _G_l_o_b_a_l _T_e_s_t _r_e_s_u_l_t

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

     Produces a plot which can be used to visualize the effect of
     specific samples on the test result produced by 'globaltest'.

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

     regressionplot(testresult, samplenr = NULL)

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

testresult: The output of a call to 'globaltest'.

samplenr: A vector giving row numbers of samples of interest.

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

     The regressionplot plots, for all pairs of samples, the covariance
     between the expression patterns against the covariance between
     their clinical outcomes. Each point in the plot therefore
     represents a pair of samples. A regression line is fitted through
     the samples, which visualizes the test result of the function
     'globaltest'. A steeply increasing slope indicates a high
     (possibly significant) value of the test statistic.

     An optional argument 'samplenr' can be supplied, giving sample
     numbers of possibly outlying arrays. In this case, all pairs of
     arrays involving one of the arrays in 'samplenr' is marked as a
     red cross, while the other pairs are marked as a blue dot. The
     blue line which is fitted through all points can now be compared
     to a red dotted line which is fitted though only the red crosses.

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

     'NULL' (no output).

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

     Jelle Goeman: j.j.goeman@lumc.nl

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

     J. J. Goeman, S.A. van de Geer, F. de Kort and J. C. van
     Houwelingen, _A global test for association of a group of genes
     with a clinical outcome_, Technical Report MI 2003-03,
     Mathematical Institute, Leiden University. Available from  <URL:
     http://www.math.leidenuniv.nl/~jgoeman>.

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

     'globaltest', 'checkerboard', 'geneplot', 'permutations'.

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

     if(interactive()){
         data(exampleX)      # Expression data (40 samples; 1000 genes)
         data(exampleY)      # Clinical outcome for the 40 samples
         pathway <- 1:25     # A pathway contains genes 1 to 25
         gt <- globaltest(exampleX, exampleY, test.genes = pathway)
         gt
         regressionplot(gt)
         regressionplot(gt,40)
     }

