hddplot-package           package:hddplot           R Documentation

_F_o_r _h_i_g_h-_d_i_m_e_n_s_i_o_n_a_l _d_a_t_a _w_i_t_h _k_n_o_w_n _g_r_o_u_p_s, _d_e_r_i_v_e _s_c_o_r_e_s _f_o_r _p_l_o_t_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Cross-validated linear discriminant calculations determine the
     optimum number of features. Test and training scores from
     successive cross-validation steps determine, via a principal
     components calculation, a low-dimensional global space onto which
     test scores are projected, in order to plot them. Further
     functions are included for didactic purposes.

_D_e_t_a_i_l_s:


       Package:  hddplot
       Type:     Package
       Version:  1.0
       Date:     2006-01-09
       License:  GPL Version 2 or later.

     The most important functions are 

     'cvdisc': Determine variation in cross-validated accuracy with
     number of features

     'cvscores': For a specific choice of number of features, determine
     scores that can be used for plotting

     Note also 'scoreplot' (plot scores), 'qqthin' (qqplots, designed
     to avoid generating large files when there are many points), and
     functions that are intended to illustrate issues that arise in the
     plotting of expression array and other high-dimensional data

_A_u_t_h_o_r(_s):

     John Maindonald

     Maintainer: John Maindonald <john.maindonald@anu.edu.au>

_R_e_f_e_r_e_n_c_e_s:

     Maindonald, J.H. and Burden, C.J., 2005. Selection bias in plots
     of microarray or other data that have been sampled from a
     high-dimensional space. In R. May and A.J. Roberts, eds., {\em
     Proceedings of 12th Computational Techniques and Applications
     Conference CTAC-2004}, volume 46, pp. C59-C74. 

     <URL: http://anziamj.austms.org.au/V46/CTAC2004/Main> [March 15,
     2005].

_S_e_e _A_l_s_o:

     'cvscores', 'scoreplot'

_E_x_a_m_p_l_e_s:

     ## Use first 500 rows (expression values) of Golub, for demonstration.
     data(Golub)
     data(golubInfo)
     attach(golubInfo) 
     miniG.BM <- Golub[1:500, BM.PB=="BM"]  # 1st 500 rows only
     cancer.BM <- cancer[BM.PB=="BM"] 
     miniG.cv <- cvdisc(miniG.BM, cl=cancer.BM, nfeatures=1:10,
                         nfold=c(10,4))
     miniG.scores <- cvscores(cvlist=miniG.cv, nfeatures=4, cl.other=NULL)
     subsetB <- (cancer=="allB") & (tissue.mf %in% c("BM:f","BM:m","PB:m"))
     tissue.mfB <- tissue.mf[subsetB, drop=TRUE] 
     scoreplot(scorelist=miniG.scores, cl.circle=tissue.mfB, 
            circle=tissue.mfB%in%c("BM:f","BM:m"), 
            params=list(circle=list(col=c("cyan","gray"))), 
            prefix="BM samples -") 
     detach(golubInfo) 
     ## Not run: demo(biasedPlots)
     ## Not run: demo(CVscoreplot)

