clValid-class            package:clValid            R Documentation

_C_l_a_s_s "_c_l_V_a_l_i_d"

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

     The class '"clValid"' contains the clustering results and
     validation measures from the accompanying call to the function
     'clValid'.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created using the function 'clValid'.

_S_l_o_t_s:

     '_c_l_u_s_t_e_r_O_b_j_s': Object of class '"list"'. A list containing the
          results from the clustering methods.

     '_m_e_a_s_u_r_e_s': Object of class '"array"'. A 3-dimensional array which
          contains the validation measures for the clustering results. 
          The first dimension indicates the validation measures, the
          second the number of clusters, and the third the clustering
          methods.

     '_m_e_a_s_N_a_m_e_s': Object of class '"character"'. The names of the
          validation measures.

     '_c_l_M_e_t_h_o_d_s': Object of class '"character"'. A character vector
          giving the clustering methods.

     '_n_C_l_u_s_t': Object of class '"numeric"'. A numeric vector giving the
          numbers of clusters which were evaluated.

     '_v_a_l_i_d_a_t_i_o_n': Object of class '"character"'. A character vector
          giving the type of validation measures used, consisting of
          some combination of "internal", "stability", or "biological".

     '_m_e_t_r_i_c': Object of class '"character"'. The metric used to
          determine the distance matrix.

     '_m_e_t_h_o_d': Object of class '"character"'. For hierarchical
          clustering, the agglomeration method used.

     '_n_e_i_g_h_b_S_i_z_e': Object of class '"numeric"'. For internal
          validation, the neighborhood size used for the connectivity
          measure.

     '_a_n_n_o_t_a_t_i_o_n': Object of class '"character or array or list"'.
          Either a character string naming the Bioconductor annotation
          package for mapping genes to GO categories, or a list with
          the names of the functional classes and the observations
          belonging to each class.

     '_G_O_c_a_t_e_g_o_r_y': Object of class '"character"'. For biological
          validation, gives which GO categories to use for biological
          validation.  Can be one of "BP", "MF", "CC", or "all"

     '_g_o_T_e_r_m_F_r_e_q': Object of class '"numeric"'. For the BSI, what
          threshold frequency of GO terms to use for functional
          annotation.

     '_c_a_l_l': Object of class '"call"'.  Gives the call to 'clValid'
          used to create the 'clValid' object.

_M_e_t_h_o_d_s:

     _c_l_u_s_t_e_r_M_e_t_h_o_d_s 'signature(object = "clValid")': Returns the names
          of the clustering methods. 

     _c_l_u_s_t_e_r_s 'signature(object = "clValid")': Returns the results from
          the clustering methods.

          Additional arguments:

          _m_e_t_h_o_d = _c_l_M_e_t_h_o_d_s(_o_b_j_e_c_t) The clustering method(s) to
               extract.


     _m_e_a_s_N_a_m_e_s 'signature(object = "clValid")': Returns the names of
          the validation measures.

     _m_e_a_s_u_r_e_s 'signature(object = "clValid")': Returns the validation
          measures.

          Additional arguments:

          _m_e_a_s_u_r_e_s = _m_e_a_s_N_a_m_e_s(_o_b_j_e_c_t) The validation measure(s) to
               extract.


     _n_C_l_u_s_t_e_r_s 'signature(object = "clValid")': Returns the numbers of
          clusters evaluated.

     _o_p_t_i_m_a_l_S_c_o_r_e_s 'signature(object = "clValid")': Returns the optimal
          value for each validation measure, along with the
          corresponding clustering method and number of clusters.

          Additional arguments:

          _m_e_a_s_u_r_e_s = _m_e_a_s_N_a_m_e_s(_o_b_j_e_c_t) The validation measure(s) to
               extract.

     _p_l_o_t 'signature(x = "clValid", y = "missing")': Plots the
          validation measures.

          Additional arguments:

          _m_e_a_s_u_r_e_s=_m_e_a_s_N_a_m_e_s(_x) The validation measures to plot.

          _l_e_g_e_n_d=_T_R_U_E If TRUE provides a legend.

          _l_e_g_e_n_d_L_o_c="_t_o_p_r_i_g_h_t" The location of the legend.

          _m_a_i_n=_N_U_L_L Title of graph.

          _p_c_h=_N_U_L_L Plotting characters to use.

          _t_y_p_e="_b" Type of plot.

          _a_s_k=_p_r_o_d(_p_a_r("_m_f_c_o_l")) < _l_e_n_g_t_h(_m_e_a_s_u_r_e_s) && _d_e_v._i_n_t_e_r_a_c_t_i_v_e() 
               Logical.  If 'TRUE' the user is prompted before each
               plot.


     _p_r_i_n_t 'signature(x = "clValid")': Print method for class
          'clValid'.

     _s_h_o_w 'signature(object = "clValid")': Same as print.

     _s_u_m_m_a_r_y 'signature(object = "clValid")': Summary method for class
          'clValid'.

          Additional arguments:

          _d_i_g_i_t_s = _m_a_x(_3,_g_e_t_O_p_t_i_o_n("_d_i_g_i_t_s")-_3) The number of
               significant digits to use.


_N_o_t_e:

     See the vignette for an illustration of the class.

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

     Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta

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

     Datta, S. and Datta, S. (2003). Comparisons and validation of
     statistical clustering techniques for microarray gene expression
     data. Bioinformatics 19(4): 459-466.

     Datta, S. and Datta, S. (2006). Methods for evaluating clustering
     algorithms for gene expression data using a reference set of
     functional classes. BMC Bioinformatics 7:397.

     Handl, J., Knowles, K., and Kell, D. (2005). Computational cluster
     validation in post-genomic data analysis. Bioinformatics 21(15):
     3201-3212.

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

     For a description of the function 'clValid' see 'clValid'.

     For help on the clustering methods see 'hclust' and 'kmeans' in
     package 'stats', 'kmeans'  in package 'stats',  'agnes', 'clara', 
     'diana', 'fanny', and 'pam' in package 'cluster', 'som' in package
     'kohonen', 'Mclust'   in package 'mclust', and 'sota'.

     For additional help on the validation measures see 'connectivity',
       'dunn', 'stability',  'BHI', and  'BSI'.

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

     ## to delete
     library(clValid)

     data(mouse)

     ## internal validation
     express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
     rownames(express) <- mouse$ID[1:25]
     intern <- clValid(express, 2:6, clMethods=c("hierarchical","fanny","model"),
                       validation="internal")
     slotNames(intern)

     ## view results
     intern
     summary(intern)
     optimalScores(intern)
     plot(intern)

     ## Extract objects from slots
     measures(intern)
     hierClust <- clusters(intern,"hierarchical")
     plot(hierClust)
     measNames(intern)
     nClusters(intern)

