stam.cv                 package:stam                 R Documentation

_C_r_o_s_s _V_a_l_i_d_a_t_e_d _T_r_a_i_n_i_n_g _f_o_r _S_t_A_M

_D_e_s_c_r_i_p_t_i_o_n:

     Determine classifiers in leaf nodes and weights in inner nodes as
     well as best graph  shrinkage by cross validated model fitting.

_U_s_a_g_e:

     stam.cv(expression.matrix, classifications, 
             chip = "hgu95av2", root = "GO:0008150", 
             beta = NULL, deltas = NULL, ndeltas = 10, 
             results.per.node = FALSE, old.cv = NULL, 
             pamimagefile = NULL, verbose = FALSE)

_A_r_g_u_m_e_n_t_s:

expression.matrix: holds the expression levels. It may be of classes
          exprSet or ExpressionSet, or a plain numeric matrix. In the
          first case 'exprs' is used to extract the expression levels.
          The matrix is expected to hold one column per sample and one
          row per probeset.

classifications: This character vector must contain one entry per
          sample  identifying the group it belongs to. Alternatively,
          if 'expression.matrix' is  an 'exprSet' or 'ExpressionSet',
          this may be the name of a 'phenoData' variable.

    chip: the name of the microarray chip. A meta data package is
          expected to be  found holding the needed annotation, namely
          the links between probesets and Gene  Ontology nodes.

    root: the GO node used as root of the classifier graph. Only
          successors of this  node are considered during construction
          of the graph.

    beta: holds class weights used when judging classifier quality. The
          default is to  set class weights to the corresponding
          prevalence. 

  deltas: numeric vector holding graph shrinkage candidates. Default is
          to  determine 'ndelta' candidates between 0 and the lowest
          shrinkage level which  removes all leaf nodes.

 ndeltas: number of automatically determined graph shrinkage candidates
          determined  if 'deltas' is not defined.

results.per.node: whether results for each node should be returned

  old.cv: 'stamCV' object used to modify when PAM fits need not to be 
          recomputed. E.g. used when only 'beta' is adapted. 

pamimagefile: When this parameter is specified 'stam.cv' tries to read
          this  file and extract a 'stamCV' object to be used as
          'old.cv'. If the file does  not yet exist, PAM fits are
          stored there after computation.

 verbose: when set to TRUE reports summary on each leaf training,
          otherwise shows a progress bar.

_D_e_t_a_i_l_s:

     'stam.cv' uses 'stam.net' to generate a classifier graph for the
     microarray  chip at hand. It then fits a PAM classifier for each
     leaf node only considering the  probesets annotated to the node.
     Afterwards, in each inner node, weights are attributed  to each
     child according to the childs classification performance. Finally,
     the weights  are shrunken such that most of them become zero. In
     fact, the best shrinkage level is  chosen in a cross validation
     setting. 

     Classification performance is evaluated using an inverted deviance
     like measure which  uses weights to overstate specificity of a
     classifier. Weights for nodes are chosen  according to this
     measure and shrunken by an absolute shrinkage level. For each 
     shrinkage candidate cross validated performance results in terms
     of graph heterogeneity  and classification performance are stored.

_V_a_l_u_e:

     An object of class stamCV is returned. Use the methods print and
     plot to extract  information about the cross validation.

_A_u_t_h_o_r(_s):

     Claudio Lottaz

_S_e_e _A_l_s_o:

     'stamCV-class', 'plot.stamCV',  'stam.writeHTML'

_E_x_a_m_p_l_e_s:

     ## Not run: 
     # load and prepare some data
     library(golubEsets)
     data(Golub_Merge)
     golubTrain <- Golub_Merge[,1:38]

     # classify into ALL and AML
     # (root is chosen to yield results reasonably fast, 
     #  consider GO:0008150 (biological process) to obtain 
     #  meaningful results)
     golubTrain.cv <- stam.cv(golubTrain, "ALL.AML", chip="hu6800",
                              root="GO:0005576", ndeltas=10)

     # get further information
     print(golubTrain.cv)
     plot(golubTrain.cv, delta=0.6)
     ## End(Not run)

