som_drmake

[sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)

 SOM_DRMAKE Make descriptive rules for given group within the given data. 

 sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis]) 
 
  D        (struct) map or data struct
           (matrix) the data, of size [dlen x dim]
  [inds1]  (vector) indeces belonging to the group
                    (the whole data set by default)
  [inds2]  (vector) indeces belonging to the contrast group
                    (the rest of the data set by default)
  [sigmea] (string) significance measure: 'accuracy', 
                    'mutuconf' (default), or 'accuracyI'.
                    (See definitions below).
  [nanis]  (scalar) value given for NaNs: 0 (=FALSE, default),
                    1 (=TRUE) or NaN (=ignored)

  sR      (struct array) best rule for each component. Each 
                   struct has the following fields:
    .type     (string) 'som_rule'
    .name     (string) name of the component
    .low      (scalar) the low end of the rule range
    .high     (scalar) the high end of the rule range
    .nanis    (scalar) how NaNs are handled: NaN, 0 or 1

  best    (vector) indeces of rules which make the best combined rule
  sig     (vector) significance measure values for each rule, and for the combined rule
  Cm      (matrix) A matrix of vectorized confusion matrices for each rule, 
                   and for the combined rule: [a, c, b, d] (see below). 
 
 For each rule, such rules sR.low <= x < sR.high are found 
 which optimize the given significance measure. The confusion
 matrix below between the given grouping (G: group - not G: contrast group) 
 and rule (R: true or false) is used to determine the significance values:

          G    not G    
       ---------------    accuracy  = (a+d) / (a+b+c+d)
 true  |  a  |   b   |    
       |--------------    mutuconf  =  a*a  / ((a+b)(a+c)) 
 false |  c  |   d   | 
       ---------------    accuracyI =   a   / (a+b+c)

 See also  SOM_DREVAL, SOM_DRTABLE.