| SOM Toolbox | Online documentation | http://www.cis.hut.fi/projects/somtoolbox/ |
[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.