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som_drmake - .high(scalar the high end of the rule...

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SOM Toolbox Online documentation http://www.cis.hut.fi/projects/somtoolbox/ 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
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Unformatted text preview: .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. [ SOM Toolbox online doc ]...
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