English
GermanFunctions and Formulas
Scoring
Scoring
linearMapping
Performs a mapping from a source vector to another vector space using either the weighted sum method or the proportional score method.
Collection linearMapping(Collection matrix, Collection x, integer sign=0.0, Object total=0.0, integer grouphandling=nothing, integer method=nothing, integer causeslevel=nothing, integer effectslevel=nothing)
Parameters
| matrix : | is the matrix describing the linear transformation. |
| x : | is the vector you want to map. |
| sign : | sets a filter for the matrix relations: all, pos(itive only), neg(ative only) |
| total : | total sum for normalization. Use 0 in order to skip normalization. |
| grouphandling : | defines how to handle parent items: shallow (leafs only), sums (accumulate hierarchically), levels (calculate system level and paramater level separately). |
| method : | wsm (weighted sum method) or prop (proportional score method) |
| causeslevel : | is the details level for the causes. Set to 0 in order to use the matrix default. |
| effectslevel : | is the details level for the effects. Set to 0 in order to use the matrix default. |
pughSum
Returns the Pugh sum for a container. The Pugh sum is a measure for the number of positive and negative effects of a number of alternatives.
Object pughSum(Collection x, Collection value, Object param1=nothing, Object param2=nothing, Object param3=nothing)
Parameters
| x : | is the container for which you want to calculate the Pugh sum. |
| value : | is the Pugh value, i.e.the value being counted. Normally, this argument should be one of -1, 0 +1 to count negative, neutral or positive relationships in the given container. |
| param1 : | is the first parameter (7.5 for positive, 5 for neutral, 2.5 for negative pugh sum) |
| param2 : | is the second parameter (10 for positive, 0 for negative pugh sum) |
| param3 : |
pughSumEx
Object pughSumEx(Collection x, Collection concepts, Collection mode, Object param1=nothing, Object param2=nothing, Object param3=nothing)
Parameters
| x : | |
| concepts : | |
| mode : | |
| param1 : | |
| param2 : | |
| param3 : |
topsis
Concept selection function TOPSIS
Collection topsis(Collection decisionMatrix, Collection importance=nothing, Collection optimization=nothing, Collection best=nothing, Collection worst=nothing, boolean useBestWorst=false)
Parameters
| decisionMatrix : | the decision matrix with quantified performance data |
| importance : | the vector of decision criteria weights |
| optimization : | the vector of decision criteria optimization directions |
| best : | the vector of best-in-class performance |
| worst : | the vector of worst in class performance |
| useBestWorst : | if true always use best and worse for calculation |
topsisEx
Concept selection function TOPSIS
Collection topsisEx(Collection decisionMatrix, Collection importance, Collection map, Collection scores)
Parameters
| decisionMatrix : | the decision matrix with quantified performance data |
| importance : | the vector of decision criteria weights |
| map : | is the data-to-performance mapping table |
| scores : | is the vector of performance scale values |
Feedback
Did you find what you needed?
If you have any further questions or suggestions to improve this page, please let us know: