ahp (version 0.2.4)

Analyze: Analyze your AHP model

Description

Converts the calculated AHP tree into a data.frame or an HTML table, containing all the weight contributions or priorities to/of the overall decision. You can also sort and filter the output.

Displays the AHP analysis in form of an html table, with gradient colors and nicely formatted.

Usage

Analyze(ahpTree, decisionMaker = "Total", variable = c("weightContribution",
  "priority", "score"), sort = c("priority", "totalPriority", "orig"),
  pruneFun = function(node, decisionMaker) TRUE)
AnalyzeTable(ahpTree, decisionMaker = "Total",
  variable = c("weightContribution", "priority", "score"),
  sort = c("priority", "totalPriority", "orig"), pruneFun = function(node,
  decisionMaker) TRUE, weightColor = "honeydew3",
  consistencyColor = "wheat2", alternativeColor = "thistle4")
PruneByCutoff(node, decisionMaker, minWeight = 0)
PruneLevels(node, decisionMaker, levelsToPrune = 0)

Arguments

ahpTree

the calculated AHP data.tree

decisionMaker

the name of the decision maker. The default returns the joint decision.

variable

the variable to display, i.e. either weightContribution (the default), priority, or score

sort

sort either by priority according to the decision maker (the default), by totalPriority, or as originally specified (orig)

pruneFun

use this to filter the what rows are shown in the analysis pruneFun must be a function taking a Node as its first argument, and the decisionMaker as its second argument. The default (NULL) returns the fu

weightColor

The name of the color to be used to emphasize weights of categories. See color for a list of possible colors.

consistencyColor

The name of the color to be used to highlight bad consistency

alternativeColor

The name of the color used to highlight big contributors to alternative choices.

node

the Node

minWeight

prunes the nodes whose weightContribution is smaller than the minWeight

levelsToPrune

cuts the n hightest levels of the ahp tree

Value

• Analyze returns a data.frame containing the contribution of each criteria

  AnalyzeTable returns a formattable data.frame object which, in most environments, will be displayed as an HTML table

  the Prune methods must return TRUE for nodes to be kept, FALSE for nodes to be pruned

Examples

ahpFile <- system.file("extdata", "car.ahp", package="ahp")
carAhp <- Load(ahpFile)
Calculate(carAhp)
Analyze(
   carAhp,
   pruneFun = function(x, decisionMaker) {
     PruneLevels(x, decisionMaker, 1) && PruneByCutoff(x, decisionMaker, minWeight = 0.05)
   }
)


ahpFile <- system.file("extdata", "vacation.ahp", package="ahp")
vacationAhp <- Load(ahpFile)
Calculate(vacationAhp)
AnalyzeTable(
   vacationAhp,
   decisionMaker = "Kid",
   variable = "score",
   sort = "totalPriority"
)