DecisionAnalysis-package: DecisionAnalysis: Multi-Objective Decision Analysis

Description Details Author(s) See Also Examples

Description

The DecisionAnalysis package contains all of the necessary functions required to plot weighted and unweighted value hierarchy trees, calculate and plot linear, exponential, and categorical single attribute value functions, calculate and graph multi value attribute functions, and conduct sensitivity analysis.

Details

Start with the vignette to learn more about using the DecisionAnalysis package: browseVignettes(package = "DecisionAnalysis")

Author(s)

Maintainer: Josh Deehr [email protected]

Authors:

See Also

Report bugs at https://github.com/AFIT-R/DecisionAnalysis

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
#Create a value hierarchy tree
branches<- as.data.frame(matrix(ncol=5,nrow=7))
names(branches)<-c("Level1","Level2","Level3","leaves","weights")
branches[1,]<-rbind("QB","Elusiveness","Speed","Forty","0.092")
branches[2,]<-rbind("QB","Elusiveness","Agility","Shuttle","0.138")
branches[3,]<-rbind("QB","Size","","Height","0.096")
branches[4,]<-rbind("QB","Size","","Weight","0.224")
branches[5,]<-rbind("QB","Intelligence","","Wonderlic","0.07")
branches[6,]<-rbind("QB","Strength","Explosiveness","Vertical","0.152")
branches[7,]<-rbind("QB","Strength","Power","Broad","0.228")
value_hierarchy_tree(branches$Level1,branches$Level2,branches$Level3,
leaves=branches$leaves,weights=branches$weights)


#subset NFLcombine data from DecisionAnalysis package
qbdata <- NFLcombine[1:7,]

#Create SAVF_matrix
Height <- SAVF_exp_score(qbdata$heightinchestotal, 68, 75.21, 82)
Weight <- SAVF_exp_score(qbdata$weight, 185, 224.34, 275)
Forty <- SAVF_exp_score(qbdata$fortyyd, 4.3, 4.81, 5.4, increasing=FALSE)
Shuttle <- SAVF_exp_score(qbdata$twentyss, 3.8, 4.3, 4.9, increasing=FALSE)
Vertical <- SAVF_exp_score(qbdata$vertical, 21, 32.04, 40)
Broad <- SAVF_exp_score(qbdata$broad, 90, 111.24, 130)
Wonderlic <- SAVF_exp_score(qbdata$wonderlic, 0, 27.08, 50)
SAVF_matrix = cbind(Height, Weight, Forty, Shuttle, Vertical, Broad, Wonderlic)

#Create weights vector
weights = c(0.096, 0.224, 0.092, 0.138, 0.152, 0.228, 0.07)

#Calculate MAVF Score
MAVF_Scores(SAVF_matrix, weights, qbdata$name)

#Plot MAVF Breakout
MAVF_breakout(SAVF_matrix, weights, qbdata$name)

#Plot sensitivity analysis for shuttle criteria
sensitivity_plot(SAVF_matrix, weights, qbdata$name, 4)

AFIT-R/DecisionAnalysis documentation built on July 31, 2018, 12:10 a.m.