Description Usage Arguments Details Value Author(s) References See Also Examples
Given parameters for a twoalternative scoring rule, plot the hypothetical scores that would be obtained for each forecast/outcome combination.
1 2 
param 
Numeric vector of length 2, containing the parameters for

fam 
scoring rule family. 
bounds 
Lower and upper bounds supplied to 
reverse 

legend 
Should a legend be displayed? Defaults to 
... 
Other arguments to 
For more information on the scoring rule families and the bounds
and reverse
arguments, see the details of calcscore()
.
Returns the result of a plot()
call that graphs the scoring rule.
Ed Merkle
Buja, A., Stuetzle, W., & Shen, Y. (2005). Loss functions for binary class probability estimation and classification: Structure and applications. (Obtained from http://stat.wharton.upenn.edu/~buja/PAPERS/)
Jose, V. R. R., Nau, R. F., & Winkler, R. L. (2008). Scoring rules, generalized entropy, and utility maximization. Operations Research, 56, 1146–1157.
Jose, V. R. R., Nau, R. F., & Winkler, R. L. (2009). Sensitivity to distance and baseline distributions in forecast evaluation. Management Science, 55, 582–590.
Merkle, E. C. & Steyvers, M. (in press). Choosing a strictly proper scoring rule. Decision Analysis.
1 2 3 4 5 6 7 8 9 10 11 12  ## Plot Brier score from power family with natural bounds
plotscore(c(2,.5), fam="pow")
## Plot Brier score from beta family with bounds of 0 and 1
plotscore(c(1,1), fam="beta", bounds=c(0,1))
## Plot log score
plotscore(c(0,0), fam="beta")
## Score from pseudospherical family with
## baseline of .3 and (0,1) bounds
plotscore(c(3, .3), fam="sph", bounds=c(0,1))

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