# plotscore: Plot a Two-Alternative Scoring Rule In scoring: Proper Scoring Rules

## Description

Given parameters for a two-alternative scoring rule, plot the hypothetical scores that would be obtained for each forecast/outcome combination.

## Usage

 ```1 2``` ```plotscore(param = c(2, 0.5), fam = "pow", bounds, reverse = FALSE, legend = TRUE, ...) ```

## Arguments

 `param` Numeric vector of length 2, containing the parameters for `fam`. For family `beta`, these are the parameters commonly denoted alpha and beta. For families `pow` and `sph`, these correspond to the family parameter gamma and the baseline parameter associated with the focal outcome, respectively. `fam` scoring rule family. `pow` (default) is the power family, `beta` is the beta family, `sph` is the pseudospherical family. `bounds` Lower and upper bounds supplied to `calcscore`. `reverse` `reverse` argument supplied to `calcscore`. `legend` Should a legend be displayed? Defaults to `TRUE` `...` Other arguments to `plot()`

## Details

For more information on the scoring rule families and the `bounds` and `reverse` arguments, see the details of `calcscore()`.

## Value

Returns the result of a `plot()` call that graphs the scoring rule.

Ed Merkle

## References

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.

`calcscore`

## Examples

 ``` 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)) ```

### Example output    ```
```

scoring documentation built on May 1, 2019, 10:29 p.m.