valstats {CGGP} | R Documentation |

## Calculate stats for prediction on validation data

### Description

Calculate stats for prediction on validation data

### Usage

```
valstats(
predmean,
predvar,
Yval,
bydim = TRUE,
RMSE = TRUE,
score = TRUE,
CRPscore = TRUE,
coverage = TRUE,
corr = TRUE,
R2 = TRUE,
MAE = FALSE,
MIS90 = FALSE,
metrics,
min_var = .Machine$double.eps
)
```

### Arguments

`predmean` |
Predicted mean |

`predvar` |
Predicted variance |

`Yval` |
Y validation data |

`bydim` |
If multiple outputs, should it be done separately by dimension? |

`RMSE` |
Should root mean squared error (RMSE) be included? |

`score` |
Should score be included? |

`CRPscore` |
Should CRP score be included? |

`coverage` |
Should coverage be included? |

`corr` |
Should correlation between predicted and true mean be included? |

`R2` |
Should R^2 be included? |

`MAE` |
Should mean absolute error (MAE) be included? |

`MIS90` |
Should mean interval score for 90% confidence be included? See Gneiting and Raftery (2007). |

`metrics` |
Optional additional metrics to be calculated. Should have same first three parameters as this function. |

`min_var` |
Minimum value of the predicted variance. Negative or zero variances can cause errors. |

### Value

data frame

### References

Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association 102.477 (2007): 359-378.

### Examples

```
valstats(c(0,1,2), c(.01,.01,.01), c(0,1.1,1.9))
valstats(cbind(c(0,1,2), c(1,2,3)),
cbind(c(.01,.01,.01),c(.1,.1,.1)),
cbind(c(0,1.1,1.9),c(1,2,3)))
valstats(cbind(c(0,1,2), c(8,12,34)),
cbind(c(.01,.01,.01),c(1.1,.81,1.1)),
cbind(c(0,1.1,1.9),c(10,20,30)), bydim=FALSE)
valstats(cbind(c(.8,1.2,3.4), c(8,12,34)),
cbind(c(.01,.01,.01),c(1.1,.81,1.1)),
cbind(c(1,2,3),c(10,20,30)), bydim=FALSE)
```

*CGGP*version 1.0.4 Index]