# valstats: Calculate stats for prediction on validation data In CollinErickson/CGGP: Composite Grid Gaussian Processes

## Description

Calculate stats for prediction on validation data

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```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.

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

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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) ```

CollinErickson/CGGP documentation built on May 14, 2021, 4:33 a.m.