valstats {CGGP} | R Documentation |
Calculate stats for prediction on validation data
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 )
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
Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association 102.477 (2007): 359-378.
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)