scores: Score and RMSE function To asses the performance of the...

In hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).

Description

Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).

Usage

scores(model, Xtest, Ztest, return.rmse = FALSE)

Arguments

model	homGP or hetGP model, including inverse matrices
Xtest	matrix of new design locations
Ztest	corresponding vector of observations, or alternatively, a matrix of size [nrow(Xtest) x number of replicates], a list of size nrow(Xtest) with a least one value per element
return.rmse	if TRUE, return the root mean squared error

References

T. Gneiting, and A. Raftery (2007). Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association, 102(477), 359-378.

hetGP documentation built on Sept. 11, 2024, 6:56 p.m.