Train and Apply a Gaussian Stochastic Process Model
Cross-validated predictions for a GaSPModel object.
Plot residuals versus each input variable.
Describe the input variables.
Fit a GaSP model.
Create a GaSPModel object.
Execute PlotPredictions, PlotResiduals, PlotStdResiduals, `PlotM...
Plot the estimated joint effects.
Plot the estimated main effects.
Plot true versus predicted output.
Normal quantile-quantile (Q-Q) plot.
Plot standardized residuals versus predictions.
Predict from a GaSPModel object.
Calculate the root mean squared error (RMSE) of prediction
Visualize a GaSPModel object.
Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.