borehole

Training and test data for the borehole function; see source for background.

datasets

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>.

William Welch

GaSP

Train and Apply a Gaussian Stochastic Process Model

William J. Welch

Yilin Yang

borehole function

A list with the following four data frames:<dl>
<dt>x</dt>
<dd>8-dimensional input for 40 training runs.</dd><dt>y</dt>
<dd>Output (the flow) for the 40 training runs in <code>x</code>.</dd><dt>x_pred</dt>
<dd>8-dimensional input for 1000 test runs at which to predict <code>y</code>.</dd><dt>y_true</dt>
<dd>Output for the 1000 runs in <code>x_pred</code>.</dd>
</dl>

Format

Data for the borehole function — borehole

A list with the following four data frames:<dl>
<dt>x</dt>
<dd>8-dimensional input for 40 training runs.</dd>

<dt>y</dt>
<dd>Output (the flow) for the 40 training runs in <code>x</code>.</dd>

<dt>x_pred</dt>
<dd>8-dimensional input for 1000 test runs at which to predict <code>y</code>.</dd>

<dt>y_true</dt>
<dd>Output for the 1000 runs in <code>x_pred</code>.</dd>


</dl>

borehole: Data for the borehole function

Description

Usage

Arguments

Format