Draw: The Plotting Function of 'GPM' Package
In GPM: Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets

Description Usage Arguments References See Also Examples

Plots the predicted response along with the assocaited uncertainty via the GP model fitted by Fit. Accepts multi-input and multi-output models. See Arguments for more details on the options.

1
2
3

Draw(Model, Plot_wrt, LB = NULL, UB = NULL, Values = NULL,
  Response_ID = NULL, res = 15, X1Label = NULL, X2Label = NULL,
  YLabel = NULL, Title = NULL, PI95 = NULL)

`Model`	The GP model fitted by `Fit`.
`Plot_wrt`	A binary vector of length `p` where `p` is the dimension of the inputs in `Model`. A maximum (minimum) of `2` (`1`) elements can be `1`. The elemenets set to `1`, would correspond to the plotting axes.
`LB, UB`	Vectors of length `sum(Plot_wrt)` indicating the lower and upper bounds used for plotting. The first (second) element corresponds to the first (second) non-zero element of `Plot_wrt`.
`Values`	A vector of length `p-sum(Plot_wrt)`. The values are assigned to the variables NOT used in plotting and correspond to the zeros in `Plot_wrt`.
`Response_ID`	A positive integer indicating the response that should be plotted if `Model` is multi-response.
`res`	A positive integer indicating the number of points used in plotting. Higher values will result in smoother plots.
`X1Label`	A string for the label of axis `1`.
`X2Label`	A string for the label of axis `2`, if plotting a surface.
`YLabel`	A string for the label of the response axis.
`Title`	A string for the title of the plot.
`PI95`	Flag (a scalar) indicating whether the `95%` prediction interval should be plotted. Set it to a non-zero value to turn the flag "on".

Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.
Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.