GPM
PackagePlots 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.
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)
The GP model fitted by Fit
.
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.
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
.
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
.
A positive integer indicating the response that should be plotted if Model
is multi-response.
A positive integer indicating the number of points used in plotting. Higher values will result in smoother plots.
A string for the label of axis 1
.
A string for the label of axis 2
, if plotting a surface.
A string for the label of the response axis.
A string for the title of the plot.
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.
# NOT RUN {
# See the examples in the fitting function.
# }
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