Fits a Gaussian process regression model to data.
An R6 object is returned with many methods.
`gpkm()` is an alias for `GauPro_kernel_model$new()`. For full documentation, see documentation for `GauPro_kernel_model`.
Standard methods that work include `plot()`, `summary()`, and `predict()`.
Usage
gpkm(
X,
Z,
kernel,
trend,
verbose = 0,
useC = TRUE,
useGrad = TRUE,
parallel = FALSE,
parallel_cores = "detect",
nug = 1e-06,
nug.min = 1e-08,
nug.max = 100,
nug.est = TRUE,
param.est = TRUE,
restarts = 0,
normalize = FALSE,
optimizer = "L-BFGS-B",
track_optim = FALSE,
formula,
data,
...
)Arguments
- X
Matrix whose rows are the input points
- Z
Output points corresponding to X
- kernel
The kernel to use. E.g., Gaussian$new().
- trend
Trend to use. E.g., trend_constant$new().
- verbose
Amount of stuff to print. 0 is little, 2 is a lot.
- useC
Should C code be used when possible? Should be faster.
- useGrad
Should the gradient be used?
- parallel
Should code be run in parallel? Make optimization faster but uses more computer resources.
- parallel_cores
When using parallel, how many cores should be used?
- nug
Value for the nugget. The starting value if estimating it.
- nug.min
Minimum allowable value for the nugget.
- nug.max
Maximum allowable value for the nugget.
- nug.est
Should the nugget be estimated?
- param.est
Should the kernel parameters be estimated?
- restarts
How many optimization restarts should be used when estimating parameters?
- normalize
Should the data be normalized?
- optimizer
What algorithm should be used to optimize the parameters.
- track_optim
Should it track the parameters evaluated while optimizing?
- formula
Formula for the data if giving in a data frame.
- data
Data frame of data. Use in conjunction with formula.
- ...
Not used