gpkm {GauPro}

Gaussian process regression model

Description

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

Details

The default kernel is a Matern 5/2 kernel, but factor/character inputs will be given factor kernels.