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GauPro_kernel_model: Gaussian process model with kernel

GauPro_kernel_model: Gaussian process model with kernel
In GauPro: Gaussian Process Fitting

GauPro_kernel_modelR Documentation

Gaussian process model with kernel

Description

Class providing object with methods for fitting a GP model. Allows for different kernel and trend functions to be used. The object is an R6 object with many methods that can be called.

'gpkm()' is equivalent to 'GauPro_kernel_model$new()', but is easier to type and gives parameter autocomplete suggestions.

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Methods

new(X, Z, corr="Gauss", verbose=0, separable=T, useC=F, useGrad=T, parallel=T, nug.est=T, ...)

This method is used to create object of this class with X and Z as the data.

update(Xnew=NULL, Znew=NULL, Xall=NULL, Zall=NULL, restarts = 0, param_update = T, nug.update = self$nug.est)

This method updates the model, adding new data if given, then running optimization again.

Public fields

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

nug.min

Minimum value of nugget

nug.max

Maximum value of the nugget.

nug.est

Should the nugget be estimated?

nug

Value of the nugget, is estimated unless told otherwise

param.est

Should the kernel parameters be estimated?

verbose

0 means nothing printed, 1 prints some, 2 prints most.

useGrad

Should grad be used?

useC

Should C code be used?

parallel

Should the code be run in parallel?

parallel_cores

How many cores are there? By default it detects.

kernel

The kernel to determine the correlations.

trend

The trend.

mu_hatX

Predicted trend value for each point in X.

s2_hat

Variance parameter estimate

K

Covariance matrix

Kchol

Cholesky factorization of K

Kinv

Inverse of K

Kinv_Z_minus_mu_hatX

K inverse times Z minus the predicted trend at X.

restarts

Number of optimization restarts to do when updating.

normalize

Should the inputs be normalized?

normalize_mean

If using normalize, the mean of each column.

normalize_sd

If using normalize, the standard deviation of each column.

optimizer

What algorithm should be used to optimize the parameters.

track_optim

Should it track the parameters evaluated while optimizing?

track_optim_inputs

If track_optim is TRUE, this will keep a list of parameters evaluated. View them with plot_track_optim.

track_optim_dev

If track_optim is TRUE, this will keep a vector of the deviance values calculated while optimizing parameters. View them with plot_track_optim.

formula

Formula

convert_formula_data

List for storing data to convert data using the formula

Methods

Public methods

Method new()

Create kernel_model object

Usage
GauPro_kernel_model$new(
  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

Method fit()

Fit model

Usage
GauPro_kernel_model$fit(X, Z)
Arguments
X

Inputs

Z

Outputs

Method update_K_and_estimates()

Update covariance matrix and estimates

Usage
GauPro_kernel_model$update_K_and_estimates()

Method predict()

Predict for a matrix of points

Usage
GauPro_kernel_model$predict(
  XX,
  se.fit = F,
  covmat = F,
  split_speed = F,
  mean_dist = FALSE,
  return_df = TRUE
)
Arguments
XX

points to predict at

se.fit

Should standard error be returned?

covmat

Should covariance matrix be returned?

split_speed

Should the matrix be split for faster predictions?

mean_dist

Should the error be for the distribution of the mean?

return_df

When returning se.fit, should it be returned in a data frame? Otherwise it will be a list, which is faster.

Method pred()

Predict for a matrix of points

Usage
GauPro_kernel_model$pred(
  XX,
  se.fit = F,
  covmat = F,
  split_speed = F,
  mean_dist = FALSE,
  return_df = TRUE
)
Arguments
XX

points to predict at

se.fit

Should standard error be returned?

covmat

Should covariance matrix be returned?

split_speed

Should the matrix be split for faster predictions?

mean_dist

Should the error be for the distribution of the mean?

return_df

When returning se.fit, should it be returned in a data frame? Otherwise it will be a list, which is faster.

Method pred_one_matrix()

Predict for a matrix of points

Usage
GauPro_kernel_model$pred_one_matrix(
  XX,
  se.fit = F,
  covmat = F,
  return_df = FALSE,
  mean_dist = FALSE
)
Arguments
XX

points to predict at

se.fit

Should standard error be returned?

covmat

Should covariance matrix be returned?

return_df

When returning se.fit, should it be returned in a data frame? Otherwise it will be a list, which is faster.

mean_dist

Should the error be for the distribution of the mean?

Method pred_mean()

Predict mean

Usage
GauPro_kernel_model$pred_mean(XX, kx.xx)
Arguments
XX

points to predict at

kx.xx

Covariance of X with XX

Method pred_meanC()

Predict mean using C

Usage
GauPro_kernel_model$pred_meanC(XX, kx.xx)
Arguments
XX

points to predict at

kx.xx

Covariance of X with XX

Method pred_var()

Predict variance

Usage
GauPro_kernel_model$pred_var(XX, kxx, kx.xx, covmat = F)
Arguments
XX

points to predict at

kxx

Covariance of XX with itself

kx.xx

Covariance of X with XX

covmat

Should the covariance matrix be returned?

Method pred_LOO()

leave one out predictions

Usage
GauPro_kernel_model$pred_LOO(se.fit = FALSE)
Arguments
se.fit

Should standard errors be included?

Method pred_var_after_adding_points()

Predict variance after adding points

Usage
GauPro_kernel_model$pred_var_after_adding_points(add_points, pred_points)
Arguments
add_points

Points to add

pred_points

Points to predict at

Method pred_var_after_adding_points_sep()

Predict variance reductions after adding each point separately

Usage
GauPro_kernel_model$pred_var_after_adding_points_sep(add_points, pred_points)
Arguments
add_points

Points to add

pred_points

Points to predict at

Method pred_var_reduction()

Predict variance reduction for a single point

Usage
GauPro_kernel_model$pred_var_reduction(add_point, pred_points)
Arguments
add_point

Point to add

pred_points

Points to predict at

Method pred_var_reductions()

Predict variance reductions

Usage
GauPro_kernel_model$pred_var_reductions(add_points, pred_points)
Arguments
add_points

Points to add

pred_points

Points to predict at

Method plot()

Plot the object

Usage
GauPro_kernel_model$plot(...)
Arguments
...

Parameters passed to cool1Dplot(), plot2D(), or plotmarginal()

Method cool1Dplot()

Make cool 1D plot

Usage
GauPro_kernel_model$cool1Dplot(
  n2 = 20,
  nn = 201,
  col2 = "green",
  xlab = "x",
  ylab = "y",
  xmin = NULL,
  xmax = NULL,
  ymin = NULL,
  ymax = NULL,
  gg = TRUE
)
Arguments
n2

Number of things to plot

nn

Number of things to plot

col2

color

xlab

x label

ylab

y label

xmin

xmin

xmax

xmax

ymin

ymin

ymax

ymax

gg

Should ggplot2 be used to make plot?

Method plot1D()

Make 1D plot

Usage
GauPro_kernel_model$plot1D(
  n2 = 20,
  nn = 201,
  col2 = 2,
  col3 = 3,
  xlab = "x",
  ylab = "y",
  xmin = NULL,
  xmax = NULL,
  ymin = NULL,
  ymax = NULL,
  gg = TRUE
)
Arguments
n2

Number of things to plot

nn

Number of things to plot

col2

Color of the prediction interval

col3

Color of the interval for the mean

xlab

x label

ylab

y label

xmin

xmin

xmax

xmax

ymin

ymin

ymax

ymax

gg

Should ggplot2 be used to make plot?

Method plot2D()

Make 2D plot

Usage
GauPro_kernel_model$plot2D(se = FALSE, mean = TRUE, horizontal = TRUE, n = 50)
Arguments
se

Should the standard error of prediction be plotted?

mean

Should the mean be plotted?

horizontal

If plotting mean and se, should they be next to each other?

n

Number of points along each dimension

Method plotmarginal()

Plot marginal. For each input, hold all others at a constant value and adjust it along it's range to see how the prediction changes.

Usage
GauPro_kernel_model$plotmarginal(npt = 5, ncol = NULL)
Arguments
npt

Number of lines to make. Each line represents changing a single variable while holding the others at the same values.

ncol

Number of columnsfor the plot

Method plotmarginalrandom()

Plot marginal prediction for random sample of inputs

Usage
GauPro_kernel_model$plotmarginalrandom(npt = 100, ncol = NULL)
Arguments
npt

Number of random points to evaluate

ncol

Number of columns in the plot

Method plotkernel()

Plot the kernel

Usage
GauPro_kernel_model$plotkernel(X = self$X)
Arguments
X

X matrix for kernel plot

Method plotLOO()

Plot leave one out predictions for design points

Usage
GauPro_kernel_model$plotLOO()

Method plot_track_optim()

If track_optim, this will plot the parameters in the order they were evaluated.

Usage
GauPro_kernel_model$plot_track_optim(minindex = NULL)
Arguments
minindex

Minimum index to plot.

Method loglikelihood()

Calculate loglikelihood of parameters

Usage
GauPro_kernel_model$loglikelihood(mu = self$mu_hatX, s2 = self$s2_hat)
Arguments
mu

Mean parameters

s2

Variance parameter

Method AIC()

AIC (Akaike information criterion)

Usage
GauPro_kernel_model$AIC()

Method get_optim_functions()

Get optimization functions

Usage
GauPro_kernel_model$get_optim_functions(param_update, nug.update)
Arguments
param_update

Should parameters be updated?

nug.update

Should nugget be updated?

Method param_optim_lower()

Lower bounds of parameters for optimization

Usage
GauPro_kernel_model$param_optim_lower(nug.update)
Arguments
nug.update

Is the nugget being updated?

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage
GauPro_kernel_model$param_optim_upper(nug.update)
Arguments
nug.update

Is the nugget being updated?

Method param_optim_start()

Starting point for parameters for optimization

Usage
GauPro_kernel_model$param_optim_start(nug.update, jitter)
Arguments
nug.update

Is nugget being updated?

jitter

Should there be a jitter?

Method param_optim_start0()

Starting point for parameters for optimization

Usage
GauPro_kernel_model$param_optim_start0(nug.update, jitter)
Arguments
nug.update

Is nugget being updated?

jitter

Should there be a jitter?

Method param_optim_start_mat()

Get matrix for starting points of optimization

Usage
GauPro_kernel_model$param_optim_start_mat(restarts, nug.update, l)
Arguments
restarts

Number of restarts to use

nug.update

Is nugget being updated?

l

Not used

Method optim()

Optimize parameters

Usage
GauPro_kernel_model$optim(
  restarts = self$restarts,
  n0 = 5 * self$D,
  param_update = T,
  nug.update = self$nug.est,
  parallel = self$parallel,
  parallel_cores = self$parallel_cores
)
Arguments
restarts

Number of restarts to do

n0

This many starting parameters are chosen and evaluated. The best ones are used as the starting points for optimization.

param_update

Should parameters be updated?

nug.update

Should nugget be updated?

parallel

Should restarts be done in parallel?

parallel_cores

If running parallel, how many cores should be used?

Method optimRestart()

Run a single optimization restart.

Usage
GauPro_kernel_model$optimRestart(
  start.par,
  start.par0,
  param_update,
  nug.update,
  optim.func,
  optim.grad,
  optim.fngr,
  lower,
  upper,
  jit = T,
  start.par.i
)
Arguments
start.par

Starting parameters

start.par0

Starting parameters

param_update

Should parameters be updated?

nug.update

Should nugget be updated?

optim.func

Function to optimize.

optim.grad

Gradient of function to optimize.

optim.fngr

Function that returns the function value and its gradient.

lower

Lower bounds for optimization

upper

Upper bounds for optimization

jit

Is jitter being used?

start.par.i

Starting parameters for this restart

Method update()

Update the model. Should only give in (Xnew and Znew) or (Xall and Zall).

Usage
GauPro_kernel_model$update(
  Xnew = NULL,
  Znew = NULL,
  Xall = NULL,
  Zall = NULL,
  restarts = self$restarts,
  param_update = self$param.est,
  nug.update = self$nug.est,
  no_update = FALSE
)
Arguments
Xnew

New X values to add.

Znew

New Z values to add.

Xall

All X values to be used. Will replace existing X.

Zall

All Z values to be used. Will replace existing Z.

restarts

Number of optimization restarts.

param_update

Are the parameters being updated?

nug.update

Is the nugget being updated?

no_update

Are no parameters being updated?

Method update_fast()

Fast update when adding new data.

Usage
GauPro_kernel_model$update_fast(Xnew = NULL, Znew = NULL)
Arguments
Xnew

New X values to add.

Znew

New Z values to add.

Method update_params()

Update the parameters.

Usage
GauPro_kernel_model$update_params(..., nug.update)
Arguments
...

Passed to optim.

nug.update

Is the nugget being updated?

Method update_data()

Update the data. Should only give in (Xnew and Znew) or (Xall and Zall).

Usage
GauPro_kernel_model$update_data(
  Xnew = NULL,
  Znew = NULL,
  Xall = NULL,
  Zall = NULL
)
Arguments
Xnew

New X values to add.

Znew

New Z values to add.

Xall

All X values to be used. Will replace existing X.

Zall

All Z values to be used. Will replace existing Z.

Method update_corrparams()

Update correlation parameters. Not the nugget.

Usage
GauPro_kernel_model$update_corrparams(...)
Arguments
...

Passed to self$update()

Method update_nugget()

Update nugget Not the correlation parameters.

Usage
GauPro_kernel_model$update_nugget(...)
Arguments
...

Passed to self$update()

Method deviance()

Calculate the deviance.

Usage
GauPro_kernel_model$deviance(
  params = NULL,
  nug = self$nug,
  nuglog,
  trend_params = NULL
)
Arguments
params

Kernel parameters

nug

Nugget

nuglog

Log of nugget. Only give in nug or nuglog.

trend_params

Parameters for the trend.

Method deviance_grad()

Calculate the gradient of the deviance.

Usage
GauPro_kernel_model$deviance_grad(
  params = NULL,
  kernel_update = TRUE,
  X = self$X,
  nug = self$nug,
  nug.update,
  nuglog,
  trend_params = NULL,
  trend_update = TRUE
)
Arguments
params

Kernel parameters

kernel_update

Is the kernel being updated? If yes, it's part of the gradient.

X

Input matrix

nug

Nugget

nug.update

Is the nugget being updated? If yes, it's part of the gradient.

nuglog

Log of the nugget.

trend_params

Trend parameters

trend_update

Is the trend being updated? If yes, it's part of the gradient.

Method deviance_fngr()

Calculate the deviance along with its gradient.

Usage
GauPro_kernel_model$deviance_fngr(
  params = NULL,
  kernel_update = TRUE,
  X = self$X,
  nug = self$nug,
  nug.update,
  nuglog,
  trend_params = NULL,
  trend_update = TRUE
)
Arguments
params

Kernel parameters

kernel_update

Is the kernel being updated? If yes, it's part of the gradient.

X

Input matrix

nug

Nugget

nug.update

Is the nugget being updated? If yes, it's part of the gradient.

nuglog

Log of the nugget.

trend_params

Trend parameters

trend_update

Is the trend being updated? If yes, it's part of the gradient.

Method grad()

Calculate gradient

Usage
GauPro_kernel_model$grad(XX, X = self$X, Z = self$Z)
Arguments
XX

points to calculate at

X

X points

Z

output points

Method grad_norm()

Calculate norm of gradient

Usage
GauPro_kernel_model$grad_norm(XX)
Arguments
XX

points to calculate at

Method grad_dist()

Calculate distribution of gradient

Usage
GauPro_kernel_model$grad_dist(XX)
Arguments
XX

points to calculate at

Method grad_sample()

Sample gradient at points

Usage
GauPro_kernel_model$grad_sample(XX, n)
Arguments
XX

points to calculate at

n

Number of samples

Method grad_norm2_mean()

Calculate mean of gradient norm squared

Usage
GauPro_kernel_model$grad_norm2_mean(XX)
Arguments
XX

points to calculate at

Method grad_norm2_dist()

Calculate distribution of gradient norm squared

Usage
GauPro_kernel_model$grad_norm2_dist(XX)
Arguments
XX

points to calculate at

Method grad_norm2_sample()

Get samples of squared norm of gradient

Usage
GauPro_kernel_model$grad_norm2_sample(XX, n)
Arguments
XX

points to sample at

n

Number of samples

Method hessian()

Calculate Hessian

Usage
GauPro_kernel_model$hessian(XX, as_array = FALSE)
Arguments
XX

Points to calculate Hessian at

as_array

Should result be an array?

Method gradpredvar()

Calculate gradient of the predictive variance

Usage
GauPro_kernel_model$gradpredvar(XX)
Arguments
XX

points to calculate at

Method sample()

Sample at rows of XX

Usage
GauPro_kernel_model$sample(XX, n = 1)
Arguments
XX

Input matrix

n

Number of samples

Method optimize_fn()

Optimize any function of the GP prediction over the valid input space. If there are inputs that should only be optimized over a discrete set of values, specify 'mopar' for all parameters. Factor inputs will be handled automatically.

Usage
GauPro_kernel_model$optimize_fn(
  fn = NULL,
  lower = apply(self$X, 2, min),
  upper = apply(self$X, 2, max),
  n0 = 100,
  minimize = FALSE,
  fn_args = NULL,
  gr = NULL,
  fngr = NULL,
  mopar = NULL,
  groupeval = FALSE
)
Arguments
fn

Function to optimize

lower

Lower bounds to search within

upper

Upper bounds to search within

n0

Number of points to evaluate in initial stage

minimize

Are you trying to minimize the output?

fn_args

Arguments to pass to the function fn.

gr

Gradient of function to optimize.

fngr

Function that returns list with names elements "fn" for the function value and "gr" for the gradient. Useful when it is slow to evaluate and fn/gr would duplicate calculations if done separately.

mopar

List of parameters using mixopt

groupeval

Can a matrix of points be evaluated? Otherwise just a single point at a time.

Method EI()

Calculate expected improvement

Usage
GauPro_kernel_model$EI(x, minimize = FALSE, eps = 0, return_grad = FALSE, ...)
Arguments
x

Vector to calculate EI of, or matrix for whose rows it should be calculated

minimize

Are you trying to minimize the output?

eps

Exploration parameter

return_grad

Should the gradient be returned?

...

Additional args

Method maxEI()

Find the point that maximizes the expected improvement. If there are inputs that should only be optimized over a discrete set of values, specify 'mopar' for all parameters.

Usage
GauPro_kernel_model$maxEI(
  lower = apply(self$X, 2, min),
  upper = apply(self$X, 2, max),
  n0 = 100,
  minimize = FALSE,
  eps = 0,
  dontconvertback = FALSE,
  EItype = "corrected",
  mopar = NULL,
  usegrad = FALSE
)
Arguments
lower

Lower bounds to search within

upper

Upper bounds to search within

n0

Number of points to evaluate in initial stage

minimize

Are you trying to minimize the output?

eps

Exploration parameter

dontconvertback

If data was given in with a formula, should it converted back to the original scale?

EItype

Type of EI to calculate. One of "EI", "Augmented", or "Corrected"

mopar

List of parameters using mixopt

usegrad

Should the gradient be used when optimizing? Can make it faster.

Method maxqEI()

Find the multiple points that maximize the expected improvement. Currently only implements the constant liar method.

Usage
GauPro_kernel_model$maxqEI(
  npoints,
  method = "pred",
  lower = apply(self$X, 2, min),
  upper = apply(self$X, 2, max),
  n0 = 100,
  minimize = FALSE,
  eps = 0,
  EItype = "corrected",
  dontconvertback = FALSE,
  mopar = NULL
)
Arguments
npoints

Number of points to add

method

Method to use for setting the output value for the points chosen as a placeholder. Can be one of: "CL" for constant liar, which uses the best value seen yet; or "pred", which uses the predicted value, also called the Believer method in literature.

lower

Lower bounds to search within

upper

Upper bounds to search within

n0

Number of points to evaluate in initial stage

minimize

Are you trying to minimize the output?

eps

Exploration parameter

EItype

Type of EI to calculate. One of "EI", "Augmented", or "Corrected"

dontconvertback

If data was given in with a formula, should it converted back to the original scale?

mopar

List of parameters using mixopt

Method KG()

Calculate Knowledge Gradient

Usage
GauPro_kernel_model$KG(x, minimize = FALSE, eps = 0, current_extreme = NULL)
Arguments
x

Point to calculate at

minimize

Is the objective to minimize?

eps

Exploration parameter

current_extreme

Used for recursive solving

Method AugmentedEI()

Calculated Augmented EI

Usage
GauPro_kernel_model$AugmentedEI(
  x,
  minimize = FALSE,
  eps = 0,
  return_grad = F,
  ...
)
Arguments
x

Vector to calculate EI of, or matrix for whose rows it should be calculated

minimize

Are you trying to minimize the output?

eps

Exploration parameter

return_grad

Should the gradient be returned?

...

Additional args

f

The reference max, user shouldn't change this.

Method CorrectedEI()

Calculated Augmented EI

Usage
GauPro_kernel_model$CorrectedEI(
  x,
  minimize = FALSE,
  eps = 0,
  return_grad = F,
  ...
)
Arguments
x

Vector to calculate EI of, or matrix for whose rows it should be calculated

minimize

Are you trying to minimize the output?

eps

Exploration parameter

return_grad

Should the gradient be returned?

...

Additional args

Method importance()

Feature importance

Usage
GauPro_kernel_model$importance(plot = TRUE, print_bars = TRUE)
Arguments
plot

Should the plot be made?

print_bars

Should the importances be printed as bars?

Method print()

Print this object

Usage
GauPro_kernel_model$print()

Method summary()

Summary

Usage
GauPro_kernel_model$summary(...)
Arguments
...

Additional arguments

Method clone()

The objects of this class are cloneable with this method.

Usage
GauPro_kernel_model$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

https://scikit-learn.org/stable/modules/permutation_importance.html#id2

Examples

n <- 12
x <- matrix(seq(0,1,length.out = n), ncol=1)
y <- sin(2*pi*x) + rnorm(n,0,1e-1)
gp <- GauPro_kernel_model$new(X=x, Z=y, kernel="gauss")
gp$predict(.454)
gp$plot1D()
gp$cool1Dplot()

n <- 200
d <- 7
x <- matrix(runif(n*d), ncol=d)
f <- function(x) {x[1]*x[2] + cos(x[3]) + x[4]^2}
y <- apply(x, 1, f)
gp <- GauPro_kernel_model$new(X=x, Z=y, kernel=Gaussian)

GauPro documentation built on Aug. 26, 2025, 9:08 a.m.

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README.md A Guide to the GauPro R package" Derivatives for estimating Gaussian process parameters" Introduction to Gaussian Processes" Leave-one-out cross-validation and error correction" Spatial derivatives of Gaussian process models"

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