Package 'PEcAn.emulator'

Title: Gausian Process Emulator
Description: Implementation of a Gaussian Process model (both likelihood and bayesian approaches) for kriging and model emulation. Includes functions for sampling design and prediction.
Authors: Mike Dietze [aut, cre], University of Illinois, NCSA [cph]
Maintainer: Mike Dietze <[email protected]>
License: BSD_3_clause + file LICENSE
Version: 1.8.0.9000
Built: 2024-12-17 17:40:08 UTC
Source: https://github.com/PecanProject/pecan

Help Index

arate

Description

Acceptance rate

Usage

arate(x)

Arguments

x

vector of MCMC samples

Author(s)

Michael Dietze

calcSpatialCov

Description

calcSpatialCov

Usage

calcSpatialCov(x, ...)

Arguments

x

either a spatial distance matrix or a list of component spatial distance matrices
...

Additional arguments

Author(s)

Michael Dietze

calcSpatialCov.list

Description

Currently assumes an exponential spatial dependency

Usage

## S3 method for class 'list'
calcSpatialCov(d, psi, tau)

Arguments

d

list of component spatial distance matrices
psi

spatial corr
tau

spatial var

Details

can make gaussian by passing squared distance matrix

Author(s)

Michael Dietze

calcSpatialCov.matrix

Description

Currently assumes an exponential spatial dependency

Usage

## S3 method for class 'matrix'
calcSpatialCov(d, psi, tau)

Arguments

d

spatial distance matrix
psi

spatial corr
tau

spatial var

Details

can make gaussian by passing squared distance matrix

Author(s)

Michael Dietze

distance

Description

distance

Usage

distance(x, power = 1)

Arguments

x

matrix of locations in physical or parameter space
power

exponent used for calculating distance, default value of 2 = Pythagorean distance

Value

dst

Author(s)

Michael Dietze

distance.matrix

Description

distance.matrix

Usage

distance.matrix(x, power = 1, dim = 2)

Arguments

x

matrix of locations in physical or parameter space
power

exponent used for calculating distance, default value of 2 = Pythagorean distance
dim

dimenstion

Value

d

Author(s)

Michael Dietze

distance12.matrix

Description

distance12.matrix

Usage

distance12.matrix(x, n1, power = 1)

Arguments

x

matrix of locations in parameter space
n1

number of rows in the original dataset
power

exponent used for calculating distance, default value of 2 = Pythagorean distance

Value

d

Author(s)

Michael Dietze

GaussProcess

Description

GaussProcess

Usage

GaussProcess(
  x,
  y,
  isotropic = TRUE,
  nugget = TRUE,
  method = "bayes",
  ngibbs = 5000,
  burnin = 1000,
  thin = 1,
  jump.ic = c(1.1, 0.2),
  prior = "IG",
  mix = "joint",
  psi = NULL,
  zeroMean = FALSE,
  exclude = NULL,
  ...
)

Arguments

x

set of independent variables
y

dependent variable
isotropic

Boolean indicating whether the GP is fit isotropically. If FALSE, distances are calculated deparately for each direction
nugget

allows additional error in Y rather than fix interpolation to go through points
method

method for calculating correlations
ngibbs

number of MCMC iterations (per chain) to run
burnin

Number of samples to discard as burnin (auto must be FALSE)
thin

thinning of the matrix to make things faster. Default is to thin to 1
jump.ic

initial condition for jump standard deviation.
prior

'unif', 'IG'
mix

joint=mix over psi simultanously, each=mix over psi individually
psi

spatial corr
zeroMean

True if mean is 0, else false
exclude

<- isn't used anywhere, should be dropped
...

Additional arguments

Author(s)

Michael Dietze

gp_mle

Description

gp_mle

Usage

gp_mle(theta, d, nugget, myY, maxval = Inf)

Arguments

theta

proposed parameter vector: [mu, tauw, tauv, phi1...phiK]
d

spatial distance matrix
nugget

allows additional error in Y rather than fix interpolation to go through points
myY

vector of observed data
maxval

maximum value

Value

val

Author(s)

Michael Dietze

gp_mle2

Description

zero mean version

Usage

gp_mle2(theta, d, nugget, myY, maxval = Inf)

Arguments

theta

proposed parameter vector: [mu, tauw, tauv, phi1...phiK]
d

spatial distance matrix
nugget

allows additional error in Y rather than fix interpolation to go through points
myY

vector of observed data
maxval

maximum value

groupid

Description

groupid

Usage

groupid(x)

Arguments

x

matrix of parameter values

Author(s)

Michael Dietze

jump

Description

jump

Usage

jump(ic = 0, rate = 0.4, ...)

Arguments

ic

optional data vector
rate

target acceptance rate
...

Addtional arguments

Author(s)

Michael Dietze

define a class for automatically tuning jump distributions

Description

define a class for automatically tuning jump distributions

Author(s)

Michael Dietze

ldinvgamma

Description

Log-dinvgamma, based on MCMCpack

Usage

ldinvgamma(x, shape, scale = 1)

Arguments

x

vector of quantiles
shape, scale

shape and scale parameters for the inverse Gamma distribution

Value

log.density

Author(s)

Michael Dietze

lhc

Description

Latin Hyper Cube

Usage

lhc(x, n.samp)

Arguments

x

<- list (n.dim x 2)
n.samp

number of samples

Details

Simple uniform sampling with LHC permutation

Author(s)

Michael Dietze

mvjump

Description

multivariate version

Usage

mvjump(ic = 0, rate = 0.4, nc = 2, ...)

Arguments

ic

optional data vector
rate

target acceptance rate
nc

NetCDF object containing target variable
...

Additional arguments

multivariate version of jump class

Description

multivariate version of jump class

nderiv

Description

nderiv

Usage

nderiv(x, y)

Arguments

x

Name of variable to plot on X axis
y

Name of variable to plot on Y axis

Value

der

Author(s)

Michael Dietze

p

Description

p

Usage

p(x, ...)

Arguments

x

jump distribution
...

Additional arguments

p.jump

Description

p.jump

Usage

## S3 method for class 'jump'
p(jmp)

Arguments

jmp

jump parameter

Author(s)

Michael Dietze

p.mvjump

Description

p.mvjump

Usage

## S3 method for class 'mvjump'
p(jmp)

Arguments

jmp

jump parameter

plot.jump

Description

plot.jump

Usage

## S3 method for class 'jump'
plot(jmp)

Arguments

jmp

jump parameter

Author(s)

Michael Dietze

plot.mvjump

Description

plot.mvjump

Usage

## S3 method for class 'mvjump'
plot(jmp)

Arguments

jmp

jump parameter

Author(s)

Michael Dietze

predict.density

Description

Simple interpolation of a density object to new points

Usage

## S3 method for class 'density'
predict(den, xnew)

Arguments

den

density object
xnew

new x coordinate

Value

ynew

Author(s)

Michael Dietze

predict.GP

Description

predict.GP

Usage

## S3 method for class 'GP'
predict(gp, xpred, cI = NULL, pI = NULL, splinefcns = NULL)

Arguments

gp

Gaussian Process
xpred

value of x where prediction should be made
cI

credible interval
pI

prediction interval
splinefcns

spline functions

Author(s)

Michael Dietze

summarize.GP

Description

summarize.GP

Usage

summarize.GP(gp, pdf_file = NULL, txt_file = NULL)

Arguments

gp

Gaussian Process
pdf_file

filename you want figures written out to
txt_file

filename you want figures written out to

Author(s)

Michael Dietze

update.jump

Description

update.jump

Usage

## S3 method for class 'jump'
update(jmp, chain)

Arguments

jmp

jump parameter
chain

mcmc chain

Value

jmp updated jump parameter

Author(s)

Michael Dietze

update.mvjump

Description

update.mvjump

Usage

## S3 method for class 'mvjump'
update(jmp, chain)

Arguments

jmp

jump parameter
chain

mcmc chain