Package 'GPvecchia' reference manual

Title:	Scalable Gaussian-Process Approximations
Description:	Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.
Authors:	Matthias Katzfuss [aut], Marcin Jurek [aut, cre], Daniel Zilber [aut], Wenlong Gong [aut], Joe Guinness [ctb], Jingjie Zhang [ctb], Florian Schaefer [ctb]
Maintainer:	Marcin Jurek <[email protected]>
License:	GPL (>= 2)
Version:	0.1.7
Built:	2024-11-08 06:34:47 UTC
Source:	CRAN

Vecchia Laplace extension of GPVecchia for non-Gaussian data

Description

Vecchia Laplace extension of GPVecchia for non-Gaussian data

Usage

calculate_posterior_VL(
  z,
  vecchia.approx,
  likelihood_model = c("gaussian", "logistic", "poisson", "gamma", "beta", "gamma_alt"),
  covparms,
  covmodel = "matern",
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  max.iter = 50,
  convg = 1e-06,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  verbose = FALSE
)
calculate_posterior_VL(
  z,
  vecchia.approx,
  likelihood_model = c("gaussian", "logistic", "poisson", "gamma", "beta", "gamma_alt"),
  covparms,
  covmodel = "matern",
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  max.iter = 50,
  convg = 1e-06,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  verbose = FALSE
)

Arguments

`z`	an array of real numbers representing observations
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`likelihood_model`	text describing likelihood model to be used for observations. Can be "gaussian","logistic", "poisson", "gamma", or "beta"
`covparms`	covariance parameters as a vector
`covmodel`	type of the model covariance or selected elements of the covariance matrix
`likparms`	likelihood parameters for the likelihood_model, as a list. Default values are sqrt(.1) for Gaussian noise and 2 for the alpha parameter for Gamma data.
`max.iter`	maximum iterations to perform
`convg`	convergence criteria. End iterations if the Newton step is this small
`return_all`	Return additional posterior covariance terms, TRUE or FALSE
`y_init`	Specify initial guess for posterior mode
`prior_mean`	specify the prior latent mean
`verbose`	if TRUE messages about the posterior estimation will be displayed

Value

multivariate normal posterior parameters calculated by the Vecchia-Laplace approximation

Examples

z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
calculate_posterior_VL(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))
z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
calculate_posterior_VL(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))

create the sparse triangular L matrix for specific parameters

Description

create the sparse triangular L matrix for specific parameters

Usage

createL(vecchia.approx, covmodel, covparms = NULL)
createL(vecchia.approx, covmodel, covparms = NULL)

Arguments

`vecchia.approx`	object returned by `vecchia_specify`
`covmodel`	covariance model. currently implemented: matern: with covparms (var,range,smoothness) esqe: exponential + squared exp with covparms (var1,range1,var2,range2) If covmodel is a function it has to be able to take a distance matrix and return a vector with distances which is of length k.
`covparms`	vector of covariance parameters

Value

list containing the sparse lower triangular L,

Examples

z=rnorm(9); locs=matrix(1:9,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
L = createL(vecchia.approx, covparms=c(1,2,.5), 'matern')
z=rnorm(9); locs=matrix(1:9,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
L = createL(vecchia.approx, covparms=c(1,2,.5), 'matern')

create the sparse triangular U matrix for specific parameters

Description

create the sparse triangular U matrix for specific parameters

Usage

createU(vecchia.approx, covparms, nuggets, covmodel = "matern")
createU(vecchia.approx, covparms, nuggets, covmodel = "matern")

Arguments

`vecchia.approx`	object returned by `vecchia_specify`
`covparms`	vector of covariance parameters
`nuggets`	nugget variances – if a scalar is provided, variance is assumed constant
`covmodel`	covariance model. currently implemented:

Value

list containing the sparse upper triangular U, plus additional objects required for other functions

Examples

z=rnorm(9); locs=matrix(1:9,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
U.obj=createU(vecchia.approx,covparms=c(1,2,.5),nuggets=.2)
z=rnorm(9); locs=matrix(1:9,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
U.obj=createU(vecchia.approx,covparms=c(1,2,.5),nuggets=.2)

extract the required elements from the covariance matrix

Description

This function takes the entire covariance matrix and creates a matrix of covariances based on the vecchia approximatino object

Usage

getMatCov(V, covariances, factor = FALSE)
getMatCov(V, covariances, factor = FALSE)

Arguments

`V`	the object returned by vecchia_specify
`covariances`	The full covariance matrix or a covariance function
`factor`	True if we are passing a factor of a matrix

Value

matrix of size n x (m+1) with only those elements that are used by the incomplete Cholesky decomposition

Calculate the covariance values required by HV for matrix factors passed as sparse matrices

Description

Calculate the covariance values required by HV for matrix factors passed as sparse matrices

Usage

getMatCovFromFactorCpp(F, revNNarray)
getMatCovFromFactorCpp(F, revNNarray)

Arguments

`F`	factor of a matrix in a sparse format
`revNNarray`	array with the neighbourhood structure

Value

matrix with covariance values

GPvecchia: fast, scalable Gaussian process approximations

Description

The package can be used for parameter inference and prediction for Gaussian and non-Gaussian spatial data using many popular GP approximation methods.

Incomplete Cholesky decomposition of a sparse matrix passed in the compressed sparse row format

Description

Incomplete Cholesky decomposition of a sparse matrix passed in the compressed sparse row format

Usage

ic0(ptrs, inds, vals)
ic0(ptrs, inds, vals)

Arguments

`ptrs`	pointers to the beginning of the row
`inds`	indices of nonzero elements in a row
`vals`	nonzero values

Value

vector of the values of the incomplete Cholesky factor

Wrapper for incomplete Cholesky decomposition

Description

Wrapper for incomplete Cholesky decomposition

Usage

ichol(M, S = NULL)
ichol(M, S = NULL)

Arguments

`M`	the matrix to be decomposed
`S`	sparsity pattern matrix given

Value

the incomplete Cholesky factor in the sparse format

Examples

A = matrix(runif(25), ncol = 5)
A = t(A) * A + 2 * Matrix::Diagonal(5)
S = Matrix::Matrix(c(rep(1, 5), c(0, 1, 1, 0, 0), c(0, 0, 1, 0, 1),
c(0, 0, 0, 1, 0), c(0, 0, 0, 0, 1)), ncol = 5, byrow = TRUE)
I1 = ichol(A, S)
I2 = ichol(A * S)

A = matrix(runif(25), ncol = 5)
A = t(A) * A + 2 * Matrix::Diagonal(5)
S = Matrix::Matrix(c(rep(1, 5), c(0, 1, 1, 0, 0), c(0, 0, 1, 0, 1),
c(0, 0, 0, 1, 0), c(0, 0, 0, 0, 1)), ncol = 5, byrow = TRUE)
I1 = ichol(A, S)
I2 = ichol(A * S)

Calculate Matern covariance function

Description

Calculate Matern covariance function

Usage

MaternFun(distmat, covparms)
MaternFun(distmat, covparms)

Arguments

`distmat`	A matrix with distances between points
`covparms`	A vector with parameters (marg. variance, range, smoothness)

Value

A matrix with covariance values corresponding to the distance matrix

Sorted coordinate ordering

Description

Return the ordering of locations sorted along one of the coordinates or the sum of multiple coordinates

Usage

order_coordinate(locs, coordinate)
order_coordinate(locs, coordinate)

Arguments

`locs`	A matrix of locations. Each row of `locs` contains a location, which can be a point in Euclidean space R^d, a point in space-time R^d x T, a longitude and latitude (in degrees) giving a point on the sphere, or a longitude, latitude, and time giving a point in the sphere-time domain.
`coordinate`	integer or vector of integers in {1,...,d}. If a single integer, coordinates are ordered along that coordinate. If multiple integers, coordinates are ordered according to the sum of specified coordinate values. For example, when `d=2`, `coordinate = c(1,2)` orders from bottom left to top right.

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the first location.

Examples

n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord1 <- order_coordinate(locs, 1 )
ord12 <- order_coordinate(locs, c(1,2) )
n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord1 <- order_coordinate(locs, 1 )
ord12 <- order_coordinate(locs, c(1,2) )

Distance to specified point ordering

Description

Return the ordering of locations increasing in their distance to some specified location

Usage

order_dist_to_point(locs, loc0, lonlat = FALSE)
order_dist_to_point(locs, loc0, lonlat = FALSE)

Arguments

`locs`	A matrix of locations. Each row of `locs` contains a location, which can be a point in Euclidean space R^d, a point in space-time R^d x T, a longitude and latitude (in degrees) giving a point on the sphere, or a longitude, latitude, and time giving a point in the sphere-time domain.
`loc0`	A vector containing a single location in R^d.
`lonlat`	TRUE/FALSE whether locations are longitudes and latitudes.

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the location nearest to loc0.

Examples

n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
loc0 <- c(1/2,1/2)
ord <- order_dist_to_point(locs,loc0)
n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
loc0 <- c(1/2,1/2)
ord <- order_dist_to_point(locs,loc0)

Maximum minimum distance ordering

Description

Return the indices of an exact maximum-minimum distance ordering. The first point is chosen as the "center" point, minimizing L2 distance. Dimensions d=2 and d=3 handled separately, dimensions d=1 and d>3 handled similarly. Algorithm is exact and scales quasilinearly.

Usage

order_maxmin_exact(locs)
order_maxmin_exact(locs)

Arguments

locs

Observation locations

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the first location.

Examples

n=100; locs <- cbind(runif(n),runif(n))
ord <- order_maxmin_exact(locs)

n=100; locs <- cbind(runif(n),runif(n))
ord <- order_maxmin_exact(locs)

Maximum minimum distance ordering for prediction

Description

Usage

order_maxmin_exact_obs_pred(locs, locs_pred)
order_maxmin_exact_obs_pred(locs, locs_pred)

Arguments

`locs`	Observation locations
`locs_pred`	Prediction locations

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the first location.

Examples

n=100; locs <- cbind(runif(n),runif(n))
locs_pred = cbind(runif(n), runif(n))
ord <- order_maxmin_exact_obs_pred(locs, locs_pred)

n=100; locs <- cbind(runif(n),runif(n))
locs_pred = cbind(runif(n), runif(n))
ord <- order_maxmin_exact_obs_pred(locs, locs_pred)

Middle-out ordering

Description

Return the ordering of locations increasing in their distance to the average location

Usage

order_middleout(locs, lonlat = FALSE)
order_middleout(locs, lonlat = FALSE)

Arguments

`locs`	A matrix of locations. Each row of `locs` contains a location, which can be a point in Euclidean space R^d, a point in space-time R^d x T, a longitude and latitude (in degrees) giving a point on the sphere, or a longitude, latitude, and time giving a point in the sphere-time domain.
`lonlat`	TRUE/FALSE whether locations are longitudes and latitudes.

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the location nearest the center.

Examples

n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord <- order_middleout(locs)
n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord <- order_middleout(locs)

Outside-in ordering

Description

Return the ordering of locations decreasing in their distance to the average location. Reverses middleout.

Usage

order_outsidein(locs, lonlat = FALSE)
order_outsidein(locs, lonlat = FALSE)

Arguments

`locs`	A matrix of locations. Each row of `locs` contains a location, which can be a point in Euclidean space R^d, a point in space-time R^d x T, a longitude and latitude (in degrees) giving a point on the sphere, or a longitude, latitude, and time giving a point in the sphere-time domain.
`lonlat`	TRUE/FALSE whether locations are longitudes and latitudes.

Value

A vector of indices giving the ordering, i.e. the first element of this vector is the index of the location farthest from the center.

Examples

n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord <- order_outsidein(locs)
n <- 100             # Number of locations
d <- 2               # dimension of domain
locs <- matrix( runif(n*d), n, d )
ord <- order_outsidein(locs)

selected inverse of a sparse matrix

Description

selected inverse of a sparse matrix

Usage

SelInv(cholmat)
SelInv(cholmat)

Arguments

cholmat

cholesky factor L of a positive definite sparseMatrix A

Value

sparse inverse of A, with same sparsity pattern as L

Examples

A=Matrix::sparseMatrix(1:9,1:9,x=4); L=Matrix::chol(A)
SelInv(L)
A=Matrix::sparseMatrix(1:9,1:9,x=4); L=Matrix::chol(A)
SelInv(L)

compute covariance matrix from V.ord Do not run this function for large n or n.p!!!

Description

compute covariance matrix from V.ord Do not run this function for large n or n.p!!!

Usage

V2covmat(preds)
V2covmat(preds)

Arguments

preds

Object returned by vecchia_prediction()

Value

Covariance matrix at all locations in original order

Examples

z=rnorm(5)
locs=matrix(1:5,ncol=1)
vecchia_specify=function(z,locs,m=5,locs.pred=(1:5)+.5)
V2covmat(vecchia_prediction(vecchia.approx,covparms=c(1,2,.5),nuggets=.2))
z=rnorm(5)
locs=matrix(1:5,ncol=1)
vecchia_specify=function(z,locs,m=5,locs.pred=(1:5)+.5)
V2covmat(vecchia_prediction(vecchia.approx,covparms=c(1,2,.5),nuggets=.2))

estimate mean and covariance parameters of a Matern covariance function using Vecchia

Description

estimate mean and covariance parameters of a Matern covariance function using Vecchia

Usage

vecchia_estimate(
  data,
  locs,
  X,
  m = 20,
  covmodel = "matern",
  theta.ini,
  output.level = 1,
  reltol = sqrt(.Machine$double.eps),
  ...
)
vecchia_estimate(
  data,
  locs,
  X,
  m = 20,
  covmodel = "matern",
  theta.ini,
  output.level = 1,
  reltol = sqrt(.Machine$double.eps),
  ...
)

Arguments

`data`	data vector of length n
`locs`	n x d matrix of spatial locations
`X`	n x p matrix of trend covariates. default is vector of ones (constant trend). set to NULL if data are already detrended
`m`	number of neighbors for vecchia approximation. default is 20
`covmodel`	covariance model. default is Matern. see `vecchia_likelihood` for details.
`theta.ini`	initial values of covariance parameters. nugget variance must be last.
`output.level`	passed on to trace in the `stats::optim` function
`reltol`	tolerance for the optimization function; by default set to the sqrt of machine precision
`...`	additional input parameters for `vecchia_specify`

Value

object containing detrended data z, trend coefficients beta.hat, covariance parameters theta.hat, and other quantities necessary for prediction

Examples


n=10^2; locs=cbind(runif(n),runif(n))
covparms=c(1,.1,.5); nuggets=rep(.1,n)
Sigma=exp(-fields::rdist(locs)/covparms[2])+diag(nuggets)
z=as.numeric(t(chol(Sigma))%*%rnorm(n));
data=z+1
vecchia.est=vecchia_estimate(data,locs,theta.ini=c(covparms,nuggets[1]))

n=10^2; locs=cbind(runif(n),runif(n))
covparms=c(1,.1,.5); nuggets=rep(.1,n)
Sigma=exp(-fields::rdist(locs)/covparms[2])+diag(nuggets)
z=as.numeric(t(chol(Sigma))%*%rnorm(n));
data=z+1
vecchia.est=vecchia_estimate(data,locs,theta.ini=c(covparms,nuggets[1]))

Wrapper for VL version of vecchia_likelihood

Description

Wrapper for VL version of vecchia_likelihood

Usage

vecchia_laplace_likelihood(
  z,
  vecchia.approx,
  likelihood_model,
  covparms,
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  covmodel = "matern",
  max.iter = 50,
  convg = 1e-05,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  vecchia.approx.IW = NA
)
vecchia_laplace_likelihood(
  z,
  vecchia.approx,
  likelihood_model,
  covparms,
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  covmodel = "matern",
  max.iter = 50,
  convg = 1e-05,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  vecchia.approx.IW = NA
)

Arguments

`z`	an array of real numbers representing observations
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`likelihood_model`	text describing likelihood model to be used for observations
`covparms`	covariance parameters as a vector
`likparms`	likelihood parameters for the likelihood_model, as a list
`covmodel`	describes the covariance model, "matern" by default
`max.iter`	maximum iterations to perform
`convg`	convergence criteria. End iterations if the Newton step is this small
`return_all`	Return additional posterior covariance terms
`y_init`	Specify initial guess for posterior mode
`prior_mean`	specify the prior latent mean
`vecchia.approx.IW`	an optional vecchia approximation object, can reduce computation if method is called repeatedly

Value

(multivariate normal) loglikelihood implied by the Vecchia approximation

Examples

z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
vecchia_laplace_likelihood(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))
z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
vecchia_laplace_likelihood(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))

Wrapper for VL version of vecchia_likelihood

Description

Wrapper for VL version of vecchia_likelihood

Usage

vecchia_laplace_likelihood_from_posterior(
  z,
  posterior,
  vecchia.approx,
  likelihood_model,
  covparms,
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  covmodel = "matern",
  max.iter = 50,
  convg = 1e-05,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  vecchia.approx.IW = NA
)
vecchia_laplace_likelihood_from_posterior(
  z,
  posterior,
  vecchia.approx,
  likelihood_model,
  covparms,
  likparms = list(alpha = 2, sigma = sqrt(0.1)),
  covmodel = "matern",
  max.iter = 50,
  convg = 1e-05,
  return_all = FALSE,
  y_init = NA,
  prior_mean = rep(0, length(z)),
  vecchia.approx.IW = NA
)

Arguments

`z`	an array of real numbers representing observations
`posterior`	posterior distribution obtained from calculate_posterior_VL()
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`likelihood_model`	text describing likelihood model to be used for observations
`covparms`	covariance parameters as a vector
`likparms`	likelihood parameters for the likelihood_model, as a list
`covmodel`	describes the covariance model, "matern" by default
`max.iter`	maximum iterations to perform
`convg`	convergence criteria. End iterations if the Newton step is this small
`return_all`	Return additional posterior covariance terms
`y_init`	Specify initial guess for posterior mode
`prior_mean`	specify the prior latent mean
`vecchia.approx.IW`	an optional vecchia approximation object, can reduce computation if method is called repeatedly

Value

(multivariate normal) loglikelihood implied by the Vecchia approximation

Wrapper for VL version of vecchia_prediction

Description

Wrapper for VL version of vecchia_prediction

Usage

vecchia_laplace_prediction(
  vl_posterior,
  vecchia.approx,
  covparms,
  pred.mean = 0,
  var.exact = FALSE,
  covmodel = "matern",
  return.values = "all"
)
vecchia_laplace_prediction(
  vl_posterior,
  vecchia.approx,
  covparms,
  pred.mean = 0,
  var.exact = FALSE,
  covmodel = "matern",
  return.values = "all"
)

Arguments

`vl_posterior`	a posterior estimate object produced by calculate_posterior_VL
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`covparms`	covariance parameters as a vector
`pred.mean`	provides the prior latent mean for the prediction locations
`var.exact`	should prediction variances be computed exactly, or is a (faster) approximation acceptable
`covmodel`	covariance model, 'matern' by default.
`return.values`	either 'mean' only, 'meanvar', 'meanmat', or 'all'

Value

(multivariate normal) loglikelihood implied by the Vecchia approximation

Examples

z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
vl_posterior = calculate_posterior_VL(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))
locs.pred=matrix(1:10+.5,ncol=1)
vecchia.approx.pred = vecchia_specify(locs, m=5, locs.pred=locs.pred )
vecchia_laplace_prediction(vl_posterior,vecchia.approx.pred,covparms=c(1,2,.5))
z=rnorm(10); locs=matrix(1:10,ncol=1); vecchia.approx=vecchia_specify(locs,m=5)
vl_posterior = calculate_posterior_VL(z,vecchia.approx,"gaussian",covparms=c(1,2,.5))
locs.pred=matrix(1:10+.5,ncol=1)
vecchia.approx.pred = vecchia_specify(locs, m=5, locs.pred=locs.pred )
vecchia_laplace_prediction(vl_posterior,vecchia.approx.pred,covparms=c(1,2,.5))

evaluation of the likelihood

Description

evaluation of the likelihood

Usage

vecchia_likelihood(z, vecchia.approx, covparms, nuggets, covmodel = "matern")
vecchia_likelihood(z, vecchia.approx, covparms, nuggets, covmodel = "matern")

Arguments

`z`	the observed data
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`covparms`	covariance parameters as a vector
`nuggets`	either a single (constant) nugget or a vector of nugget terms for the observations
`covmodel`	covariance model, 'matern' by default

Value

(multivariate normal) loglikelihood implied by the Vecchia approximation

Examples

z=rnorm(5); locs=matrix(1:5,ncol=1); vecchia.approx=vecchia_specify(locs,m=3)
vecchia_likelihood(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)
z=rnorm(5); locs=matrix(1:5,ncol=1); vecchia.approx=vecchia_specify(locs,m=3)
vecchia_likelihood(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)

linear combination of predictions compute the distribution of a linear combination Hy

Description

linear combination of predictions compute the distribution of a linear combination Hy

Usage

vecchia_lincomb(H, U.obj, V.ord, cov.mat = FALSE)
vecchia_lincomb(H, U.obj, V.ord, cov.mat = FALSE)

Arguments

`H`	sparse matrix with n.all columns specifying the linear combination
`U.obj`	U matrix is the full joint approximated cholesky matrix
`V.ord`	ordered V matrix from vecchia_prediction() or U2V()
`cov.mat`	logical TRUE or FALSE – should the entire covariance matrix be returned (only do if H has a small number of rows)

Value

Variance of linear combination of predictions.

Examples

n=5; z=rnorm(n); locs=matrix(1:n,ncol=1); n.p=5
vecchia.approx = vecchia_specify(locs,m=3,locs.pred=locs+.5)
preds=vecchia_prediction(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)
H=Matrix::sparseMatrix(i=rep(1,n.p),j=n+(1:n.p),x=1/n.p)
vecchia_lincomb(H,vecchia.approx,preds$V.ord,cov.mat=TRUE)
n=5; z=rnorm(n); locs=matrix(1:n,ncol=1); n.p=5
vecchia.approx = vecchia_specify(locs,m=3,locs.pred=locs+.5)
preds=vecchia_prediction(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)
H=Matrix::sparseMatrix(i=rep(1,n.p),j=n+(1:n.p),x=1/n.p)
vecchia_lincomb(H,vecchia.approx,preds$V.ord,cov.mat=TRUE)

make spatial predictions using Vecchia based on estimated parameters

Description

make spatial predictions using Vecchia based on estimated parameters

Usage

vecchia_pred(vecchia.est, locs.pred, X.pred, m = 30, ...)
vecchia_pred(vecchia.est, locs.pred, X.pred, m = 30, ...)

Arguments

`vecchia.est`	object returned by `vecchia_estimate`
`locs.pred`	n.p x d matrix of prediction locations
`X.pred`	n.p x p matrix of trend covariates at prediction locations. does not need to be specified if constant or no trend was used in `vecchia_estimate`
`m`	number of neighbors for vecchia approximation. default is 30.
`...`	additional input parameters for `vecchia_specify`

Value

object containing prediction means mean.pred and variances var.pred

Examples


n=10^2; locs=cbind(runif(n),runif(n))
covparms=c(1,.1,.5); nuggets=rep(.1,n)
Sigma=exp(-fields::rdist(locs)/covparms[2])+diag(nuggets)
z=as.numeric(t(chol(Sigma))%*%rnorm(n));
data=z+1
vecchia.est=vecchia_estimate(data,locs,theta.ini=c(covparms,nuggets[1]))
n.p=30^2; grid.oneside=seq(0,1,length=round(sqrt(n.p)))
locs.pred=as.matrix(expand.grid(grid.oneside,grid.oneside))
vecchia.pred=vecchia_pred(vecchia.est,locs.pred)

n=10^2; locs=cbind(runif(n),runif(n))
covparms=c(1,.1,.5); nuggets=rep(.1,n)
Sigma=exp(-fields::rdist(locs)/covparms[2])+diag(nuggets)
z=as.numeric(t(chol(Sigma))%*%rnorm(n));
data=z+1
vecchia.est=vecchia_estimate(data,locs,theta.ini=c(covparms,nuggets[1]))
n.p=30^2; grid.oneside=seq(0,1,length=round(sqrt(n.p)))
locs.pred=as.matrix(expand.grid(grid.oneside,grid.oneside))
vecchia.pred=vecchia_pred(vecchia.est,locs.pred)

Vecchia prediction

Description

Vecchia prediction

Usage

vecchia_prediction(
  z,
  vecchia.approx,
  covparms,
  nuggets,
  var.exact,
  covmodel = "matern",
  return.values = "all"
)
vecchia_prediction(
  z,
  vecchia.approx,
  covparms,
  nuggets,
  var.exact,
  covmodel = "matern",
  return.values = "all"
)

Arguments

`z`	observed data
`vecchia.approx`	a vecchia object as generated by vecchia_specify()
`covparms`	covariance parameters as a vector
`nuggets`	nugget
`var.exact`	should prediction variances be computed exactly, or is a (faster) approximation acceptable
`covmodel`	covariance model, 'matern' by default.
`return.values`	either 'mean' only, 'meanvar', 'meanmat', or 'all'

Value

posterior mean and variances at observed and unobserved locations; V matrix

Examples

z=rnorm(5); locs=matrix(1:5,ncol=1); vecchia.approx=vecchia_specify(locs,m=3,locs.pred=locs+.5)
vecchia_prediction(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)
z=rnorm(5); locs=matrix(1:5,ncol=1); vecchia.approx=vecchia_specify(locs,m=3,locs.pred=locs+.5)
vecchia_prediction(z,vecchia.approx,covparms=c(1,2,.5),nuggets=.2)

specify a general vecchia approximation

Description

specify the vecchia approximation for later use in likelihood evaluation or prediction. This function does not depend on parameter values, and only has to be run once before repeated likelihood evaluations.

Usage

vecchia_specify(
  locs,
  m = -1,
  ordering,
  cond.yz,
  locs.pred,
  ordering.pred,
  pred.cond,
  conditioning,
  mra.options = NULL,
  ic0 = FALSE,
  verbose = FALSE
)
vecchia_specify(
  locs,
  m = -1,
  ordering,
  cond.yz,
  locs.pred,
  ordering.pred,
  pred.cond,
  conditioning,
  mra.options = NULL,
  ic0 = FALSE,
  verbose = FALSE
)

Arguments

`locs`	nxd matrix of observed locs
`m`	Number of nearby points to condition on
`ordering`	options are 'coord' or 'maxmin'
`cond.yz`	options are 'y', 'z', 'SGV', 'SGVT', 'RVP', 'LK', and 'zy'
`locs.pred`	nxd matrix of locations at which to make predictions
`ordering.pred`	options are 'obspred' or 'general'
`pred.cond`	prediction conditioning, options are 'general' or 'independent'
`conditioning`	conditioning on 'NN' (nearest neighbor) or 'firstm' (fixed set for low rank) or 'mra'
`mra.options`	Settings for number of levels and neighbors per level
`ic0`	Specifies if ic0 decomposition should be used as opposed to regular Cholesky
`verbose`	Provide more detail when using MRA calculations. Default is false.

Value

An object that specifies the vecchia approximation for later use in likelihood evaluation or prediction.

Examples

locs=matrix(1:5,ncol=1); vecchia_specify(locs,m=2)
locs=matrix(1:5,ncol=1); vecchia_specify(locs,m=2)

Package 'GPvecchia'

Help Index

Vecchia Laplace extension of GPVecchia for non-Gaussian data

Description

Usage

Arguments

Value

Examples

create the sparse triangular L matrix for specific parameters

Description

Usage

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Value

Examples

create the sparse triangular U matrix for specific parameters

Description

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Value

Examples

extract the required elements from the covariance matrix

Description

Usage

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Value

Calculate the covariance values required by HV for matrix factors passed as sparse matrices

Description

Usage

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Value

GPvecchia: fast, scalable Gaussian process approximations

Description

Incomplete Cholesky decomposition of a sparse matrix passed in the compressed sparse row format

Description

Usage

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Value

Wrapper for incomplete Cholesky decomposition

Description

Usage

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Value

Examples

Calculate Matern covariance function

Description

Usage

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Value

Sorted coordinate ordering

Description

Usage

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Value

Examples

Distance to specified point ordering

Description

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Value

Examples

Maximum minimum distance ordering

Description

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Value

Examples

Maximum minimum distance ordering for prediction

Description

Usage

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Value

Examples

Middle-out ordering

Description

Usage

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Value

Examples

Outside-in ordering

Description