Package 'INLAspacetime'

Illustrative code to compute the covariance of the second order autoregression (AR2) model.

Description

Computes the auto-covariance for given coefficients.

Usage

ar2cov(a1, a2, k = 30, useC = FALSE)

Arguments

a1	the first auto-regression coefficient.
a2	the second auto-regression coefficient.
k	maximum lag for evaluating the auto-correlation.
useC	just a test (to use C code).

Value

the autocorrelation as a vector or matrix, whenever a1 or a2 are scalar or vector.

Details

Let the second order auto-regression model defined as ⁠x_t + a_1 x_{t-1} + a_2 x_{t-2} = w_t⁠ where w_t ~ N(0, 1).

See Also

ar2precision

Examples

ar2cov(c(-1.7, -1.8), 0.963, k = 5)
plot(ar2cov(-1.7, 0.963), type = "o")

---

Precision matrix for an AR2 model.

Description

Creates a precision matrix as a sparse matrix object considering the specification stated in Details.

Usage

ar2precision(n, a)

Arguments

n	the size of the model.
a	a length three vector with the coefficients. See details.

Value

the precision matrix as a sparse matrix object with edge correction.

Details

Let the second order auto-regression model be defined as

$a_0 x_t + a_1 x_{t-1} + a_2 x_{t-2} = w_t, w_t ~ N(0, 1).$

The n times n symmetric precision matrix Q for x_1, x_2, ..., x_n has the following non-zero elements:

$Q_{1,1} = Q_{n,n} = a_0^2$

$Q_{2,2} = Q_{n-1,n-1} = a_0^2 + a_1^2$

$Q_{1,2} = Q_{2,1} = Q_{n-1,n} = Q_{n,n-1} = a_0 a_1$

$Q_{t,t} = q_0 = a_0^2 + a_1^2 + a_2^2, t = 3, 4, ..., n-2$

$Q_{t,t-1} = Q_{t-1,t} = q_1 = a_1(a_0 + a_2), t = 3, 4, ..., n-1$

$Q_{t,t-2} = Q_{t-2,t} = q_2 = a_2 a_0, t = 3, 4, ..., n$

See Also

ar2cov

Examples

ar2precision(7, c(1, -1.5, 0.9))

---

Define a spacetime model object for the f() call.

Description

Define a spacetime model object for the f() call.

Usage

barrierModel.define(
  mesh,
  barrier.triangles,
  prior.range,
  prior.sigma,
  range.fraction = 0.1,
  constr = FALSE,
  debug = FALSE,
  verbose = FALSE,
  useINLAprecomp = TRUE,
  libpath = NULL
)

Arguments

mesh	a spatial mesh
barrier.triangles	a integer vector to specify which triangles centers are in the barrier domain
prior.range	numeric vector containing U and a to define the probability statements P(range < U) = a used to setup the PC-prior for range. If a = 0 then U is taken to be the fixed value for the range.
prior.sigma	numeric vector containing U and a to define the probability statements P(range > U) = a used to setup the PC-prior for sigma. If a = 0 then U is taken to be the fixed value for sigma.
range.fraction	numeric to specify the fraction of the range for the barrier domain. Default value is 0.1. This has to be specified with care in order to have it small enough to make it act as barrier but not too small in order to prevent numerical issues.
constr	logical to indicate if the integral of the field over the domain is to be constrained to zero. Default value is FALSE.
debug	logical indicating if to run in debug mode.
verbose	logical indicating if to print parameter values.
useINLAprecomp	logical indicating if is to be used shared object pre-compiled by INLA. This will not be considered if the argument libpath is provided.
libpath	string to the shared object. Default is NULL.

Details

See the paper.

Value

objects to be used in the f() formula term in INLA.

---

Mapper object for automatic inlabru interface

Description

Return an inlabru bru_mapper object that can be used for computing model matrices for the space-time model components. The bru_get_mapper() function is called by the inlabru methods to automatically obtain the needed mapper object (from inlabru ⁠2.7.0.9001⁠; before that, use mapper = bru_get_mapper(model) explicitly).

Usage

bru_get_mapper.stModel_cgeneric(model, ...)

Arguments

model	The model object (of class stModel_cgeneric, from stModel.define or barrierModel_cgeneric, from barrierModel.define)
...	Unused.

Value

A bru_mapper object of class bru_mapper_multi with sub-mappers space and time based on the model smesh and tmesh or mesh objects.

See Also

inlabru::bru_get_mapper()

---

Computes the Whittle-Matern correlation function.

Description

This computes the correlation function as derived in Matern model, see Matern (1960) eq. (2.4.7). For nu=1, see Whittle (1954) eq. (68). For the limiting case of nu=0, see Besag (1981) eq. (14-15).

Usage

cWhittleMatern(x, range, nu, kappa = sqrt(8 * nu)/range)

Arguments

x	distance.
range	practical range (our prefered parametrization) given as range = sqrt(8 * nu) / kappa, where kappa is the scale parameter in the specialized references.
nu	process smoothness parameter.
kappa	scale parameter, commonly considered in the specialized literature.

Value

the correlation.

Details

Whittle, P. (1954) On Stationary Processes in the Plane. Biometrika, Vol. 41, No. 3/4, pp. 434-449. http://www.jstor.org/stable/2332724

Matern, B. (1960) Spatial Variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations. PhD Thesis.

Besag, J. (1981) On a System of Two-Dimensional Recurrence Equations. JRSS-B, Vol. 43 No. 3, pp. 302-309. https://www.jstor.org/stable/2984940

Examples

plot(function(x) cWhittleMatern(x, 1, 5),
  bty = "n", las = 1,
  xlab = "Distance", ylab = "Correlation"
)
plot(function(x) cWhittleMatern(x, 1, 1), add = TRUE, lty = 2)
plot(function(x) cWhittleMatern(x, 1, 0.5), add = TRUE, lty = 3)
abline(h = 0.139, lty = 3, col = gray(0.5, 0.5))

---

Download files from the NOAA's GHCN daily data

Description

Download files from the NOAA's GHCN daily data

Usage

downloadUtilFiles(data.dir, year = 2022, force = FALSE)

Arguments

data.dir	the folder to store the files.
year	the year of the daily weather data.
force	logical indicating if it is to force the download. If FALSE each file will be downloaded if it does not exists locally yet.

Value

a named character vector with the local file names: daily.data, stations.all, elevation.

---

Function to define the boundary Earch polygon in longlat projection for a given resolution.

Description

Function to define the boundary Earch polygon in longlat projection for a given resolution.

Usage

Earth_poly(resol = 300, crs = "+proj=moll +units=km")

Arguments

resol	is the number of subdivisions along the latitude coordinates and half the number of subdivisions along the longitude coordinates.
crs	a string with the projection. Default is the Mollweide projection with units in kilometers.

Value

a 'st_sfc' object with the Earth polygon.

---

Select data from the daily dataset

Description

Select data from the daily dataset

Usage

ghcndSelect(
  gzfile,
  variable = c("TMIN", "TAVG", "TMAX"),
  station = NULL,
  qflag = "",
  verbose = TRUE,
  astype = as.integer
)

Arguments

gzfile	the local filename for the daily data file file. E.g. 2023.csv.gz from https://www.ncei.noaa.gov/pub/data/ghcn/daily/by_year/ see references bellow.
variable	string with the variable name(s) to be selected
station	string (vector) with the station(s) to be selected
qflag	a string with quality control flag(s)
verbose	logical indicating if progress is to be printed
astype	function to convert data to a class, default is set to convert the data to integer.

Value

if more than one variable, it returns an array whose dimentions are days, stations, variables. If one variable, then it returns a matrix whose dimentions are days, stations.

Details

The default selects TMIN, TAVG and TMAX and return it as integer because the original data is also integer with units in 10 Celcius degrees.

Warning

It can take time to execute if, for example, the data.table package is not available.

References

Menne, M., Durre, I., Vose, R., Gleason, B. and Houston, T. (2012) An overview of the global historical climatology network-daily database. Journal of Atmospheric and Oceanic Technology, 897–910.

---

Internal util functions for polygon properties.

Description

This computes the area of a triangle given its three coordinates.

Usage

Heron(x, y)

Area(x, y)

s2trArea(tr, R = 1)

flatArea(tr)

Stiffness(tr)

Arguments

x, y	coordinate vectors.
tr	the triangle coordinates
R	the radius of the spherical domain

Details

Function used internally to compute the area of a triangle.

Value

the area of a 2d triangle

the area of a 2d polygon

the area of a triangle in S2

the area of a triangle

the stiffness matrix for a triangle

Warning

Internal functions, not exported.

---

Spatial and Spatio-Temporal Models using INLA

Description

This package main purpose is to provide user friendly functions to fit temporal, spatial and space-time models using the INLA software available at www.r-inla.org as well the inlabru package available

Usage

INLAspacetime()

Value

opens the Vignettes directory on a browser

---

The 2nd order temporal matrices with boundary correction

Description

The 2nd order temporal matrices with boundary correction

Usage

Jmatrices(tmesh)

Arguments

tmesh

Temporal mesh

Details

Temporal GMRF representation with stationary boundary conditions as in Appendix E of the paper.

Value

return a list of temporal finite element method matrices for the supplied mesh.

---

Extracts the dual of a mesh object.

Description

Extracts the dual of a mesh object.

Usage

mesh.dual(
  mesh,
  returnclass = c("list", "sf", "sv", "SP"),
  mc.cores = getOption("mc.cores", 2L)
)

Arguments

mesh	a 2d mesh object.
returnclass	if 'list' return a list of polygon coordinates, if "sf" return a 'sf' sfc_multipolygon object, if "sv" return a 'terra', SpatVector object, if "SP" return a 'sp' SpatialPolygons object.
mc.cores	number of threads to be used.

Value

one of the three in 'returnclass'

---

Illustrative code for building a mesh in 2d domain.

Description

Creates a mesh object. This is just a test code. For efficient, reliable and general code use the fmesher package.

Usage

mesh2d(loc, domain, max.edge, offset, SP = TRUE)

Arguments

loc	a two column matrix with location coordinates.
domain	a two column matrix defining the domain.
max.edge	the maximum edge length.
offset	the length of the outer extension.
SP	logical indicating if the output will include the SpatialPolygons.

Value

a mesh object.

Warning

This is just for illustration purpose and one should consider the efficient function available a the INLA package.

---

Illustrative code for Finite Element matrices of a mesh in 2d domain.

Description

Illustrative code for Finite Element matrices of a mesh in 2d domain.

Illustrative code for Finite Element matrices when some triangles are in a barrier domain.

Usage

mesh2fem(mesh, order = 2, barrier.triangles = NULL)

mesh2fem.barrier(mesh, barrier.triangles = NULL)

Arguments

mesh	a 2d mesh object.
order	the desired order.
barrier.triangles	integer index to specify the triangles in the barrier domain

Value

a list object containing the FE matrices.

a list object containing the FE matrices for the barrier problem.

---

Illustrative code to build the projector matrix for SPDE models.

Description

Creates a projector matrix object.

Usage

mesh2projector(
  mesh,
  loc = NULL,
  lattice = NULL,
  xlim = NULL,
  ylim = NULL,
  dims = c(100, 100)
)

Arguments

mesh	a 2d mesh object.
loc	a two columns matrix with the locations to project for.
lattice	Unused; feature not supported by this illustration.
xlim, ylim	vector with the boundary limits.
dims	the number of subdivisions over each boundary limits.

Value

the projector matrix as a list with sparse matrix object at x$proj$A..

Warning

This is just for illustration purpose and one should consider the efficient functions available in the INLA and inlabru packages, e.g. inlabru::fm_evaluator.

---

Detect outliers in a time series considering the raw data and a smoothed version of it.

Description

Detect outliers in a time series considering the raw data and a smoothed version of it.

Usage

outDetect(x, weights = NULL, ff = c(7, 7))

Arguments

x	numeric vector
weights	non-increasing numeric vector used as weights for computing a smoothed vector as a rooling window average. Default is null and then $w_j$ is proportional to j in the equation in the Details below.
ff	numeric length two vector with the factors used to consider how many times the standard deviation one data point is out to be considered as an outlier.

Value

logical vector indicating if the data is an outlier with attributes as detailed bellow.

• attr(, 'm') is the mean of x.

• attr(, 's') is the standard devation of x.

• attr(, 'ss') is the standard deviation for the smoothed data $y_t$ that is defined as

$y_t = \sum_{k=j}^h w_j * (x_{t-j}+x_{t+j})/2$

Both s and ss are used to define outliers if

$|x_t-m|/s>ff_1$ or $|x_t-y_t|/ss>ff_2$

• attr(, 'xs') the smoothed time series $y_t$

---

Functions to help converting from/to user/internal parametrization. The internal parameters are 'gamma_s, 'gamma_t', 'gamma_E' The user parameters are 'r_s', 'r_t', 'sigma'

Description

Functions to help converting from/to user/internal parametrization. The internal parameters are 'gamma_s, 'gamma_t', 'gamma_E' The user parameters are 'r_s', 'r_t', 'sigma'

Convert from user parameters to SPDE parameters

Convert from SPDE parameters to user parameters

Usage

lgsConstant(lg.s, alpha, smanifold)

params2gammas(
  lparams,
  alpha.t,
  alpha.s,
  alpha.e,
  smanifold = "R2",
  verbose = FALSE
)

gammas2params(lgammas, alpha.t, alpha.s, alpha.e, smanifold = "R2")

Arguments

lg.s	the logarithm of the SPDE parameter ⁠\gamma_s⁠
alpha	the resulting spatial order.
smanifold	spatial domain manifold, which could be "S1", "S2", "R1", "R2" and "R3".
lparams	log(spatial range, temporal range, sigma)
alpha.t	temporal order of the SPDE
alpha.s	spatial order of the spatial differential operator in the non-separable part.
alpha.e	spatial order of the spatial differential operator in the separable part.
verbose	logical if it is to print internal variables
lgammas	numeric of length 3 with $log(\gamma_k)$ model parameters. The parameter order is log(gamma.s, gamma.t, gamma.e)

Details

See equation (23) in the paper.

See equations (19), (20) and (21) in the paper.

See equations (19), (20) and (21) in the paper.

Value

the part of sigma from the spatial constant and ⁠\gamma_s⁠.

log(gamma.s, gamma.t, gamma.e)

log(spatial range, temporal range, sigma)

Examples

params2gammas(log(c(1, 1, 1)), 1, 2, 1, "R2")
gammas2params(log(c(0, 0, 0)), 1, 2, 1, "R2")

---

Illustrative code to build the precision matrix for SPDE kind models.

Description

Creates a precision matrix as a sparse matrix object. For general code look at the functions in the INLA package.

Usage

spde2precision(kappa, fem, alpha)

Arguments

kappa	the scale parameter.
fem	a list containing the Finite Element matrices.
alpha	the smoothness parameter.

Value

the precision matrix as a sparse matrix object.

Warning

This is just for illustration purpose and one should consider the efficient function available a the INLA package.

---

To retrieve goodness of fit statistics for a specific model class.

Description

Extracts dic, waic and log-cpo from an output returned by the inla function from the INLA package or by the bru function from the inlabru package, and computes log-po, mse, mae, crps and scrps for a given input. A summary is applied considering the user imputed function, which by default is the mean.

Usage

stats.inla(m, i = NULL, y, fsummarize = mean)

Arguments

m	an inla output object.
i	an index to subset the estimated values.
y	observed to compare against.
fsummarize	the summary function, the default is base::mean().

Value

A named numeric vector with the extracted statistics.

Details

It assumes Gaussian posterior predictive distributions! Considering the defaults, for n observations, $y_i, i = 1, 2, ..., n$, we have

. dic

$\sum_i d_i/n$

where $d_i$ is the dic computed for observation i.

. waic

$\sum_i w_i/n$

where $w_i$ is the waic computed for observation i.

. lcpo

$-\sum_i \log(p_i)/n$

where $p_i$ is the cpo computed for observation i.

For the log-po, crps, and scrps scores it assumes a Gaussian predictive distribution for each observation $y_i$ which the following definitions: $z_i = (y_i-\mu_i)/\sigma_i$, $\mu_i$ is the posterior mean for the linear predictor, $\sigma_i = \sqrt{v_i + 1/\tau_y}$, $\tau_y$ is the observation posterior mean, $v_i$ is the posterior variance of the linear predictor for $y_i$.

Then we consider $\phi()$ the density of a standard Gaussian variable and $\psi()$ the corresponding Cumulative Probability Distribution.

. lpo

$-\sum_i \log(\phi(z_i))/n$

. crps

$\sum_i r_i/n$

where

$r_i=\sigma_i/\sqrt{\pi} - 2\sigma_i\phi(z_i) + (y_i-\mu_i)(1-2\psi(z_i))$

. scrps

$\sum_i s_i/n$

where

$s_i=-\log(2\sigma_i/\sqrt{\pi})/2 -\sqrt{\pi}(\phi(z_i)-\sigma_iz_i/2+z_i\psi(z_i))$

Warning

All the scores are negatively oriented which means that smaller scores are better.

References

Held, L. and Schrödle, B. and Rue, H. (2009). Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA. Statistical Modelling and Regression Structures pp 91–110. https://link.springer.com/chapter/10.1007/978-3-7908-2413-1_6.

Bolin, D. and Wallin, J. (2022) Local scale invariance and robustness of proper scoring rules. Statistical Science. doi:10.1214/22-STS864.

---

To check unusual low/high variance segments

Description

To check unusual low/high variance segments

Usage

stdSubs(x, nsub = 12, fs = 15)

Arguments

x	numeric vector
nsub	number for the segments length
fs	numeric to use for detecting too hight or too low local standard deviations.

Value

logical indicating if any of the st are fs times lower/higher the average of st, where is returned as an attribute:

• attr(, 'st') numeric vector with the standard deviation at each segment of the data.

---

To visualize time series over space.

Description

To visualize time series over space.

Usage

stlines(
  stdata,
  spatial,
  group = NULL,
  nmax.group = NULL,
  xscale = 1,
  yscale = 1,
  colour = NULL,
  ...
)

stpoints(
  stdata,
  spatial,
  group = NULL,
  nmax.group = NULL,
  xscale = 1,
  yscale = 1,
  colour = NULL,
  ...
)

Arguments

stdata	matrix with the data, each column is a location.
spatial	an object with one of class defined in the sp package.
group	an integer vector indicating to which spatial unit each time series belongs to. Default is NULL and them it is assumed that each time series belongs o each spatial unit.
nmax.group	an integer indicating the maximum number of time series to be plotted over each spatial unit. Default is NULL, so all will be drawn.
xscale	numeric to define a scaling factor in the horizontal direction.
yscale	numeric to define a scaling factor in the vertical direction.
colour	color (may be a vector, one for each time series). Default is NULL and it will generate colors considering the average of each time series. These automatic colors are defined using the rgb() function with alpha=0.5. It considers the relative rank of each time series mean, r. r is then used for red, 1-r is used for blue and a triangular function, ⁠1-2*|1-r/2|⁠, is considered for green. That is, time series with mean among the lowest time series averages are shown in blue and those among the highest temperatures are shown in red. The transition from blue to red goes so that the intermediate ones are shown in light green.
...	further arguments to be passed for the lines function.

Details

Scaling the times series is needed before drawing it over the map. The area of the bounding box for the spatial object divided by the number of locations is the standard scaling factor. This is further multiplied by the user given xcale and yscale.

Value

add lines to an existing plot

Functions

• stlines(): each time series over the map centered at the location.

• stpoints(): each time series over the map centered at the location.

Warning

if there are too many geographical locations, it will not look good

---

Define a spacetime model object for the f() call.

Description

Define a spacetime model object for the f() call.

Usage

stModel.define(
  smesh,
  tmesh,
  model,
  control.priors,
  constr = FALSE,
  debug = FALSE,
  verbose = FALSE,
  useINLAprecomp = TRUE,
  libpath = NULL
)

Arguments

smesh	a spatial mesh
tmesh	a temporal mesh
model	a three characters string to specify the smoothness alpha (each one as integer) parameters. Currently it considers the 102, 121, 202 and 220 models.
control.priors	a named list with parameter priors. E.g. prior.rs, prior.rt and prior.sigma as vectors with length two (U, a) to define the corresponding PC-prior such that P(r_s<U)=a, P(r_t<U)=a or P(sigma>U)=a. If a=0 then U is taken to be the fixed value of the parameter.
constr	logical to indicate if the integral of the field over the domain is to be constrained to zero. Default value is FALSE.
debug	logical indicating if to run in debug mode.
verbose	logical indicating if to print parameter values.
useINLAprecomp	logical indicating if is to be used shared object pre-compiled by INLA. Not considered if libpath is provided.
libpath	string to the shared object. Default is NULL.

Details

See the paper.

Value

objects to be used in the f() formula term in INLA.

---

Define the spacetime model matrices.

Description

This function computes all the matrices needed to build the precision matrix for spatio-temporal model, as in Lindgren et. al. (2023)

Usage

stModel.matrices(smesh, tmesh, model, constr = FALSE)

Arguments

smesh	a mesh object over the spatial domain.
tmesh	a mesh object over the time domain.
model	a string identifying the model. So far we have the following models: '102', '121', '202' and '220' models.
constr	logical to indicate if the integral of the field over the domain is to be constrained to zero. Default value is FALSE.

Details

See the paper for details.

Value

a list containing needed objects for model definition.

1. 'manifold' to spedify the a string with the model identification

2. a length three vector with the constants c1, c2 and c3

3. the vector d

4. the matrix T

5. the model matrices M_1, ..., M_m

---

Spacetime precision matrix.

Description

To build the the precision matrix for a spacetime model given the temporal and the spatial meshes.

Usage

stModel.precision(smesh, tmesh, model, theta, verbose = FALSE)

Arguments

smesh	a mesh object over the spatial domain.
tmesh	a mesh object over the time domain.
model	a string identifying the model. So far we have the following models: '102', '121', '202' and '220' models.
theta	numeric vector of length three with $log(gamma_s, gamma_t, gamma_e)$.
verbose	logical to print intermediate objects.

Value

a (sparse) precision matrix, as in Lindgren et. al. (2023)

---

Prepare a matrix or a list of matrices for use in some 'cgeneric' code.

Description

Define a graph of the union of the supplied matrices and return the row ordered diagonal plus upper triangle after padding with zeroes each one so that all the returned matrices have the same pattern.

Usage

upperPadding(M, relative = FALSE)

Arguments

M	a matrix or a list of matrices
relative	logical. If a list of matrices is supplied, it indicates if it is to be returned a relative index and the value for each matrix. See details.

Details

If relative=FALSE, each columns of 'xx' is the elements of the corresponding matrix after being padded to fill the pattern of the union graph. If relative=TRUE, each element of 'xx' would be a list with a relative index, 'r', for each non-zero elements of each matrix is returned relative to the union graph, the non-lower elements, 'x', of the corresponding matrix, and a vector, 'o', with the number of non-zero elements for each line of each resulting matrix.

Value

If a unique matrix is given, return the upper triangle considering the 'T' representation in the Matrix package. If a list of matrices is given, return a list of two elements: 'graph' and 'xx'. The 'graph' is the union of the graph from each matrix. If relative=FALSE, 'xx' is a matrix with number of column equals the the number of matrices imputed. If relative=TRUE, it is a list of length equal the number of matrices imputed. See details.

Examples

A <- sparseMatrix(
  i = c(1, 1, 2, 3, 3, 5),
  j = c(2, 5, 3, 4, 5, 5),
  x = -(0:5), symmetric = TRUE
)
A
upperPadding(A)
B <- Diagonal(nrow(A), -colSums(A))
list(a = A, a = B)
upperPadding(list(a = A, b = B))
upperPadding(list(a = A, b = B), relative = TRUE)

---

Define a regular grid in 'Mollweide' projection, with units in kilometers.

Description

Define a regular grid in 'Mollweide' projection, with units in kilometers.

Usage

world_grid(size = 50, domain)

Arguments

size	the (in kilometers) of the grid cells.
domain	if provided it should be an sf or sfc object. In this case, the grid cells with centers falling inside will be retrieved.

Value

a 'sf' points object with the centers of a grid set within Earth (and the supplied domain)

---

Helper functions to retrieve the world map, a world polygon, and create grid centers.

Description

Retrieve the map of the countries

Usage

worldMap(
  crs = "+proj=moll +units=km",
  scale = "medium",
  returnclass = c("sf", "sv")
)

Arguments

crs	a string with the projection. Default is the Mollweide projection with units in kilometers.
scale	The scale of map to return. Please see the help of 'ne_countries' function from the 'rnaturalearth' package.
returnclass	A string determining the class of the spatial object to return. Please see the help of 'ne_countries' function from the 'rnaturalearth' package.

References

The land and ocean maps are obtained with the 'rnaturalearth' package.

---