Package 'spatial'

Title: Functions for Kriging and Point Pattern Analysis
Description: Functions for kriging and point pattern analysis.
Authors: Brian Ripley [aut, cre, cph], Roger Bivand [ctb], William Venables [cph]
Maintainer: Brian Ripley <[email protected]>
License: GPL-2 | GPL-3
Version: 7.3-17
Built: 2024-03-27 22:42:16 UTC
Source: CRAN

Help Index

Average K-functions from Simulations

Description

Forms the average of a series of (usually simulated) K-functions.

Usage

Kaver(fs, nsim, ...)

Arguments

fs

full scale for K-fn

nsim

number of simulations

...

arguments to simulate one point process object

Value

list with components x and y of the average K-fn on L-scale.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Kfn, Kenvl

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 40), type="b")
plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
for(i in 1:10) lines(Kfn(Psim(69), 10))
lims <- Kenvl(10,100,Psim(69))
lines(lims$x,lims$lower, lty=2, col="green")
lines(lims$x,lims$upper, lty=2, col="green")
lines(Kaver(10,25,Strauss(69,0.5,3.5)),  col="red")

Compute Envelope and Average of Simulations of K-fns

Description

Computes envelope (upper and lower limits) and average of simulations of K-fns

Usage

Kenvl(fs, nsim, ...)

Arguments

fs

full scale for K-fn

nsim

number of simulations

...

arguments to produce one simulation

Value

list with components

x

distances

lower

min of K-fns

upper

max of K-fns

aver

average of K-fns

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Kfn, Kaver

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 40), type="b")
plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
for(i in 1:10) lines(Kfn(Psim(69), 10))
lims <- Kenvl(10,100,Psim(69))
lines(lims$x,lims$lower, lty=2, col="green")
lines(lims$x,lims$upper, lty=2, col="green")
lines(Kaver(10,25,Strauss(69,0.5,3.5)), col="red")

Compute K-fn of a Point Pattern

Description

Actually computes L=K/πL = \sqrt{K/\pi}.

Usage

Kfn(pp, fs, k=100)

Arguments

pp

a list such as a pp object, including components x and y

fs

full scale of the plot

k

number of regularly spaced distances in (0, fs)

Details

relies on the domain D having been set by ppinit or ppregion.

Value

A list with components

x

vector of distances

y

vector of L-fn values

k

number of distances returned – may be less than k if fs is too large

dmin

minimum distance between pair of points

lm

maximum deviation from L(t) = t

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

ppinit, ppregion, Kaver, Kenvl

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")

Simulate Binomial Spatial Point Process

Description

Simulate Binomial spatial point process.

Usage

Psim(n)

Arguments

n

number of points

Details

relies on the region being set by ppinit or ppregion.

Value

list of vectors of x and y coordinates.

Side Effects

uses the random number generator.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

SSI, Strauss

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")
for(i in 1:10) lines(Kfn(Psim(69), 10))

Simulates Sequential Spatial Inhibition Point Process

Description

Simulates SSI (sequential spatial inhibition) point process.

Usage

SSI(n, r)

Arguments

n

number of points

r

inhibition distance

Details

uses the region set by ppinit or ppregion.

Value

list of vectors of x and y coordinates

Side Effects

uses the random number generator.

Warnings

will never return if r is too large and it cannot place n points.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Psim, Strauss

Examples

towns <- ppinit("towns.dat")
par(pty = "s")
plot(Kfn(towns, 10), type = "b", xlab = "distance", ylab = "L(t)")
lines(Kaver(10, 25, SSI(69, 1.2)))

Simulates Strauss Spatial Point Process

Description

Simulates Strauss spatial point process.

Usage

Strauss(n, c=0, r)

Arguments

n

number of points

c

parameter c in [0,1][0, 1]. c = 0 corresponds to complete inhibition at distances up to r.

r

inhibition distance

Details

Uses spatial birth-and-death process for 4n steps, or for 40n steps starting from a binomial pattern on the first call from an other function. Uses the region set by ppinit or ppregion.

Value

list of vectors of xx and yy coordinates

Side Effects

uses the random number generator

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Psim, SSI

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
lines(Kaver(10, 25, Strauss(69,0.5,3.5)))

Anova tables for fitted trend surface objects

Description

Compute analysis of variance tables for one or more fitted trend surface model objects; where anova.trls is called with multiple objects, it passes on the arguments to anovalist.trls.

Usage

## S3 method for class 'trls'
anova(object, ...)
anovalist.trls(object, ...)

Arguments

object

A fitted trend surface model object from surf.ls

...

Further objects of the same kind

Value

anova.trls and anovalist.trls return objects corresponding to their printed tabular output.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.ls

Examples

library(stats)
data(topo, package="MASS")
topo0 <- surf.ls(0, topo)
topo1 <- surf.ls(1, topo)
topo2 <- surf.ls(2, topo)
topo3 <- surf.ls(3, topo)
topo4 <- surf.ls(4, topo)
anova(topo0, topo1, topo2, topo3, topo4)
summary(topo4)

Compute Spatial Correlograms

Description

Compute spatial correlograms of spatial data or residuals.

Usage

correlogram(krig, nint, plotit = TRUE,  ...)

Arguments

krig

trend-surface or kriging object with columns x, y, and z

nint

number of bins used

plotit

logical for plotting

...

parameters for the plot

Details

Divides range of data into nint bins, and computes the covariance for pairs with separation in each bin, then divides by the variance. Returns results for bins with 6 or more pairs.

Value

x and y coordinates of the correlogram, and cnt, the number of pairs averaged per bin.

Side Effects

Plots the correlogram if plotit = TRUE.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

variogram

Examples

data(topo, package="MASS")
topo.kr <- surf.ls(2, topo)
correlogram(topo.kr, 25)
d <- seq(0, 7, 0.1)
lines(d, expcov(d, 0.7))

Spatial Covariance Functions

Description

Spatial covariance functions for use with surf.gls.

Usage

expcov(r, d, alpha = 0, se = 1)
gaucov(r, d, alpha = 0, se = 1)
sphercov(r, d, alpha = 0, se = 1, D = 2)

Arguments

r

vector of distances at which to evaluate the covariance

d

range parameter

alpha

proportion of nugget effect

se

standard deviation at distance zero

D

dimension of spheres.

Value

vector of covariance values.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.gls

Examples

data(topo, package="MASS")
topo.kr <- surf.ls(2, topo)
correlogram(topo.kr, 25)
d <- seq(0, 7, 0.1)
lines(d, expcov(d, 0.7))

Get Domain for Spatial Point Pattern Analyses

Description

Retrieves the rectangular domain (xl, xu) ×\times (yl, yu) from the underlying C code.

Usage

ppgetregion()

Value

A vector of length four with names c("xl", "xu", "yl", "yu").

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

ppregion

Read a Point Process Object from a File

Description

Read a file in standard format and create a point process object.

Usage

ppinit(file)

Arguments

file

string giving file name

Details

The file should contain

the number of points
a header (ignored)
xl xu yl yu scale
x y (repeated n times)

Value

class "pp" object with components x, y, xl, xu, yl, yu

Side Effects

Calls ppregion to set the domain.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

ppregion

Examples

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")

Pseudo-likelihood Estimation of a Strauss Spatial Point Process

Description

Pseudo-likelihood estimation of a Strauss spatial point process.

Usage

pplik(pp, R, ng=50, trace=FALSE)

Arguments

pp

a pp object

R

the fixed parameter R

ng

use a ng x ng grid with border R in the domain for numerical integration.

trace

logical? Should function evaluations be printed?

Value

estimate for c in the interval [0,1][0, 1].

References

Ripley, B. D. (1988) Statistical Inference for Spatial Processes. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Strauss

Examples

pines <- ppinit("pines.dat")
pplik(pines, 0.7)

Set Domain for Spatial Point Pattern Analyses

Description

Sets the rectangular domain (xl, xu) ×\times (yl, yu).

Usage

ppregion(xl = 0, xu = 1, yl = 0, yu = 1)

Arguments

xl

Either xl or a list containing components xl, xu, yl, yu (such as a point-process object)

xu, yl, yu

otheri limits of the rectangle if given separately.

Value

none

Side Effects

initializes variables in the C subroutines.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

ppinit, ppgetregion

Predict method for trend surface fits

Description

Predicted values based on trend surface model object

Usage

## S3 method for class 'trls'
predict(object, x, y, ...)

Arguments

object

Fitted trend surface model object returned by surf.ls

x

Vector of prediction location eastings (x coordinates)

y

Vector of prediction location northings (y coordinates)

...

further arguments passed to or from other methods.

Value

predict.trls produces a vector of predictions corresponding to the prediction locations. To display the output with image or contour, use trmat or convert the returned vector to matrix form.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.ls, trmat

Examples

data(topo, package="MASS")
topo2 <- surf.ls(2, topo)
topo4 <- surf.ls(4, topo)
x <- c(1.78, 2.21)
y <- c(6.15, 6.15)
z2 <- predict(topo2, x, y)
z4 <- predict(topo4, x, y)
cat("2nd order predictions:", z2, "\n4th order predictions:", z4, "\n")

Evaluate Kriging Surface over a Grid

Description

Evaluate Kriging surface over a grid.

Usage

prmat(obj, xl, xu, yl, yu, n)

Arguments

obj

object returned by surf.gls

xl

limits of the rectangle for grid

xu

ditto

yl

ditto

yu

ditto

n

use n x n grid within the rectangle

Value

list with components x, y and z suitable for contour and image.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.gls, trmat, semat

Examples

data(topo, package="MASS")
topo.kr <- surf.gls(2, expcov, topo, d=0.7)
prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
contour(prsurf, levels=seq(700, 925, 25))

Evaluate Kriging Standard Error of Prediction over a Grid

Description

Evaluate Kriging standard error of prediction over a grid.

Usage

semat(obj, xl, xu, yl, yu, n, se)

Arguments

obj

object returned by surf.gls

xl

limits of the rectangle for grid

xu

ditto

yl

ditto

yu

ditto

n

use n x n grid within the rectangle

se

standard error at distance zero as a multiple of the supplied covariance. Otherwise estimated, and it assumed that a correlation function was supplied.

Value

list with components x, y and z suitable for contour and image.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.gls, trmat, prmat

Examples

data(topo, package="MASS")
topo.kr <- surf.gls(2, expcov, topo, d=0.7)
prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
contour(prsurf, levels=seq(700, 925, 25))
sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
contour(sesurf, levels=c(22,25))

Fits a Trend Surface by Generalized Least-squares

Description

Fits a trend surface by generalized least-squares.

Usage

surf.gls(np, covmod, x, y, z, nx = 1000, ...)

Arguments

np

degree of polynomial surface

covmod

function to evaluate covariance or correlation function

x

x coordinates or a data frame with columns x, y, z

y

y coordinates

z

z coordinates. Will supersede x$z

nx

Number of bins for table of the covariance. Increasing adds accuracy, and increases size of the object.

...

parameters for covmod

Value

list with components

beta

the coefficients

x
y
z

and others for internal use only.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

trmat, surf.ls, prmat, semat, expcov, gaucov, sphercov

Examples

library(MASS)  # for eqscplot
data(topo, package="MASS")
topo.kr <- surf.gls(2, expcov, topo, d=0.7)
trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE)

prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
contour(prsurf, levels=seq(700, 925, 25))
sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
eqscplot(sesurf, type = "n")
contour(sesurf, levels = c(22, 25), add = TRUE)

Fits a Trend Surface by Least-squares

Description

Fits a trend surface by least-squares.

Usage

surf.ls(np, x, y, z)

Arguments

np

degree of polynomial surface

x

x coordinates or a data frame with columns x, y, z

y

y coordinates

z

z coordinates. Will supersede x$z

Value

list with components

beta

the coefficients

x
y
z

and others for internal use only.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

trmat, surf.gls

Examples

library(MASS)  # for eqscplot
data(topo, package="MASS")
topo.kr <- surf.ls(2, topo)
trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE)
points(topo)

eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE)
plot(topo.kr, add = TRUE)
title(xlab= "Circle radius proportional to Cook's influence statistic")

Regression diagnostics for trend surfaces

Description

This function provides the basic quantities which are used in forming a variety of diagnostics for checking the quality of regression fits for trend surfaces calculated by surf.ls.

Usage

trls.influence(object)
## S3 method for class 'trls'
plot(x, border = "red", col = NA, pch = 4, cex = 0.6,
     add = FALSE, div = 8, ...)

Arguments

object, x

Fitted trend surface model from surf.ls

div

scaling factor for influence circle radii in plot.trls

add

add influence plot to existing graphics if TRUE

border, col, pch, cex, ...

additional graphical parameters

Value

trls.influence returns a list with components:

r

raw residuals as given by residuals.trls

hii

diagonal elements of the Hat matrix

stresid

standardised residuals

Di

Cook's statistic

References

Unwin, D. J., Wrigley, N. (1987) Towards a general-theory of control point distribution effects in trend surface models. Computers and Geosciences, 13, 351–355.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.ls, influence.measures, plot.lm

Examples

library(MASS)  # for eqscplot
data(topo, package = "MASS")
topo2 <- surf.ls(2, topo)
infl.topo2 <- trls.influence(topo2)
(cand <- as.data.frame(infl.topo2)[abs(infl.topo2$stresid) > 1.5, ])
cand.xy <- topo[as.integer(rownames(cand)), c("x", "y")]
trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE, col = "grey")
plot(topo2, add = TRUE, div = 3)
points(cand.xy, pch = 16, col = "orange")
text(cand.xy, labels = rownames(cand.xy), pos = 4, offset = 0.5)

Evaluate Trend Surface over a Grid

Description

Evaluate trend surface over a grid.

Usage

trmat(obj, xl, xu, yl, yu, n)

Arguments

obj

object returned by surf.ls or surf.gls

xl

limits of the rectangle for grid

xu

ditto

yl

ditto

yu

ditto

n

use n x n grid within the rectangle

Value

list with components x, y and z suitable for contour and image.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

surf.ls, surf.gls

Examples

data(topo, package="MASS")
topo.kr <- surf.ls(2, topo)
trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)

Compute Spatial Variogram

Description

Compute spatial (semi-)variogram of spatial data or residuals.

Usage

variogram(krig, nint, plotit = TRUE, ...)

Arguments

krig

trend-surface or kriging object with columns x, y, and z

nint

number of bins used

plotit

logical for plotting

...

parameters for the plot

Details

Divides range of data into nint bins, and computes the average squared difference for pairs with separation in each bin. Returns results for bins with 6 or more pairs.

Value

x and y coordinates of the variogram and cnt, the number of pairs averaged per bin.

Side Effects

Plots the variogram if plotit = TRUE

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

correlogram

Examples

data(topo, package="MASS")
topo.kr <- surf.ls(2, topo)
variogram(topo.kr, 25)