spatstat/ 0000755 0001760 0000144 00000000000 12252364235 012151 5 ustar ripley users spatstat/inst/ 0000755 0001760 0000144 00000000000 12237642736 013136 5 ustar ripley users spatstat/inst/CITATION 0000755 0001760 0000144 00000003114 12237642736 014275 0 ustar ripley users citHeader("To cite spatstat in publications use:")
citEntry(entry = "Article",
title = "{spatstat}: An {R} Package for Analyzing Spatial Point Patterns",
author = personList(as.person("Adrian Baddeley"),
as.person("Rolf Turner")),
journal = "Journal of Statistical Software",
year = "2005",
volume = "12",
number = "6",
pages = "1--42",
url = "http://www.jstatsoft.org/v12/i06/",
textVersion =
paste("Adrian Baddeley, Rolf Turner (2005).",
"spatstat: An R Package for Analyzing Spatial Point Patterns.",
"Journal of Statistical Software 12(6), 1-42.",
"URL http://www.jstatsoft.org/v12/i06/.")
)
citEntry(entry = "Article",
title = "Hybrids of Gibbs Point Process Models and Their Implementation",
author = personList(as.person("Adrian Baddeley"),
as.person("Rolf Turner"),
as.person("Jorge Mateu"),
as.person("Andrew Bevan")),
journal = "Journal of Statistical Software",
year = "2013",
volume = "55",
number = "11",
pages = "1--43",
url = "http://www.jstatsoft.org/v55/i11/",
textVersion =
paste("Adrian Baddeley, Rolf Turner, Jorge Mateu, Andrew Bevan (2013).",
"Hybrids of Gibbs Point Process Models and Their Implementation.",
"Journal of Statistical Software, 55(11), 1-43.",
"URL http://www.jstatsoft.org/v55/i11/."),
header = "If you use hybrid models, please also cite:"
)
spatstat/inst/doc/ 0000755 0001760 0000144 00000000000 12252324024 013663 5 ustar ripley users spatstat/inst/doc/getstart.pdf 0000644 0001760 0000144 00000346375 12252324027 016240 0 ustar ripley users %PDF-1.5
%
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options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1))))
@
\usepackage{graphicx}
\usepackage[colorlinks=true,urlcolor=blue]{hyperref}
\usepackage{color}
\newcommand{\pkg}[1]{\texttt{#1}}
\newcommand{\bold}[1]{{\textbf {#1}}}
\newcommand{\R}{{\sf R}}
\begin{document}
%\bibliographystyle{plain}
\thispagestyle{empty}
<>=
library(spatstat)
options(useFancyQuotes=FALSE)
sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"),
fields = "Date")
sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"),
fields = "Version")
@
\title{Handling shapefiles in the \texttt{spatstat} package}
\author{Adrian Baddeley}
\date{
\Sexpr{sdate} \\
\pkg{spatstat} version \texttt{\Sexpr{sversion}}
}
\maketitle
This vignette explains how to read data into the \pkg{spatstat} package
from files in the popular `shapefile' format.
This vignette is part of the documentation included in
\pkg{spatstat} version \texttt{\Sexpr{sversion}}.
The information applies to
\pkg{spatstat} versions \texttt{1.18-0} and above.
\section{Shapefiles}
A shapefile represents a list of spatial objects
--- a list of points, a list of lines, or a list of polygonal regions ---
and each object in the list may have additional variables attached to it.
A dataset stored in shapefile format is actually stored in a
collection of text files, for example
\begin{verbatim}
mydata.shp
mydata.prj
mydata.sbn
mydata.dbf
\end{verbatim}
which all have the same base name \texttt{mydata} but different file extensions.
To refer to this collection you will always use the filename with the
extension \texttt{shp}, for example \texttt{mydata.shp}.
\section{Helper packages}
\label{S:helpers}
We'll use two other packages%
\footnote{In previous versions of \pkg{spatstat},
the package \pkg{gpclib} was also needed for some tasks.
This is no longer required.}
to handle shapefile data.
The \pkg{maptools} package is designed specifically for handling
file formats for spatial data. It contains
facilities for reading and writing files in shapefile format.
The \pkg{sp} package supports a standard set of spatial data types
in \R. These standard data types can be handled by many other packages,
so it is useful to convert your spatial data into one of the
data types supported by \pkg{sp}.
\section{How to read shapefiles into \pkg{spatstat}}
To read shapefile data into \pkg{spatstat}, you follow two steps:
\begin{enumerate}
\item
using the facilities of \pkg{maptools}, read the shapefiles
and store the data in one of the standard formats supported by \pkg{sp}.
\item
convert the \pkg{sp} data type into one of the data types
supported by \pkg{spatstat}.
\end{enumerate}
\subsection{Read shapefiles using \pkg{maptools}}
Here's how to read shapefile data.
\begin{enumerate}
\item ensure that the package \pkg{maptools} is installed. You will need
version \texttt{0.7-16} or later.
\item start R and load the package:
<>=
library(maptools)
@
\item read the shapefile into an object in the \pkg{sp} package
using \texttt{readShapeSpatial}, for example
<>=
x <- readShapeSpatial("mydata.shp")
@
\item
To find out what kind of spatial objects are represented by the dataset,
inspect its class:
<>=
class(x)
@
The class may be either \texttt{SpatialPoints} indicating a point pattern,
\texttt{SpatialLines} indicating a list of polygonal lines, or
\texttt{SpatialPolygons} indicating a list of polygons. It may also be
\texttt{SpatialPointsDataFrame},
\texttt{SpatialLinesDataFrame} or
\texttt{SpatialPolygonsDataFrame} indicating that, in addition to the
spatial objects, there is a data frame of additional variables.
\end{enumerate}
Here are some examples, using the example shapefiles supplied in the
\pkg{maptools} package itself.
% fake data because we don't want spatstat to depend on maptools
<>=
baltim <- columbus <- fylk <- list()
class(baltim) <- "SpatialPointsDataFrame"
class(columbus) <- "SpatialPolygonsDataFrame"
class(fylk) <- "SpatialLinesDataFrame"
@
<>=
setwd(system.file("shapes", package="maptools"))
baltim <- readShapeSpatial("baltim.shp")
columbus <- readShapeSpatial("columbus.shp")
fylk <- readShapeSpatial("fylk-val.shp")
@
<<>>=
class(baltim)
class(columbus)
class(fylk)
@
\subsection{Convert data to \pkg{spatstat} format}
To convert the dataset to an object in the
\pkg{spatstat} package, the procedure depends on the
type of data, as explained below.
\subsubsection{Objects of class \texttt{SpatialPoints}}
An object \texttt{x} of class \texttt{SpatialPoints}
represents a spatial point pattern.
Use \verb!as(x, "ppp")! or \texttt{as.ppp(x)} to convert it to a
spatial point pattern in \pkg{spatstat}.
The window for the point pattern will be taken from
the bounding box of the points. You will probably wish to change this window,
usually by taking another dataset to provide the window information.
Use \verb![.ppp! to change the window: if \texttt{X} is a point pattern
object of class \verb!"ppp"! and \texttt{W} is a window object of class
\verb!"owin"!, type
<>=
X <- X[W]
@
\subsubsection{Objects of class \texttt{SpatialPointsDataFrame }}
An object \texttt{x} of class \texttt{SpatialPointsDataFrame}
represents a pattern of points with additional variables (`marks') attached to
each point. It includes an object of class \texttt{SpatialPoints}
giving the point locations, and a data frame containing the
additional variables attached to the points.
Use \verb!as(x, "ppp")! or \texttt{as.ppp(x)} to convert an
object \texttt{x} of class \texttt{SpatialPointsDataFrame} to a
spatial point pattern in \pkg{spatstat}. In this conversion,
the data frame of additional variables in \texttt{x} will
become the \texttt{marks} of the point pattern \texttt{z}.
<>=
y <- as(x, "ppp")
@
Before the conversion you can extract the
data frame of auxiliary data by
\verb!df <- x@data! or \verb!df <- slot(x, "data")!.
After the conversion you can extract these data by
\verb!df <- marks(y)!.
For example:
<>=
balt <- as(baltim, "ppp")
bdata <- slot(baltim, "data")
@
\subsubsection{Objects of class \texttt{SpatialLines}}
\label{spatiallines.2.psp}
A ``line segment'' is the straight line between two points in the plane.
In the \pkg{spatstat} package, an object of class \texttt{psp}
(``planar segment pattern'')
represents a pattern of line segments, which may or may not be
connected to each other (like matches which have fallen at random
on the ground).
In the \pkg{sp} package, an object of class \texttt{SpatialLines}
represents a \textbf{list of lists} of \textbf{connected curves},
each curve consisting of a sequence of straight
line segments that are joined together (like
several pieces of a broken bicycle chain.)
So these two data types do not correspond exactly.
The list-of-lists hierarchy in a \texttt{SpatialLines} object
is useful when representing internal divisions in a country.
For example, if \texttt{USA} is an object of class \texttt{SpatialLines}
representing the borders of the United States
of America, then \verb!USA@lines! might be a list of length 52, with
\verb!USA@lines[[i]]! representing the borders of the \texttt{i}-th State.
The borders of each State consist of several different curved lines. Thus
\verb!USA@lines[[i]]@Lines[[j]]! would represent the \texttt{j}th
piece of the boundary of the \texttt{i}-th State.
If \texttt{x} is an object of class \texttt{SpatialLines},
there are several things that you might want to do:
\begin{enumerate}
\item
collect together all the line segments (all the segments that make up all the
connected curves) and store them as a single object of class \texttt{psp}.
\begin{quote}
To do this,
use \verb!as(x, "psp")! or \texttt{as.psp(x)} to convert it to a
spatial line segment pattern.
\end{quote}
\item
convert each connected curve to an object of class \texttt{psp},
keeping different connected curves separate.
To do this, type something like the following:
<>=
out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) })
@
The result will be a \textbf{list of lists} of objects of class \texttt{psp}.
Each one of these objects represents a connected curve,
although the \pkg{spatstat} package does not know that.
The list structure will reflect the list structure of the original
\texttt{SpatialLines} object \texttt{x}. If that's not what you want,
then use \verb!curvelist <- do.call("c", out)! or
<>=
curvegroup <- lapply(out, function(z) { do.call("superimposePSP", z)})
@
to collapse the list-of-lists-of-\texttt{psp}'s
into a list-of-\texttt{psp}'s. In the first case, \texttt{curvelist[[i]]}
is a \texttt{psp} object representing the \texttt{i}-th connected curve.
In the second case, \texttt{curvegroup[[i]]}
is a \texttt{psp} object containing all the line segments in
the \texttt{i}-th group of connected curves (for example the
\texttt{i}-th State in the \texttt{USA} example).
\end{enumerate}
The window for the spatial line segment pattern can be specified
as an argument \texttt{window} to the function \texttt{as.psp}.
\subsubsection{Objects of class \texttt{SpatialLinesDataFrame}}
An object \texttt{x} of class \texttt{SpatialLinesDataFrame}
is a \texttt{SpatialLines} object with additional data.
The additional data is stored as a data frame \verb!x@data!
with one row for each entry in \verb!x@lines!, that is,
one row for each group of connected curves.
In the \pkg{spatstat} package, an object of class \texttt{psp}
(representing a collection of line segments)
may have a data frame of marks. Note that each \emph{line segment}
in a \texttt{psp} object may have different mark values.
If \texttt{x} is an object of class \texttt{SpatialLinesDataFrame},
there are two things that you might want to do:
\begin{enumerate}
\item collect together all the line segments that make up all the
connected lines, and store them as a single object of class \texttt{psp}.
\begin{quote}
To do this,
use \verb!as(x, "psp")! or \texttt{as.psp(x)} to convert it to a
marked spatial line segment pattern.
\end{quote}
\item keep each connected curve separate, and convert each connected
curve to an object of class \texttt{psp}.
To do this, type something like the following:
<>=
out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) })
dat <- x@data
for(i in seq(nrow(dat)))
out[[i]] <- lapply(out[[i]], "marks<-", value=dat[i, , drop=FALSE])
@
The result is a list-of-lists-of-\texttt{psp}'s.
See the previous subsection for explanation on how to
change this using \texttt{c()} or \texttt{superimposePSP}.
\end{enumerate}
In either case,
the mark variables attached to a particular \emph{group of connected lines}
in the \texttt{SpatialLinesDataFrame} object, will be duplicated
and attached to each \emph{line segment} in the resulting \texttt{psp} object.
\subsubsection{Objects of class \texttt{SpatialPolygons}}
First, so that we don't go completely crazy, let's introduce some terminology.
A \emph{polygon} is a closed curve that is composed of
straight line segments. You can draw a polygon
without lifting your pen from the paper.
\setkeys{Gin}{width=0.4\textwidth}
\begin{center}
<>=
data(chorley)
plot(as.owin(chorley), lwd=3, main="polygon")
@
\end{center}
A \emph{polygonal region}
is a region in space whose boundary is composed of straight line segments.
A polygonal region may consist of several unconnected pieces, and each piece
may have holes. The boundary of a polygonal region
consists of one or more polygons. To draw the boundary of a polygonal
region, you may need to lift and drop the pen several times.
\setkeys{Gin}{width=0.4\textwidth}
\begin{center}
<>=
data(demopat)
plot(as.owin(demopat), col="blue", main="polygonal region")
@
\end{center}
An object of class \texttt{owin} in \pkg{spatstat}
represents a polygonal region. It is a region of space that is delimited
by boundaries made of lines.
An object \texttt{x} of class \texttt{SpatialPolygons}
represents a \textbf{list of polygonal regions}. For example,
a single object of class \texttt{SpatialPolygons} could
store information about every State in the United States of America
(or the United States of Malaysia). Each State would be a separate
polygonal region (and it might contain holes such as lakes).
There are two things
that you might want to do with an object of class \texttt{SpatialPolygons}:
\begin{enumerate}
\item
combine all the polygonal regions together into a single
polygonal region, and convert this to a single object of class \texttt{owin}.
\begin{quote}
For example, you could combine all the States of the USA together
and obtain a single object that represents the territory of the USA.
To do this, use \verb!as(x, "owin")! or \texttt{as.owin(x)}.
The result is a single window (object of class \texttt{"owin"})
in the \pkg{spatstat} package.
\end{quote}
\item keep the different polygonal regions separate; convert each
one of the polygonal regions to an object of class \texttt{owin}.
\begin{quote}
For example, you could keep the States of the USA separate,
and convert each State to an object of class \texttt{owin}.
\end{quote}
To do this, type the following:
<>=
regions <- slot(x, "polygons")
regions <- lapply(regions, function(x) { SpatialPolygons(list(x)) })
windows <- lapply(regions, as.owin)
@
The result is a list of objects of class \texttt{owin}.
Often it would make sense to convert this to a
tessellation object, by typing
<>=
te <- tess(tiles=windows)
@
\end{enumerate}
The conversion process may generate an error message, saying
that some of the polygons intersect each other, or are self-intersecting,
or violate other geometrical conditions.
This happens because an object of class \texttt{SpatialPolygons}
is just a list of lists of polygons,
possibly self-intersecting or mutually intersecting,
but an object of class \texttt{"owin"} is intended to specify a
well-defined region of space.
If you chose option 1, the conversion process will check whether
any of the polygons in \texttt{x} intersect each other. This often
generates an error with a shapefile representing a division of space
into states or counties or administrative regions,
like the D\'epartements of France,
because two adjacent regions have boundaries that
intersect (even though the intersection has zero area).
If you chose option 2, the conversion process will only check
whether, for each polygonal region in \texttt{x}, the component polygons
intersect each other. This will \emph{usually} avoid the checking problem.
If an error occurs, the error message
will usually specify which component polygons fail the test.
The best strategy is usually just to
plot the object \texttt{x} (using the plot facilities in \pkg{sp})
to identify the problem.
It is possible to suppress the stringent checking of polygons in
\pkg{spatstat} during the conversion:
<>=
spatstat.options(checkpolygons=FALSE)
y <- as(x, "owin")
spatstat.options(checkpolygons=TRUE)
@
The resulting object \texttt{y} should be inspected carefully and used
circumspectly; it has not passed the stringent tests
required for many algorithms in \pkg{spatstat}.
\subsubsection{Objects of class \texttt{SpatialPolygonsDataFrame}}
What a mouthful!
An object \texttt{x} of class \texttt{SpatialPolygonsDataFrame}
represents a list of polygonal regions,
with additional variables attached to
each region. It includes an object of class \texttt{SpatialPolygons}
giving the spatial regions, and a data frame containing the
additional variables attached to the regions.
The regions are extracted by
<>=
y <- as(x, "SpatialPolygons")
@
and you then proceed as above to convert the curves to
\pkg{spatstat} format.
The data frame of auxiliary data is extracted by
\verb!df <- x@data! or \verb!df <- slot(x, "data")!.
For example:
<>=
cp <- as(columbus, "SpatialPolygons")
cregions <- slot(cp, "polygons")
cregions <- lapply(cregions, function(x) { SpatialPolygons(list(x)) })
cwindows <- lapply(cregions, as.owin)
@
There is currently no facility in \pkg{spatstat} for attaching
marks to an \texttt{owin} object directly.
However, \pkg{spatstat} supports objects called \textbf{hyperframes},
which are like data frames except that the entries can be any type of object.
Thus we can represent the \texttt{columbus} data in \pkg{spatstat} as
follows:
<>=
ch <- hyperframe(window=cwindows)
ch <- cbind.hyperframe(ch, columbus@data)
@
Then \texttt{ch} is a hyperframe containing a column of \texttt{owin}
objects followed by the columns of auxiliary data.
\end{document}
spatstat/inst/doc/replicated.R 0000644 0001760 0000144 00000037730 12252324024 016134 0 ustar ripley users ### R code from vignette source 'replicated.Rnw'
###################################################
### code chunk number 1: replicated.Rnw:29-30
###################################################
options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1))))
###################################################
### code chunk number 2: replicated.Rnw:35-42
###################################################
library(spatstat)
spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) })
sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"),
fields = "Date")
sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"),
fields = "Version")
options(useFancyQuotes=FALSE)
###################################################
### code chunk number 3: replicated.Rnw:189-190
###################################################
waterstriders
###################################################
### code chunk number 4: replicated.Rnw:208-209
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(waterstriders, main="")
###################################################
### code chunk number 5: replicated.Rnw:216-217
###################################################
summary(waterstriders)
###################################################
### code chunk number 6: replicated.Rnw:225-226
###################################################
X <- listof(rpoispp(100), rpoispp(100), rpoispp(100))
###################################################
### code chunk number 7: replicated.Rnw:231-233
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(X)
X
###################################################
### code chunk number 8: replicated.Rnw:262-263 (eval = FALSE)
###################################################
## hyperframe(...)
###################################################
### code chunk number 9: replicated.Rnw:288-290
###################################################
H <- hyperframe(X=1:3, Y=list(sin,cos,tan))
H
###################################################
### code chunk number 10: replicated.Rnw:298-303
###################################################
G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]),
W=list(rpoispp(100),rpoispp(100), rpoispp(100)),
U=42,
V=rpoispp(100), stringsAsFactors=FALSE)
G
###################################################
### code chunk number 11: replicated.Rnw:332-333
###################################################
simba
###################################################
### code chunk number 12: replicated.Rnw:346-347
###################################################
pyramidal
###################################################
### code chunk number 13: replicated.Rnw:353-354
###################################################
ws <- hyperframe(Striders=waterstriders)
###################################################
### code chunk number 14: replicated.Rnw:361-363
###################################################
H$X
H$Y
###################################################
### code chunk number 15: replicated.Rnw:373-375
###################################################
H$U <- letters[1:3]
H
###################################################
### code chunk number 16: replicated.Rnw:380-384
###################################################
G <- hyperframe()
G$X <- waterstriders
G$Y <- 1:3
G
###################################################
### code chunk number 17: replicated.Rnw:392-396
###################################################
H[,1]
H[2,]
H[2:3, ]
H[1,1]
###################################################
### code chunk number 18: replicated.Rnw:402-405
###################################################
H[,1,drop=TRUE]
H[1,1,drop=TRUE]
H[1,2,drop=TRUE]
###################################################
### code chunk number 19: replicated.Rnw:418-419 (eval = FALSE)
###################################################
## plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL)
###################################################
### code chunk number 20: replicated.Rnw:434-435
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(waterstriders, pch=16, nrows=1)
###################################################
### code chunk number 21: replicated.Rnw:450-451
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(simba)
###################################################
### code chunk number 22: replicated.Rnw:463-465
###################################################
getOption("SweaveHooks")[["fig"]]()
H <- hyperframe(X=1:3, Y=list(sin,cos,tan))
plot(H$Y)
###################################################
### code chunk number 23: replicated.Rnw:477-478 (eval = FALSE)
###################################################
## plot(h, e)
###################################################
### code chunk number 24: replicated.Rnw:487-488
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }))
###################################################
### code chunk number 25: replicated.Rnw:500-502
###################################################
getOption("SweaveHooks")[["fig"]]()
H <- hyperframe(Bugs=waterstriders)
plot(H, quote(plot(Kest(Bugs))), marsize=1)
###################################################
### code chunk number 26: replicated.Rnw:515-517
###################################################
df <- data.frame(A=1:10, B=10:1)
with(df, A-B)
###################################################
### code chunk number 27: replicated.Rnw:530-531 (eval = FALSE)
###################################################
## with(h,e)
###################################################
### code chunk number 28: replicated.Rnw:541-544
###################################################
H <- hyperframe(Bugs=waterstriders)
with(H, npoints(Bugs))
with(H, distmap(Bugs))
###################################################
### code chunk number 29: replicated.Rnw:567-568
###################################################
with(simba, npoints(Points))
###################################################
### code chunk number 30: replicated.Rnw:575-577
###################################################
H <- hyperframe(Bugs=waterstriders)
K <- with(H, Kest(Bugs))
###################################################
### code chunk number 31: replicated.Rnw:585-586
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(K)
###################################################
### code chunk number 32: replicated.Rnw:591-593
###################################################
H <- hyperframe(Bugs=waterstriders)
with(H, nndist(Bugs))
###################################################
### code chunk number 33: replicated.Rnw:599-600
###################################################
with(H, min(nndist(Bugs)))
###################################################
### code chunk number 34: replicated.Rnw:612-613
###################################################
simba$Dist <- with(simba, distmap(Points))
###################################################
### code chunk number 35: replicated.Rnw:626-630
###################################################
getOption("SweaveHooks")[["fig"]]()
lambda <- rexp(6, rate=1/50)
H <- hyperframe(lambda=lambda)
H$Points <- with(H, rpoispp(lambda))
plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4)))))
###################################################
### code chunk number 36: replicated.Rnw:636-637
###################################################
H$X <- with(H, rpoispp(50))
###################################################
### code chunk number 37: replicated.Rnw:666-667
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(simba, quote(plot(density(Points), main="")), nrows=2)
###################################################
### code chunk number 38: replicated.Rnw:686-688
###################################################
getOption("SweaveHooks")[["fig"]]()
rhos <- with(demohyper, rhohat(Points, Image))
plot(rhos)
###################################################
### code chunk number 39: replicated.Rnw:705-706 (eval = FALSE)
###################################################
## mppm(formula, data, interaction, ...)
###################################################
### code chunk number 40: replicated.Rnw:716-717 (eval = FALSE)
###################################################
## mppm(Points ~ group, simba, Poisson())
###################################################
### code chunk number 41: replicated.Rnw:750-751
###################################################
mppm(Points ~ 1, simba)
###################################################
### code chunk number 42: replicated.Rnw:758-759
###################################################
mppm(Points ~ group, simba)
###################################################
### code chunk number 43: replicated.Rnw:765-766
###################################################
mppm(Points ~ id, simba)
###################################################
### code chunk number 44: replicated.Rnw:776-777
###################################################
mppm(Points ~ Image, data=demohyper)
###################################################
### code chunk number 45: replicated.Rnw:795-796 (eval = FALSE)
###################################################
## mppm(Points ~ offset(log(Image)), data=demohyper)
###################################################
### code chunk number 46: replicated.Rnw:808-809 (eval = FALSE)
###################################################
## mppm(Points ~ log(Image), data=demop)
###################################################
### code chunk number 47: replicated.Rnw:826-827 (eval = FALSE)
###################################################
## mppm(formula, data, interaction, ..., iformula=NULL)
###################################################
### code chunk number 48: replicated.Rnw:877-878
###################################################
radii <- with(simba, mean(nndist(Points)))
###################################################
### code chunk number 49: replicated.Rnw:885-887
###################################################
Rad <- hyperframe(R=radii)
Str <- with(Rad, Strauss(R))
###################################################
### code chunk number 50: replicated.Rnw:892-894
###################################################
Int <- hyperframe(str=Str)
mppm(Points ~ 1, simba, interaction=Int)
###################################################
### code chunk number 51: replicated.Rnw:921-924
###################################################
h <- hyperframe(Y=waterstriders)
g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7))
mppm(Y ~ 1, data=h, interaction=g, iformula=~str4)
###################################################
### code chunk number 52: replicated.Rnw:935-936
###################################################
fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group)
###################################################
### code chunk number 53: replicated.Rnw:954-955
###################################################
fit
###################################################
### code chunk number 54: replicated.Rnw:958-960
###################################################
co <- coef(fit)
si <- function(x) { signif(x, 4) }
###################################################
### code chunk number 55: replicated.Rnw:971-972
###################################################
coef(fit)
###################################################
### code chunk number 56: replicated.Rnw:1029-1030 (eval = FALSE)
###################################################
## interaction=hyperframe(po=Poisson(), str=Strauss(0.07))
###################################################
### code chunk number 57: replicated.Rnw:1035-1036 (eval = FALSE)
###################################################
## iformula=~ifelse(group=="control", po, str)
###################################################
### code chunk number 58: replicated.Rnw:1046-1047 (eval = FALSE)
###################################################
## iformula=~I((group=="control")*po) + I((group=="treatment") * str)
###################################################
### code chunk number 59: replicated.Rnw:1057-1062
###################################################
g <- hyperframe(po=Poisson(), str=Strauss(0.07))
fit2 <- mppm(Points ~ 1, simba, g,
iformula=~I((group=="control")*po)
+ I((group=="treatment") * str))
fit2
###################################################
### code chunk number 60: replicated.Rnw:1085-1088
###################################################
H <- hyperframe(W=waterstriders)
fit <- mppm(W ~ 1, H)
subfits(fit)
###################################################
### code chunk number 61: replicated.Rnw:1109-1110 (eval = FALSE)
###################################################
## subfits <- subfits.new
###################################################
### code chunk number 62: replicated.Rnw:1122-1124
###################################################
H <- hyperframe(W=waterstriders)
with(H, ppm(W))
###################################################
### code chunk number 63: replicated.Rnw:1147-1149
###################################################
fit <- mppm(P ~ x, hyperframe(P=waterstriders))
res <- residuals(fit)
###################################################
### code chunk number 64: replicated.Rnw:1159-1160
###################################################
getOption("SweaveHooks")[["fig"]]()
plot(res)
###################################################
### code chunk number 65: replicated.Rnw:1165-1167
###################################################
getOption("SweaveHooks")[["fig"]]()
smor <- with(hyperframe(res=res), Smooth(res, sigma=4))
plot(smor)
###################################################
### code chunk number 66: replicated.Rnw:1179-1182
###################################################
fit <- mppm(P ~ x, hyperframe(P=waterstriders))
res <- residuals(fit)
totres <- sapply(res, integral.msr)
###################################################
### code chunk number 67: replicated.Rnw:1188-1195
###################################################
getOption("SweaveHooks")[["fig"]]()
fit <- mppm(Points~Image, data=demohyper)
resids <- residuals(fit, type="Pearson")
totres <- sapply(resids, integral.msr)
areas <- with(demohyper, area.owin(as.owin(Points)))
df <- as.data.frame(demohyper[, "Group"])
df$resids <- totres/areas
plot(resids~Group, df)
###################################################
### code chunk number 68: replicated.Rnw:1216-1219
###################################################
getOption("SweaveHooks")[["fig"]]()
fit <- mppm(P ~ 1, hyperframe(P=waterstriders))
sub <- hyperframe(Model=subfits(fit))
plot(sub, quote(diagnose.ppm(Model)))
###################################################
### code chunk number 69: replicated.Rnw:1232-1240
###################################################
H <- hyperframe(P = waterstriders)
fitall <- mppm(P ~ 1, H)
together <- subfits(fitall)
separate <- with(H, ppm(P))
Fits <- hyperframe(Together=together, Separate=separate)
dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together)))
dr
exp(dr)
###################################################
### code chunk number 70: replicated.Rnw:1257-1266
###################################################
H <- hyperframe(X=waterstriders)
# Poisson with constant intensity for all patterns
fit1 <- mppm(X~1, H)
quadrat.test(fit1, nx=2)
# uniform Poisson with different intensity for each pattern
fit2 <- mppm(X ~ id, H)
quadrat.test(fit2, nx=2)
###################################################
### code chunk number 71: replicated.Rnw:1295-1296 (eval = FALSE)
###################################################
## kstest.mppm(model, covariate)
spatstat/inst/doc/replicated.pdf 0000644 0001760 0000144 00001412431 12252324033 016500 0 ustar ripley users %PDF-1.5
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2013-12-12T20:12:07+08:00
2013-12-12T20:12:07+08:00
David M. Jones
CMR17
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