1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
|
#
# psst.R
#
# Computes the GNZ contrast of delta-f for any function f
#
# $Revision: 1.10 $ $Date: 2022/01/04 05:30:06 $
#
################################################################################
#
psst <- function(object, fun, r=NULL, breaks=NULL, ...,
model=NULL,
trend=~1, interaction=Poisson(),
rbord=reach(interaction),
truecoef=NULL, hi.res=NULL,
funargs=list(correction="best"),
verbose=TRUE) {
if(is.ppm(object)) {
fit <- object
} else if(is.ppp(object) || is.quad(object)) {
if(is.ppp(object)) object <- quadscheme(object, ...)
if(!is.null(model)) {
fit <- update(model, Q=object, forcefit=TRUE)
} else {
fit <- ppm(object, trend=trend, interaction=interaction, rbord=rbord,
forcefit=TRUE)
}
} else
stop("object should be a fitted point process model or a point pattern")
# rfixed <- !is.null(r) || !is.null(breaks)
# Extract data and quadrature points
Q <- quad.ppm(fit, drop=FALSE)
X <- data.ppm(fit)
U <- union.quad(Q)
Z <- is.data(Q) # indicator data/dummy
# E <- equalsfun.quad(Q)
# WQ <- w.quad(Q) # quadrature weights
# integrals will be restricted to quadrature points
# that were actually used in the fit
# USED <- getglmsubset(fit)
if(fit$correction == "border") {
rbord <- fit$rbord
b <- bdist.points(U)
USED <- (b > rbord)
} else USED <- rep.int(TRUE, U$n)
# basic statistics
Win <- Window(X)
npts <- npoints(X)
areaW <- area(Win)
lambda <- npts/areaW
# adjustments to account for restricted domain of pseudolikelihood
# if(any(!USED) && spatstat.options("eroded.intensity")) {
# XUSED <- USED[Z]
# npts.used <- sum(Z & USED)
# area.used <- sum(WQ[USED])
# lambda.used <- npts.used/area.used
# } else {
# XUSED <- rep.int(TRUE, npts)
# npts.used <- npts
# area.used <- areaW
# lambda.used <- lambda
# }
# determine breakpoints for r values
rmaxdefault <- rmax.rule("G", Win, lambda)
breaks <- handle.r.b.args(r, breaks, Win, rmaxdefault=rmaxdefault)
rvals <- breaks$r
rmax <- breaks$max
# residuals
resid <- residuals(fit, type="raw",drop=FALSE,
new.coef=truecoef, quad=hi.res)
rescts <- with(resid, "continuous")
# absolute weight for continuous integrals
wc <- -rescts
# initialise fv object
df <- data.frame(r=rvals, theo=0)
desc <- c("distance argument r", "value 0 corresponding to perfect fit")
ans <- fv(df, "r", substitute(bold(R)~Delta~S(r), NULL),
"theo", . ~ r,
alim=c(0, rmax), c("r","%s[theo](r)"), desc,
fname="bold(R)~Delta~S")
# evaluate fun(X) for data
fX <- do.call(fun, append(list(X, r=rvals), funargs))
fXunits <- unitname(fX)
# Extract 'best' estimate only
fX <- with(fX, .y)
zero <- numeric(length(fX))
# sum over all quadrature points
iused <- seq(U$n)[USED]
nused <- length(iused)
if(verbose) cat(paste("\nProcessing", nused, "quadrature points..."))
# running sums & integrals
sumX <- zero
integ <- integ2 <- zero
# template for X \cup {u}
uX <- superimpose(U[1], X, W=Win, check=FALSE)
Ux <- U$x
Uy <- U$y
#
if(verbose) pstate <- list()
#
for(j in seq(nused)) {
i <- iused[j]
wi <- wc[i]
if(Z[i]) {
# data point
fXi <- do.call(fun, append(list(X[-i], r=rvals), funargs))
fXi <- with(fXi, .y)
deltaf <- fX - fXi
sumX <- sumX + deltaf
} else {
# dummy point
uX$x[1] <- Ux[i]
uX$y[1] <- Uy[i]
fuX <- do.call(fun, append(list(uX, r=rvals), funargs))
fuX <- with(fuX, .y)
deltaf <- fuX - fX
}
integ <- integ + wi * deltaf
integ2 <- integ2 + wi * deltaf^2
#
if(j %% 500 == 0) {
cat("[garbage ")
gc()
cat("collected]")
}
if(verbose) pstate <- progressreport(j, nused, state=pstate)
}
sdv <- sqrt(integ2)
res <- sumX - integ
ans <- bind.fv(ans,
data.frame(dat=sumX,
com=integ,
var=integ2,
sd=sdv,
hi=2*sdv,
lo=-2*sdv,
res=res,
stdres=res/sdv),
c("Sigma~Delta~S(r)",
"bold(C)~Delta~S(r)",
"bold(C)^2~Delta~S(r)",
"sqrt(bold(C)^2~Delta~S(r))",
"%s[hi](r)",
"%s[lo](r)",
"bold(R)~Delta~S(r)",
"bold(T)~Delta~S(r)"),
c("data pseudosum (contribution to %s)",
"model compensator (contribution to %s)",
"pseudovariance of %s",
"sqrt(pseudovariance) of %s",
"upper 2 sigma critical band for %s",
"lower 2 sigma critical band for %s",
"pseudoresidual function %s",
"standardised pseudoresidual function %s"),
"res")
fvnames(ans,".") <- c("res", "hi", "lo", "theo")
unitname(ans) <- fXunits
#
return(ans)
}
npfun <- function(X, ..., r) {
npts <- npoints(X)
# initialise fv object
df <- data.frame(r=r, theo=0, npoint=npts)
desc <- c("distance argument r",
"value 0",
"value equal to number of points")
ans <- fv(df, "r", substitute(npoints(r), NULL),
"npoint", . ~ r,
alim=c(0, max(r)), c("r","%s[theo](r)", "%s[obs](r)"),
desc, fname="npoints")
unitname(ans) <- unitname(X)
return(ans)
}
nndcumfun <- function(X, ..., r) {
nn <- nndist(X)
bk <- breakpts.from.r(r)
# nn <- nn[nn <= bdist.points(X)]
h <- whist(nn, bk$val)
# initialise fv object
df <- data.frame(r=r, theo=0, obs=h)
desc <- c("distance argument r",
"value 0",
"observed count")
ans <- fv(df, "r", substitute(nndcount(r), NULL),
"obs", . ~ r,
alim=c(0, max(r)), c("r","%s[theo](r)", "%s[obs](r)"),
desc, fname="nndcount")
unitname(ans) <- unitname(X)
return(ans)
}
|
