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##
## logistic.R
##
## $Revision: 1.28 $ $Date: 2020/11/27 03:04:30 $
##
## Logistic composite likelihood method
##
## (quadscheme construction is now in 'logiquad.R')
logi.engine <- function(Q,
trend = ~1,
interaction,
...,
covariates=NULL,
subsetexpr=NULL,
clipwin=NULL,
correction="border",
rbord=reach(interaction),
covfunargs=list(),
allcovar=FALSE,
vnamebase=c("Interaction", "Interact."),
vnameprefix=NULL,
justQ = FALSE,
savecomputed = FALSE,
precomputed = NULL,
VB=FALSE
){
if(is.null(trend)) trend <- ~1
if(is.null(interaction)) interaction <- Poisson()
want.trend <- !identical.formulae(trend, ~1)
want.inter <- !is.poisson(interaction)
want.subset <- !is.null(subsetexpr)
# validate choice of edge correction
correction <- pickoption("correction", correction,
c(border="border",
periodic="periodic",
isotropic="isotropic",
Ripley="isotropic",
trans="translate",
translate="translate",
translation="translate",
none="none"))
# rbord applies only to border correction
if(correction == "border") {
check.1.real(rbord, "In ppm")
explain.ifnot(rbord >= 0, "In ppm")
} else rbord <- 0
# backdoor stuff
if(!missing(vnamebase)) {
if(length(vnamebase) == 1)
vnamebase <- rep.int(vnamebase, 2)
if(!is.character(vnamebase) || length(vnamebase) != 2)
stop("Internal error: illegal format of vnamebase")
}
if(!is.null(vnameprefix)) {
if(!is.character(vnameprefix) || length(vnameprefix) != 1)
stop("Internal error: illegal format of vnameprefix")
}
# create dummy points
if(inherits(Q, "ppp")){
Xplus <- Q
Q <- quadscheme.logi(Xplus, ...)
D <- Q$dummy
Dinfo <- Q$param
} else if(checkfields(Q, c("data", "dummy"))) {
Xplus <- Q$data
D <- Q$dummy
Dinfo <- Q$param
if(is.null(Dinfo)){
Dinfo <- list(how="given", rho=npoints(D)/(area(D)*markspace.integral(D)))
}
Q <- quadscheme.logi(Xplus, D)
} else stop("Format of object Q is not understood")
## clip to subset?
if(!is.null(clipwin)) {
if(is.data.frame(covariates)) {
ok <- inside.owin(union.quad(Q), w=clipwin)
covariates <- covariates[ok, , drop=FALSE]
}
Q <- Q[clipwin]
Xplus <- Q$data
D <- Q$dummy
}
if (justQ)
return(Q)
### Dirty way of recording arguments so that the model can be refitted later (should probably be done using call, eval, envir, etc.):
extraargs <- list(covfunargs = covfunargs, allcovar = allcovar, vnamebase = vnamebase, vnameprefix = vnameprefix)
extraargs <- append(extraargs, list(...))
## Dummy intensity
if(correction == "border" && Dinfo$how=="grid"){
Dbord <- D[bdist.points(D)>=rbord]
Dinfo$rho <- npoints(Dbord)/(eroded.areas(as.owin(Dbord), rbord)*markspace.integral(Dbord))
}
rho <- Dinfo$rho
##Setting the B from Barker dynamics (relative to dummy intensity)
B <- list(...)$Barker
if(is.null(B))
B <- 1
B <- B*rho
Dinfo <- append(Dinfo, list(B=B))
Dinfo <- append(Dinfo, list(extraargs=extraargs))
#
Wplus <- as.owin(Xplus)
nXplus <- npoints(Xplus)
U <- superimpose(Xplus, D, W=Wplus, check=FALSE)
# E <- equalpairs(U, Xplus, marked = is.marked(Xplus))
E <- cbind(1:nXplus, 1:nXplus)
#
computed <- if (savecomputed) list(X = Xplus, Q = Q, U = U) else list()
# assemble covariate data frame
if(want.trend || want.subset) {
tvars <- variablesinformula(trend)
if(want.subset)
tvars <- union(tvars, all.vars(subsetexpr))
if(!is.data.frame(covariates)) {
## resolve 'external' covariates
externalvars <- setdiff(tvars, c("x", "y", "marks"))
tenv <- environment(trend)
covariates <- getdataobjects(externalvars, tenv, covariates, fatal=TRUE)
}
wantxy <- c("x", "y") %in% tvars
wantxy <- wantxy | rep.int(allcovar, 2)
cvdf <- data.frame(x=U$x, y=U$y)[, wantxy, drop=FALSE]
if(!is.null(covariates)) {
df <- mpl.get.covariates(covariates, U, "quadrature points", covfunargs)
cvdf <- cbind(cvdf, df)
}
wantmarks <- "marks" %in% tvars
if(wantmarks) cvdf <- cbind(cvdf, marks = marks(U))
} else cvdf <- NULL
# evaluate interaction sufficient statistics
if (!is.null(ss <- interaction$selfstart))
interaction <- ss(Xplus, interaction)
V <- evalInteraction(Xplus, U, E, interaction, correction, precomputed = precomputed, savecomputed = savecomputed)
if(!is.matrix(V))
stop("evalInteraction did not return a matrix")
if (savecomputed)
computed <- append(computed, attr(V, "computed"))
IsOffset <- attr(V, "IsOffset")
if(is.null(IsOffset)) IsOffset <- rep.int(FALSE, ncol(V))
# determine names
if(ncol(V) > 0) {
Vnames <- colnames(V)
if(is.null(Vnames)) {
nc <- ncol(V)
Vnames <- if(nc == 1) vnamebase[1L] else paste(vnamebase[2L], 1:nc, sep="")
colnames(V) <- Vnames
} else if(!is.null(vnameprefix)) {
Vnames <- paste(vnameprefix, Vnames, sep="")
colnames(V) <- Vnames
}
} else Vnames <- character(0)
# combine all data
glmdata <- as.data.frame(V)
if(!is.null(cvdf)) glmdata <- cbind(glmdata, cvdf)
# construct response and weights
ok <- if(correction == "border") (bdist.points(U) >= rbord) else rep.int(TRUE, npoints(U))
# Keep only those quadrature points for which the
# conditional intensity is nonzero.
KEEP <- if(ncol(V)>0) matrowall(V != -Inf) else rep.int(TRUE, npoints(U))
ok <- ok & KEEP
wei <- c(rep.int(1,npoints(Xplus)),rep.int(B/rho,npoints(D)))
resp <- c(rep.int(1,npoints(Xplus)),rep.int(0,npoints(D)))
## User-defined subset:
if(!is.null(subsetexpr)) {
USERSUBSET <- eval(subsetexpr, glmdata, environment(trend))
ok <- ok & USERSUBSET
}
# add offset, subset and weights to data frame
# using reserved names beginning with ".logi."
glmdata <- cbind(glmdata,
.logi.Y = resp,
.logi.B = B,
.logi.w = wei,
.logi.ok =ok)
# build glm formula
# (reserved names begin with ".logi.")
trendpart <- paste(as.character(trend), collapse=" ")
fmla <- paste(".logi.Y ", trendpart)
# Interaction terms
if(want.inter) {
VN <- Vnames
# enclose offset potentials in 'offset(.)'
if(any(IsOffset))
VN[IsOffset] <- paste("offset(", VN[IsOffset], ")", sep="")
fmla <- paste(c(fmla, VN), collapse="+")
}
# add offset intrinsic to logistic technique
fmla <- paste(fmla, "offset(-log(.logi.B))", sep="+")
fmla <- as.formula(fmla)
# to satisfy package checker:
.logi.B <- B
.logi.w <- wei
.logi.ok <- ok
.logi.Y <- resp
# suppress warnings from code checkers
dont.complain.about(.logi.B, .logi.w, .logi.ok, .logi.Y)
# go
##fit <- glm(fmla, data=glmdata,
## family=binomial(), subset = .logi.ok, weights = .logi.w)
fit <- if(VB)
vblogit.fmla(fmla, data = glmdata,
subset = .logi.ok, weights = .logi.w, ...)
else
glm(fmla, data = glmdata,
family = binomial(), subset = .logi.ok, weights = .logi.w)
environment(fit$terms) <- sys.frame(sys.nframe())
## Fitted coeffs
co <- coef(fit)
fitin <- fii(interaction, co, Vnames, IsOffset)
## Saturated log-likelihood:
satlogpl <- sum(ok*resp*log(B))
## Max. value of log-likelihood:
maxlogpl <- logLik(fit) + satlogpl
# Stamp with spatstat version number
spv <- package_version(versionstring.spatstat())
the.version <- list(major=spv$major,
minor=spv$minor,
release=spv$patchlevel,
date="$Date: 2020/11/27 03:04:30 $")
## Compile results
fit <- list(method = "logi",
fitter = "glm",
projected = FALSE,
coef = co,
trend = trend,
interaction = interaction,
fitin = fitin,
Q = Q,
maxlogpl = maxlogpl,
satlogpl = satlogpl,
internal = list(Vnames = Vnames,
IsOffset=IsOffset,
glmdata = glmdata,
glmfit = fit,
logistic = Dinfo,
computed = computed,
vnamebase=vnamebase,
vnameprefix=vnameprefix,
VB = if(VB) TRUE else NULL,
priors = if(VB) fit$priors else NULL
),
covariates = mpl.usable(covariates),
covfunargs= covfunargs,
subsetexpr = subsetexpr,
correction = correction,
rbord = rbord,
fisher = NULL,
varcov = NULL, # if(VB) fit$S else NULL,
terms = terms(trend),
version = the.version,
problems = list()
)
class(fit) <- "ppm"
return(fit)
}
forbid.logi <- function(object) {
if(object$method == "logi")
stop("Sorry, this is not implemented for method=\'logi\'")
return(invisible(NULL))
}
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