#getusermodel <- function(info, range, cov, derivative) {
# ## hypermodels not allowed!
# stopifnot(!is.null(info), !is.null(range), is.null(check))
# .C("Init")
# if (!is.null(cov))
# .C("AddCov")
# if (!is.null(derivative))
# .C("AddTBM")
#}
GetRegisterInfo <- function(register=0, ignore.active=FALSE, max.elements=10^6) {
# ignore.active=TRUE only for internal debugging information!
if (ignore.active)
warning("ignore.active = TRUE may cause failure of R -- do not use it!!")
.Call("GetRegisterInfo", as.integer(register), as.logical(ignore.active),
as.integer(if (max.elements > .Machine$integer.max)
.Machine$integer.max else max.elements),
PACKAGE="RandomFields")
}
GetModelInfo <- function(register, modelreg, level=3, gatter=FALSE) {
## positive values refer the covariance models in the registers
#define MODEL_USER : 0 /* for user call of Covariance etc. */
#define MODEL_SIMU : 1 /* for GaussRF etc */
#define MODEL_INTERN : 2 /* for kriging, etc; internal call of covariance */
#define MODEL_MLE : 3
#define MODEL_BOUNDS 4 : - /* MLE, lower, upper */
# [ in RF.h we have the modulus minus 1 ]
if (!missing(modelreg)) {
stopifnot(missing(register))
register <- -modelreg -1
} else if (missing(register)) register <- 0
.Call("GetExtModelInfo", as.integer(register), as.integer(level),
as.integer(gatter), PACKAGE="RandomFields")
}
GetModel <- function(register, modelreg, modus=0) {
#define MODEL_USER 0 /* for user call of Covariance etc. */
#define MODEL_SIMU 1 M/* for GaussRF etc */
#define MODEL_INTERN 2 /* for kriging, etc; internal call of covariance */
#define MODEL_MLE 3
#define MODEL_BOUNDS 4 /* MLE, lower, upper */
if (!missing(modelreg)) {
stopifnot(missing(register))
register <- -modelreg -1
} else if (missing(register)) register <- 0
.Call("GetModel", as.integer(register), as.integer(modus),
PACKAGE="RandomFields")
}
GetPracticalRange <- function(model, param, dim=1) {
vdim <- .Call("CheckModelUser", PrepareModel(model, param)$model,
dim=as.integer(dim), xdim=as.integer(dim), FALSE,
PACKAGE="RandomFields")
natscl <- double(1)
.C("UserGetNatScaling", natscl, PACKAGE="RandomFields", DUP=FALSE)
return(1.0 / natscl)
}
#GetPracticalRange("whittle", 1)
GetrfParameters <- function(initcov){
## if initcov then InitModelList is called before
## values are return (necessary to get covnr right, but should
## not be done for RFparameters (debugging reasons)
maxints <- integer(1)
.C("GetMaxDims", maxints, DUP=FALSE, PACKAGE="RandomFields")
p <- .C(c("GetrfParameters", "GetrfParametersI")[1 + (initcov != 0) ],
covmaxchar=integer(1), methodmaxchar=integer(1),
distrmaxchar=integer(1),
covnr=integer(1), methodnr=integer(1), distrnr=integer(1),
maxdim=integer(maxints), maxmodels=integer(1),
PACKAGE="RandomFields")
names(p$maxdim) <- c("cov", "mle", "simu", "ce", "tbm", "mpp", "hyper", "nug",
"vario")
return(p)
}
GetMethodNames <- function() {
assign(".p", GetrfParameters(TRUE))
l <- character(.p$methodnr)
for (i in 1:.p$methodnr) {
l[i] <- .C("GetMethodName", as.integer(i-1),
n=paste(rep(" ", .p$methodmaxchar), collapse=""),
PACKAGE="RandomFields")$n
}
return(l)
}
GetDistributionNames <- function() {
assign(".p", GetrfParameters(TRUE))
l <- character(.p$distrnr)
for (i in 1:.p$distrnr) {
l[i] <- .C("GetDistrName", as.integer(i-1),
n=paste(rep(" ",.p$distrmaxchar), collapse=""),
PACKAGE="RandomFields")$n
}
return(l)
}
GetModelNames <- function(stationary = c("any", "stationary", "variogram", "irf",
"auxmatrix", "auxvector", "gencovariance",
"nonstationary", "genvariogram", "prev.model"),
isotropy = c("any", "isotropic", "spaceisotropic",
"zerospaceiso", "anisotropic", "prev.model"),
multivariate = -9999:9999, ## any !
operator,
normalmix,
finiterange,
methods,
internal=FALSE,
dim = 1) {
# vdim koennte man auch noch in die args aufnehmen.
assign(".p", GetrfParameters(TRUE))
par <- as.character(formals()$stationary)[-1]
stationary <- pmatch(stationary, par, duplicates.ok=TRUE) - 1
stationary <- if (any(stationary == 0)) 0:(length(par) - 2) else stationary - 1
par <- as.character(formals()$isotropy)[-1]
isotropy <- pmatch(isotropy, par, duplicates.ok=TRUE) - 1
isotropy <- if (any(isotropy == 0)) 0:(length(par) - 2) else isotropy - 1
mn <- GetMethodNames()
methods <-
if (missing(methods)) 1:length(mn) else pmatch(methods, mn,
duplicates.ok=TRUE)
empty <- paste(rep(" ", .p$covmaxchar), collapse="")
l <- character(.p$covnr)
for (i in 1:.p$covnr) {
l[i] <- .C("GetModelName",as.integer(i-1),
n=empty, PACKAGE="RandomFields")$n
}
loperator <- integer(.p$covnr)
lnormalmix <- integer(.p$covnr)
lfiniterange <- integer(.p$covnr)
linternal <- integer(.p$covnr)
stats <- integer(.p$covnr)
isos <- integer(.p$covnr)
maxcdim <- integer(.p$covnr)
vdim <- integer(.p$covnr)
.C("GetAttr", loperator, lnormalmix, lfiniterange, linternal,
stats, isos, maxcdim, vdim,
DUP=FALSE, PACKAGE="RandomFields")
idx <- integer(.p$covnr * length(mn))
.C("GetModelList", idx, as.integer(TRUE), PACKAGE="RandomFields", DUP=FALSE)
dim(idx) <- c(.p$covnr, length(mn))
if (!(hyp <- missing(operator))) hyp <- loperator - !operator
if (!(nmix <- missing(normalmix))) nmix <- lnormalmix - !normalmix
if (!(nfr <- missing(finiterange))) nfr <- lfiniterange - !finiterange
if (!(nint <- missing(internal))) nint <- linternal - !internal
return(l[(stats %in% stationary) &(isos %in% isotropy) &
(vdim %in% multivariate) &
hyp & nmix & nfr & nint & (maxcdim >= dim) &
apply(idx, 1, function(x) any(x[methods]))
])
}
GetModelList <- function(abbr=TRUE, internal=FALSE) {
names <- GetModelNames(internal=internal)
methods <- GetMethodNames()
if (abbr) methods <- substr(methods, 1, if (is.logical(abbr)) 5 else abbr)
idx <- integer(length(names) * length(methods))
.C("GetModelList", idx, as.integer(internal), PACKAGE="RandomFields", DUP=FALSE)
t(matrix(as.logical(idx), ncol=length(names), dimnames=list(methods, names)))
}
RFparameters <- function (..., no.readonly=FALSE) {
assign(".p", GetrfParameters(FALSE))
assign(".methods", if (.p$covnr == -1) 0:100 else GetMethodNames())
## do not add any temporary variable til ## **
## do not remove leading "." from .maxdim
PracticalRange <- integer(1)
## always logical returned
## PracticalRange also allows for being set to
## 0 : no practical range
## 1,11 : practical range, evaluated exactly (if given in RFCovfct.cc)
## 2,12 : approximative value (if given in RFCovfct.cc)
## 3,13 : rough guess (good enough for MLE) (if given in RFCovfct.cc)
## >10: and if nothing appropriate given in RFCovfct.cc then numerical approx.
pch <- as.character(" ")
PrintLevel <- integer(1)
Storing <- integer(1)
skipchecks <- integer(1)
stationary.only <- integer(1)
exactness <- integer(1)
every <- integer(1)
# ave.r <- double(1)
# ave.dist <- double(1)
CE.force <- integer(1)
CE.mmin <- double(.p$maxdim["ce"])
CE.strategy <- integer(1)
CE.maxmem <- double(1)
CE.tolIm <- double(1)
CE.tolRe <- double(1)
CE.trials <- integer(1)
CE.useprimes <- integer(1)
CE.dependent <- integer(1)
CE.method <- integer(1)
local.force <- integer(1)
local.mmin <- double(.p$maxdim["ce"])
local.maxmem <- double(1)
local.tolIm <- double(1)
local.tolRe <- double(1)
local.useprimes <- integer(1)
local.dependent <- integer(1)
direct.bestvariables <- integer(1)
direct.maxvariables <- integer(1)
direct.method <- integer(1)
direct.svdtolerance <- double(1)
markov.neighbours <- integer(1)
markov.precision <- double(1)
markov.cyclic <- integer(1)
markov.maxmem <- integer(1)
nugget.tol <- double(1)
nugget.meth <- integer(1)
sequ.max <- integer(1)
sequ.back <- integer(1)
sequ.initial <- integer(1)
## parameters for special functions
## currently none
##
spectral.lines <- integer(.p$maxdim["tbm"])
spectral.grid <- integer(1)
spectral.ergodic <- integer(1)
spectral.metro <- integer(1)
spectral.nmetro <- integer(1)
spectral.sigma <- double(1)
TBM.method <- integer(1)
TBM.center <- double(.p$maxdim["tbm"])
TBM.points <- integer(1)
TBM2.lines <- integer(1)
TBM2.linesimufactor <- double(1)
TBM2.linesimustep <- double(1)
TBM2.layers <- integer(1)
# TBM2.num <- integer(1)
TBM3.lines <- integer(1)
TBM3.linesimufactor <- double(1)
TBM3.linesimustep <- double(1)
TBM3.layers <- integer(1)
TBMCE.force <- integer(1)
TBMCE.mmin <- double(.p$maxdim["ce"])
TBMCE.strategy <- integer(1)
TBMCE.maxmem <- double(1)
TBMCE.tolIm <- double(1)
TBMCE.tolRe <- double(1)
TBMCE.trials <- integer(1)
TBMCE.useprimes <- integer(1)
TBMCE.dependent <- integer(1)
mpp.locations <- integer(1)
mpp.intensity <- double(.p$maxdim["mpp"])
mpp.plus <- double(.p$maxdim["mpp"])
mpp.relRadius <- double(.p$maxdim["mpp"])
# mpp.scale <- double(.p$maxdim["mpp"])
mpp.approxzero <- double(1)
mpp.samplingdist <- double(1)
mpp.samplingr <- double(1)
mpp.p <- double(1)
mpp.beta <- double(1)
hyper.superpos <- integer(1)
hyper.maxlines <- integer(1)
hyper.mar.distr <- integer(1)
hyper.mar.param <- double(1)
maxstable.maxGauss <- double(1)
arg.list <- ls()
## **
## first element is the function name
parameters <- list(...)
for (m in 0:1) {
# m = 0 reading parameters
# m = 1 storing parameters
storage.mode(m) <- "integer"
## "SetParam" more complicated since pch is of character type
pch <- .Call("SetParamPch", m, pch, PACKAGE="RandomFields");
.C("SetParam", m, Storing, PrintLevel, PracticalRange, skipchecks,
every, PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamDecision", m, stationary.only, exactness,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamCircEmbed", m, CE.force, CE.tolRe, CE.tolIm, CE.trials,
CE.mmin, CE.useprimes, CE.strategy, CE.maxmem, CE.dependent,
CE.method,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamLocal", m, local.force, local.tolRe, local.tolIm,
local.mmin, local.useprimes, local.maxmem,
local.dependent,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamDirect", m, direct.method,
direct.bestvariables, direct.maxvariables, direct.svdtolerance,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamMarkov", m, markov.neighbours, markov.precision, markov.cyclic,
markov.maxmem,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamNugget", m, nugget.tol, nugget.meth,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamSequential", m, sequ.max, sequ.back, sequ.initial,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamSpectral", m, spectral.lines, spectral.grid, spectral.ergodic,
spectral.metro, spectral.nmetro, spectral.sigma,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamTBMCE", m, TBMCE.force, TBMCE.tolRe, TBMCE.tolIm, TBMCE.trials,
TBMCE.mmin, TBMCE.useprimes, TBMCE.strategy, TBMCE.maxmem,
TBMCE.dependent,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamTBM2", m, TBM2.lines, TBM2.linesimufactor,
TBM2.linesimustep, TBM2.layers,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamTBM3", m, TBM3.lines, TBM3.linesimufactor,
TBM3.linesimustep, TBM3.layers,
PACKAGE="RandomFields", DUP=FALSE)
.C("SetParamTBM", m, TBM.method, TBM.center, TBM.points,
PACKAGE="RandomFields", DUP=FALSE, NAOK=TRUE)
.C("SetParamMPP", m, mpp.locations, mpp.intensity,
mpp.plus, mpp.relRadius, # mpp.scale,
mpp.approxzero,
mpp.samplingdist, mpp.samplingr, mpp.p, mpp.beta,
PACKAGE="RandomFields", DUP=FALSE)
## in der naehcsten Zeile bringt valgrind einen Fehler
## im system, nicht klar wieso
##17264## Conditional jump or move depends on uninitialised value(s)
##17264## at 0x401620C: (within /lib/ld-2.8.so)
##17264## by 0x42895C3: (within /lib/libc-2.8.so)
##17264## by 0x4289989: _dl_sym (in /lib/libc-2.8.so)
##17264## by 0x4181DE7: (within /lib/libdl-2.8.so)
##17264## by 0x400DE25: (within /lib/ld-2.8.so)
##17264## by 0x41820DB: (within /lib/libdl-2.8.so)
##17264## by 0x4181D72: dlsym (in /lib/libdl-2.8.so)
##17264## by 0x8119155: R_dlsym (Rdynload.c:796)
##17264## by 0x8119AC8: R_FindSymbol (Rdynload.c:845)
##17264## by 0x8176E9C: resolveNativeRoutine (dotcode.c:238)
##17264## by 0x817782A: do_dotCode (dotcode.c:1651)
##17264## by 0x819FC0A: Rf_eval (eval.c:495)
##17264## by 0x81A233A: do_begin (eval.c:1266)
##17264## by 0x819F9D0: Rf_eval (eval.c:469)
##17264## by 0x81A296A: do_for (eval.c:1154)
##17264## by 0x819F9D0: Rf_eval (eval.c:469)
##17264## by 0x81A233A: do_begin (eval.c:1266)
##17264## by 0x819F9D0: Rf_eval (eval.c:469)
##17264## by 0x81A36DB: Rf_applyClosure (eval.c:704)
##17264## by 0x819F923: Rf_eval (eval.c:513)
.C("SetParamHyperplane", m, hyper.superpos, hyper.maxlines,
hyper.mar.distr, hyper.mar.param, NAOK=TRUE, DUP=FALSE)
.C("SetExtremes", m, maxstable.maxGauss, PACKAGE="RandomFields", DUP=FALSE)
if (length(parameters)==0)
return(c(list(
PracticalRange=if (PracticalRange<=1)
as.logical(PracticalRange) else PracticalRange,
PrintLevel=PrintLevel,
pch=pch,
Storing=as.logical(Storing),
skipchecks = if (skipchecks<=1)
as.logical(skipchecks) else skipchecks,
stationary.only = (if(stationary.only==-1) NA else
as.logical(stationary.only)),
exactness =if(exactness==-1) NA else as.logical(exactness),
every = every,
CE.force=as.logical(CE.force),
CE.mmin=CE.mmin,
CE.strategy=CE.strategy,
CE.maxmem=CE.maxmem,
CE.tolIm=CE.tolIm,
CE.tolRe=CE.tolRe,
CE.trials=CE.trials,
CE.useprimes=as.logical(CE.useprimes),
CE.dependent=as.logical(CE.dependent),
CE.method=CE.method,
direct.bestvariables=direct.bestvariables,
direct.maxvariables=direct.maxvariables,
direct.method=direct.method,
direct.svdtolerance=direct.svdtolerance,
hyper.superpos=hyper.superpos,
hyper.maxlines=hyper.maxlines,
hyper.mar.distr=hyper.mar.distr,
hyper.mar.param=hyper.mar.param,
local.force=as.logical(local.force),
local.mmin=local.mmin,
local.maxmem=local.maxmem,
local.tolIm=local.tolIm,
local.tolRe=local.tolRe,
local.useprimes=as.logical(local.useprimes),
local.dependent=as.logical(local.dependent),
markov.neighbours = markov.neighbours,
markov.precision = markov.precision,
markov.cyclic = as.logical(markov.cyclic),
markov.maxmem = markov.maxmem,
maxstable.maxGauss=maxstable.maxGauss,
mpp.locations = mpp.locations,
mpp.intensity =mpp.intensity,
mpp.plus = mpp.plus,
mpp.relRadius = mpp.relRadius,
# mpp.scale =mpp.scale,
mpp.approxzero=mpp.approxzero,
mpp.samplingdist = mpp.samplingdist,
mpp.samplingr = mpp.samplingr,
mpp.p = mpp.p,
mpp.beta = mpp.beta,
nugget.tol=nugget.tol,
nugget.meth= as.logical(nugget.meth),
sequ.max = sequ.max,
sequ.back = sequ.back,
sequ.initial = sequ.initial,
spectral.lines=spectral.lines,
spectral.grid=as.logical(spectral.grid),
spectral.ergodic=as.logical(spectral.ergodic),
spectral.metro = as.logical(spectral.metro),
spectral.nmetro = spectral.nmetro,
spectral.sigma = spectral.sigma,
TBM.method=.methods[TBM.method+1],
TBM.center=TBM.center,
TBM.points=TBM.points,
TBM2.lines=TBM2.lines,
TBM2.linesimufactor=TBM2.linesimufactor,
TBM2.linesimustep=TBM2.linesimustep,
TBM2.layers=if (TBM2.layers==0 || TBM2.layers==1)
as.logical(TBM2.layers) else TBM2.layers,
# TBM2.num=as.logical(TBM2.num),
TBM3.lines=TBM3.lines,
TBM3.linesimufactor=TBM3.linesimufactor,
TBM3.linesimustep=TBM3.linesimustep,
TBM3.layers=if (TBM3.layers==0 || TBM3.layers==1)
as.logical(TBM3.layers) else TBM3.layers,
TBMCE.force=as.logical(TBMCE.force),
TBMCE.mmin=TBMCE.mmin,
TBMCE.strategy=TBMCE.strategy,
TBMCE.maxmem = TBMCE.maxmem,
TBMCE.tolIm=TBMCE.tolIm,
TBMCE.tolRe=TBMCE.tolRe,
TBMCE.trials=TBMCE.trials,
TBMCE.useprimes=as.logical(TBMCE.useprimes),
TBMCE.dependent=as.logical(TBMCE.dependent)
),
if (!no.readonly)
list(
covmaxchar=.p$covmaxchar,
covnr=.p$covnr,
distrmaxchar=.p$distrmaxchar,
distrnr=.p$distrnr,
maxdim=.p$maxdim,
maxmodels=.p$maxmodels,
methodmaxchar=.p$methodmaxchar,
methodnr=.p$methodnr
)
)
)
if (m==1) return(invisible(parameters))
orig.name <- names(parameters)
if (is.null(orig.name) || (orig.name[1]=="")) {
txt <- "either a single unnamed list must be given or the parameters should be referenced by names"
if ((length(parameters)!=1)) stop(txt)
parameters <- parameters[[1]]
orig.name <- names(parameters)
if ((length(parameters) != sum(orig.name!="") ||
(length(parameters)==0))) stop(txt)
}
name <- arg.list[pmatch(orig.name, arg.list)]
if (any(is.na(name)))
stop("the following parameter(s) could not be matched: ",
paste(orig.name[is.na(name)], collapse=", "))
names(parameters) <- name
for (i in 1:length(parameters)) {
type <- storage.mode(get(name[i]))
## parameters[i] is not sufficient since user give expression,
## which have type "language"
v <- parameters[[i]]
if (name[i]=="TBM.method" && !is.integer(v)) v <- pmatch(v, .methods) - 1
if (name[i]=="stationary.only" && is.na(v)) v <- -1
if (name[i]=="exactness" && is.na(v)) v <- -1
if (switch(type,
character = !is.character(v),
integer = !is.finite(v) || (v != as.integer(v)),
double = !is.numeric(v)))
stop("`", orig.name[i], "' is not ", type)
len <- length(get(name[i]))
if (length(v) > len)
stop("`", orig.name[i], "' is a too long vector")
assign(name[i], rep(v, length=len))
eval(parse(text=paste("storage.mode(",name[i],") <- type")))
}
stopifnot(PracticalRange %in% c(FALSE, TRUE, 2, 3, 11, 12, 13, 999))
}
}
PrintModelList <-function (operators=FALSE, internal=FALSE) {
stopifnot(internal <=2, internal >=0, operators<=1, operators>=0)
.C("PrintModelList", as.integer(internal), as.integer(operators),
PACKAGE="RandomFields")
invisible()
}
PrintMethodList <-function () {
.C("PrintMethods", PACKAGE="RandomFields")
invisible()
}
parampositions <- function(model, param, trend=NULL, dim, print=1) {
stopifnot(!missing(dim))
if (!is.null(trend)) stop("trend not programmed yet")
model <- PrepareModel(model, param, trend=trend, nugget.remove=FALSE)
.Call("GetNAPositions", model$model, as.integer(dim), as.integer(dim),
FALSE, as.integer(print), PACKAGE="RandomFields")
}
.distInt <- function(x) {
##
## only for gene data where each coordinates takes
## only three neighboured integer values !
stopifnot(is.matrix(x), is.integer(x))
n <- nrow(x)
genes <- ncol(x)
res <- double(n * n)
.C("distInt", t(x), n, genes, res, PACKAGE="RandomFields", DUP=FALSE)
dim(res) <- rep(n, 2)
res
}
parameter.range <- function(model, param, dim=1){
cat("sorry not programmed yet\n")
return(NULL)
pm <- PrepareModel(model=model, param=param, nugget.remove=FALSE)
storage.mode(dim) <- "integer"
ResGet <- .Call("MLEGetModelInfo", pm$model, dim, dim,
PACKAGE="RandomFields")
minmax <- ResGet$minmax[, 1:2]
dimnames(minmax) <- list(attr(ResGet$minmax, "row.names"), c("min", "max"))
return(minmax)
}