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## Authors
## Martin Schlather, schlather@math.uni-mannheim.de
##
##
## Copyright (C) 2015 -- 2017 Martin Schlather
##
## This program is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License
## as published by the Free Software Foundation; either version 3
## of the License, or (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
print.RFratiotest <- function(x, ...) {
if (!is.null(x$simu.ratios)) {
## MC ratio test
cat("\nMonte Carlo likelihood ratio test",
"\n=================================",
"\nnull model:", rfConvertRMmodel2string(x$model.list$nullmodel),
"\nalt. model:", rfConvertRMmodel2string(x$model.list$alternative),
"\n",x$msg)
# } else if (is.null(x$model1.df) && is.null(x$loglik)) {
# ## wann wird diese if-Anweiung verwendet?
# print.default(t(as.matrix(x)))
} else {
cat("\nApprox. likelihood ratio test\n=============================\n")
if (is.null(x$model1.df)) {
cat("null model: df=", x$df[1], " loglik=", x$loglik[1], "\n", sep="")
cat("alt. model: df=", x$df[2], " loglik=", x$loglik[2], "\n", sep="")
cat("p=", x$p,"\n\n")
} else {
len <- length(x$model1.df)
for (i in 1:len) {
if (len > 1) cat("Test", i, "\n")
cat("Null model:", as.character(x$model1.name)[i], "\n")
cat("Alt. model:", as.character(x$model2.name)[i], "\n")
cat(as.character(x$txt[i]))
}
cat("\n")
}
}
}
mess <- function(alpha, p, df, nullmodelname=0, altmodelname=1:length(df)) {
if (is.na(p)) return("NA") else p <- formatC(p, digits=4)
nullmodelname <- if (is.numeric(nullmodelname)) paste("model", nullmodelname)
else paste("'", nullmodelname, "'", sep="")
altmodelname <- if (is.numeric(altmodelname)) paste("model", altmodelname)
else paste("'", altmodelname, "'", sep="")
if (missing(df)) ## simulated
paste("The p-value equals", p,
"and the hypothesis that the two models significantly differ at",
"the level alpha=", alpha, "is",
ifelse(p <= alpha, "accepted.", "rejected."),
"\n")
else if (missing(alpha))
paste("loglikelihood test: ", altmodelname,
" against ", nullmodelname, ": p=", p,
" (df=", df, ")\n", sep="")
else {
if (length(alpha) != 1) stop("alpha should be a scalar")
paste("loglikelihood test: ", altmodelname,
" against ", nullmodelname,
": p=", p,
" (df=", df, ")",
ifelse(p <= alpha, "Null hypothesis accepted.",
"Null hypothesis rejected."),
"\n",
sep="")
}
## paste(p, df) ## to do
}
approx_test <- function(modellist, alpha) {
n <- length(modellist)
loglik <- df <- numeric()
for (m in 1:n) {
if (is(modellist[[m]], "RF_fit")) {
df[m] <- modellist[[m]]$number.of.parameters
loglik[m] <- modellist[[m]]$ml$likelihood
} else if (is(modellist[[m]], "RFfit")) {
df[m] <- modellist[[m]]@number.of.parameters
loglik[m] <- modellist[[m]]["ml"]@likelihood
} else stop("wrong class ('", class(modellist), "') in approx_test.")
}
if (length(df) > 2 && (!missing(alpha) || length(alpha) > 1))
stop("'alpha' must be a scalar")
p <- pchisq(diff(loglik), diff(df), lower.tail = FALSE)
txt <- mess(alpha=alpha, p=p, df=diff(df))
return(invisible(list(df=df, loglik=loglik, p=p, txt=txt)))
}
approx_test_single <- function(model, method, alpha, modelinfo) {
if (is(model) == "RF_fit") {
submodels <- model$submodels
df <- model$number.of.parameters
loglik <- model[[method]]$likelihood
report <- model$report
p.proj <- model$p.proj
v.proj <- model$v.proj
x.proj <- model$x.proj
true.tsdim <- model$true.tsdim
true.vdim <- model$true.vdim
AIC <- model[[method]]$AIC
BIC <- model[[method]]$BIC
number.of.data <- model$number.of.data
if (missing(modelinfo)) modelinfo <- model$modelinfo
fixed <- model$fixed
fitted.model <- model[[method]]$model
} else { # "RFfit"
submodels <- model@submodels
df <- model@number.of.parameters
loglik <- model[method]@likelihood
report <- model@report
p.proj <- model@p.proj
v.proj <- model@v.proj
x.proj <- model@x.proj
true.tsdim <- model@true.tsdim
true.vdim <- model@true.vdim
AIC <- model[method]@AIC
BIC <- model[method]@BIC
number.of.data <- model@number.of.data
if (missing(modelinfo)) modelinfo <- model@modelinfo
fixed <- NULL
fitted.model <- PrepareModel2(model[method])## ok no params
}
if (!is.logical(x.proj) && length(x.proj) != true.tsdim) ## todo x.proj!=NULL streichen
stop("space-time projection can't be evaluated yet. Please contact author.")
nm <- rownames(modelinfo)
proj.txt <- (if (length(p.proj) == 0) "user's model" else
paste("(", paste(nm[p.proj], collapse=", "), sep=""))
if (length(fixed$zero) > 0)
proj.txt <- paste(proj.txt, ", ", sep="",
paste(nm[fixed$zero], "=0", collapse=", ", sep=""))
if (length(fixed$one) > 0)
proj.txt <- paste(proj.txt, ", ", sep="",
paste(nm[fixed$one], "=0", collapse=", "))
if (length(p.proj) > 0) proj.txt <- paste(proj.txt, ")", sep="")
if (length(submodels) > 0) {
sub.df <- sub.loglik <- sub.report <- sub.p.proj <-
sub.proj.txt <- sub.fixed <- NULL
for (i in 1:length(submodels)) {
ret <- approx_test_single(submodels[[i]], method, alpha, modelinfo)
sub.df <- c(sub.df, ret$df)
sub.loglik <- c(sub.loglik, ret$loglik)
sub.report <- c(sub.report, ret$report)
sub.fixed <- c(if (i > 1) sub.fixed, list(ret$fixed))
sub.p.proj <- c(if (i > 1) sub.p.proj, list(ret$p.proj))
sub.v.proj <- c(if (i > 1) sub.v.proj, list(ret$v.proj))
sub.proj.txt <- c(sub.proj.txt, ret$proj.txt)
sub.fitted.models <-
c(if (i > 1) sub.fitted.models, ret$fitted.model)
}
len <- length(sub.df)
i <- 1
result <- NULL
result.model <- list()
result.n <- 0
while(i <= len) {
result.n <- result.n + 1
j <- i
if (j > len) stop("Error. Please contact author")
while(j <= len && sub.report[i] == sub.report[j]) {
j <- j + 1;
}
j <- j - 1
tot.loglik <- sum(sub.loglik[i:j])
tot.df <- sum(sub.df[i:j])
tot.proj.txt <- paste(sub.proj.txt[i:j], collapse=" * ")
tot.p.proj <- sub.p.proj[i:j]
tot.v.proj <- sub.v.proj[i:j]
tot.models <- sub.fitted.models[i:j]
if (length(sub.fixed) == 0) {
tot.fixed.zero <- tot.fixed.one <- NULL
} else {
tot.fixed.zero <- unlist(lapply(sub.fixed[i:j], function(x) x$zero))
tot.fixed.one <- unlist(lapply(sub.fixed[i:j], function(x) x$one))
}
if (true.vdim != length(tot.v.proj[[1]])) {
aux.model <- list("+")
for (k in 1:length(tot.models)) {
aux.model[[k+1]] <-
list("M", M=diag(true.vdim)[, tot.v.proj[[k]], drop=FALSE],
tot.models[[k]])
}
result.model[[result.n]] <- aux.model
} else {
result.model[[result.n]] <- tot.models[[1]]
if (i!=j) stop("model mismatch. Please contact author")
}
## oder einfach nur AIC der submodels addieren
tot.AIC <- 2 * tot.df - 2 * tot.loglik
tot.BIC <- log(number.of.data) * tot.df - 2 * tot.loglik
subpproj <- unlist(tot.p.proj)
if (length(subpproj) == length(unique(subpproj))) {
delta.df <- df - tot.df
if (delta.df <= 0) stop("negative df -- please contact author")
p <- pchisq(2 * (loglik - tot.loglik), df=delta.df, lower.tail = FALSE)
} else {
delta.df <- p <- NA
}
result <-
rbind(result,
data.frame(model1.name = tot.proj.txt,
model1.loglik = tot.loglik,
model1.df = tot.df,
model1.AIC = tot.AIC,
model1.BIC = tot.BIC,
model1.zero = paste(nm[tot.fixed.zero], collapse=","),
model1.one = paste(nm[tot.fixed.one], collapse=","),
model2.name = proj.txt,
model2.loglik=loglik,
model2.df = df,
model2.AIC = AIC,
model2.BIC = BIC,
model2.zero = paste(nm[fixed$zero], collapse=","),
model2.one = paste(nm[fixed$one], collapse=","),
delta.df = delta.df,
p = p,
txt = mess(alpha=alpha, p=p, df=delta.df,
nullmodelname="Null model",
altmodelname="Alt. model")
)
)
i <- j + 1
}
return(list(df=df, loglik=loglik, report=report,
p.proj = p.proj, v.proj=v.proj, AIC=AIC, BIC=BIC,
fixed = fixed,
fitted.model=c(result.model, list(fitted.model)),
proj.txt=proj.txt, result = result))
} else {
return(list(df=df, loglik=loglik, report=report,
p.proj = p.proj, v.proj=v.proj, AIC=AIC, BIC=BIC,
fixed = fixed, fitted.model=list(fitted.model),
proj.txt = proj.txt,
result = data.frame(
model1.name = "",
model1.loglik = NA,
model1.df = -1,
model1.AIC = NA,
model1.BIC = NA,
model1.zero = "",
model1.one = "",
model2.name = proj.txt,
model2.loglik=loglik,
model2.df = df,
model2.AIC = AIC,
model2.BIC = BIC,
model2.zero = if (is.null(fixed)) "" else
paste(nm[fixed$zero], collapse=","),
model2.one = if (is.null(fixed)) "" else
paste(nm[fixed$one], collapse=","),
delta.df = NA,
p = NA,
txt = "NA"
)))
}
}
RFratiotest <-
function(nullmodel, alternative, ## no params as output of RFfit
x, y=NULL, z=NULL, T=NULL, grid=NULL, data,
alpha,
n = 5 / alpha, ## number of simulations to do
seed = 0,
lower=NULL, upper=NULL,
methods, # "reml", "rml1",
sub.methods,
## "internal" : name should not be changed; should always be last
## method!
optim.control=NULL,
users.guess=NULL,
distances=NULL, dim,
transform=NULL,
##type = c("Gauss", "BrownResnick", "Smith", "Schlather",
## "Poisson"),
...
) {
classes <- c("RF_fit", "RFfit")
RFoptOld <- internal.rfoptions(#general.modus_operandi="normal",
..., general.seed=NA)
on.exit(RFoptions(LIST=RFoptOld[[1]]))
RFopt <- RFoptOld[[2]]
printlevel <- RFopt$basic$printlevel
if (RFopt$general$modus_operandi == "neurotic")
stop("crossvalidation is not a precise method")
if ((!RFopt$fit$ratiotest_approx && (missing(alpha) || n < 1 / alpha)) ||
(!missing(alpha) && (alpha < 0 && alpha > 1)) )
stop("alpha is not given or outside [0,1] or to small")
if (class(nullmodel) %in% classes) {
if (!missing(alternative) && !(class(alternative) %in% classes))
stop("alternative model not of the class 'RFfit'")
if (!RFopt$fit$ratiotest_approx)
stop("for models of class 'RFfit' the parameter 'ratiotest_approx' must be'TRUE'")
if (missing(alternative)) {
ats <- approx_test_single(nullmodel, "ml", alpha)$result
ats <- ats[!is.na(ats$delta.df) ,
c("model1.name", "model1.loglik", "model1.df", "model1.zero",
"model1.one",
"model2.name", "model2.loglik", "model2.df", "model2.zero",
"model2.one",
"delta.df", "p", "txt"
), drop=FALSE]
class(ats) <- "RFratiotest"
if (RFopt$general$returncall)
attr(ats, "call") <- as.character(deparse(match.call()))
attr(ats, "coord_system") <- c(orig=RFopt$coords$coord_system,
model=RFopt$coords$new_coord_system)
return(ats)
} else {
ats <- approx_test(list(nullmodel, alternative), alpha)
class(ats) <- "RFratiotest"
if (RFopt$general$returncall)
attr(ats, "call") <- as.character(deparse(match.call()))
attr(ats, "coord_system") <- c(orig=RFopt$coords$coord_system,
model=RFopt$coords$new_coord_system)
return(ats)
}
} else if (missing(alternative) || (class(alternative) %in% classes))
stop("alternative model is not given or not of model type")
if (exists(".Random.seed")) {
old.seed <- .Random.seed
on.exit(.Random.seed <<- old.seed, add = TRUE)
}
if (!is.null(seed) && !is.na(seed)) set.seed(seed)
else if (!is.na(RFopt$basic$seed)) {
if (printlevel >= PL_IMPORTANT)
message("NOTE: 'RFratiotest' is performed with fixed random seed ",
RFopt$basic$seed,
".\nSet RFoptions(seed=NA) to make the seed arbitrary.")
set.seed(RFopt$basic$seed)
}
nullmodel <- PrepareModel2(nullmodel, ...) ## ok no params
alternative <- PrepareModel2(alternative, ...)## ok no params
Z <- UnifyData(x=x, y=y, z=z, T=T, grid=grid, data=data,
distances=distances, dim=dim, RFopt=RFopt)
values <- try(GetValuesAtNA(NAmodel=nullmodel, valuemodel=alternative,
# spatialdim=Z$spatialdim, Time=Z$has.time.comp,
# shortnamelength=3,
skipchecks=FALSE),
silent=TRUE)
remove("Z")
isSubmodel <- is.numeric(values) && all(is.na(values))
if (!isSubmodel && printlevel >= PL_IMPORTANT)
message("'nullmodel' cannot be automatically detected as being a nullmodel of 'alternative'")
model.list <- list(nullmodel=nullmodel, alternative=alternative)
data.fit <- list()
guess <- users.guess
for (m in 1:length(model.list)) {
data.fit[[m]] <-
RFfit(model.list[[m]], x=x, y=y, z=z, T=T, grid=grid, data=data,
lower=lower, upper=upper,
methods=methods,
sub.methods=sub.methods, optim.control=optim.control,
users.guess=guess,
distances=distances, dim=dim,
transform=transform,
..., spConform = FALSE)
guess <- if (isSubmodel) data.fit[[m]]$ml$model else NULL
}
if (RFopt$fit$ratiotest_approx) {
ats <- approx_test(data.fit)
class(ats) <- "RFratiotest"
if (RFopt$general$returncall)
attr(ats, "call") <- as.character(deparse(match.call()))
attr(ats, "coord_system") <- c(orig=RFopt$coords$coord_system,
model=RFopt$coords$new_coord_system)
return(ats)
}
model <- data.fit[[1]]$ml$model
data.ratio <- -diff(sapply(data.fit, function(x) x$ml$likelihood))
stopifnot(!isSubmodel || data.ratio <= 0) # should never appear
simu.n <- n - 1
ratio <- numeric(simu.n)
fit <- numeric(2)
Z <- UnifyData(x=x, y=y, z=z, T=T, grid=grid, data=data,
distances=distances, dim=dim, RFopt=RFopt)
if (length(Z$coord) > 1)
stop("multisets of data cannot be considered yet")
Coord <- Z$coord[[1]]
dist.given <- Coord$dist.given
newx <- if (!dist.given) Coord$x # lapply(Z$coord, function(x) x$x)
newT <- if (!dist.given) Coord$T # lapply(Z$coord, function(x) x$T)
pch <- if (RFopt$general$pch=="") "" else '@'
for (i in 1:simu.n) {
if (printlevel>=PL_SUBIMPORTANT)
cat("\n ", i, "th simulation out of", simu.n)
else cat(pch)
simu <- RFsimulate(model, x=newx, T=newT, grid=grid,
distances=if (dist.given) Coord, dim=dim, spC=FALSE)
guess <- users.guess
for (m in 1:length(model.list)) {
simufit <-
RFfit(model.list[[m]], x=newx, T=newT, grid=grid,
data=simu,
lower=lower, upper=upper,
methods=methods,
sub.methods=sub.methods, optim.control=optim.control,
users.guess=guess,
distances=if (dist.given) Coord, dim=dim,
transform=transform,
..., spConform=FALSE)
fit[m] <- simufit$ml$ml
guess <- if (isSubmodel) simufit$ml$model else NULL
}
ratio[i] <- -diff(fit)
stopifnot(!isSubmodel || ratio[i] <= 0)# should never appear
if (printlevel > PL_SUBIMPORTANT)
Print(c(data.ratio, ratio), fit, rank(c(data.ratio, ratio))[1])#
}
r <- rank(c(data.ratio, ratio))[1]
p <- r / n
msg <-
paste("\nThe likehood ratio test ranks the likelihood of the data on rank",
r, "among", simu.n, "simulations:", mess(alpha=alpha, p=p))
res <- list(p=p, n=n, data.ratio=data.ratio, simu.ratios=ratio,
data.fit=data.fit, msg=msg, model.list=model.list)
class(res) <- "RFratiotest"
if (RFopt$general$returncall)
attr(res, "call") <- as.character(deparse(match.call()))
attr(res, "coord_system") <- c(orig=RFopt$coords$coord_system,
model=RFopt$coords$new_coord_system)
return(res)
}
|
