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#
# progress.R
#
# $Revision: 1.21 $ $Date: 2016/04/25 02:34:40 $
#
# progress plots (envelope representations)
#
dclf.progress <- function(X, ...)
mctest.progress(X, ..., exponent=2)
mad.progress <- function(X, ...)
mctest.progress(X, ..., exponent=Inf)
mctest.progress <- local({
smoothquantile <- function(z, alpha) {
min(quantile(density(z), 1-alpha), max(z))
}
silentmax <- function(z) {
if(all(is.nan(z))) return(NaN)
z <- z[is.finite(z)]
if(length(z) == 0) return(NA) else return(max(z))
}
mctest.progress <- function(X, fun=Lest, ...,
exponent=1, nrank=1, interpolate=FALSE,
alpha, rmin=0) {
check.1.real(exponent)
explain.ifnot(exponent >= 0)
if(missing(fun) && inherits(X, "envelope"))
fun <- NULL
Z <- envelopeProgressData(X, fun=fun, ..., rmin=rmin, exponent=exponent)
R <- Z$R
devdata <- Z$devdata
devsim <- Z$devsim
nsim <- ncol(devsim)
# determine 'alpha' and 'nrank'
if(missing(alpha)) {
if((nrank %% 1) != 0)
stop("nrank must be an integer")
alpha <- nrank/(nsim + 1)
} else {
check.1.real(alpha)
stopifnot(alpha > 0 && alpha < 1)
if(!interpolate) {
if(!missing(nrank))
warning("nrank was ignored because alpha was given", call.=FALSE)
nrank <- alpha * (nsim + 1)
if(abs(nrank - round(nrank)) > 1e-2)
stop("alpha should be a multiple of 1/(nsim + 1)", call.=FALSE)
nrank <- as.integer(round(nrank))
}
}
alphastring <- paste(100 * alpha, "%%", sep="")
# compute critical values
critval <-
if(interpolate) apply(devsim, 1, smoothquantile, alpha=alpha) else
if(nrank == 1) apply(devsim, 1, silentmax) else
apply(devsim, 1, orderstats, k=nrank, decreasing=TRUE)
# create fv object
fname <- if(is.infinite(exponent)) "mad" else
if(exponent == 2) "T" else paste("D[",exponent,"]", sep="")
ylab <- if(is.infinite(exponent)) quote(mad(R)) else
if(exponent == 2) quote(T(R)) else
eval(substitute(quote(D[p](R)), list(p=exponent)))
df <- data.frame(R=R, obs=devdata, crit=critval, zero=0)
mcname <- if(interpolate) "interpolated Monte Carlo" else "Monte Carlo"
p <- fv(df,
argu="R", ylab=ylab, valu="obs", fmla = . ~ R,
desc = c("Interval endpoint R",
"observed value of test statistic %s",
paste(mcname, alphastring, "critical value for %s"),
"zero"),
labl=c("R", "%s(R)", "%s[crit](R)", "0"),
unitname = unitname(X), fname = fname)
fvnames(p, ".") <- c("obs", "crit", "zero")
fvnames(p, ".s") <- c("zero", "crit")
p <- hasenvelope(p, Z$envelope) # envelope may be NULL
return(p)
}
mctest.progress
})
# Do not call this function.
# Performs underlying computations
envelopeProgressData <- local({
envelopeProgressData <-
function(X, fun=Lest, ..., exponent=1,
alternative=c("two.sided", "less", "greater"),
leaveout=1, scale=NULL, clamp=FALSE,
normalize=FALSE, deflate=FALSE,
rmin=0,
save.envelope = savefuns || savepatterns,
savefuns = FALSE,
savepatterns = FALSE) {
alternative <- match.arg(alternative)
if(!(leaveout %in% 0:2))
stop("Argument leaveout should equal 0, 1 or 2")
## compute or extract simulated functions
X <- envelope(X, fun=fun, ..., alternative=alternative,
savefuns=TRUE, savepatterns=savepatterns)
Y <- attr(X, "simfuns")
## extract values
R <- with(X, .x)
obs <- with(X, .y)
sim <- as.matrix(as.data.frame(Y))[, -1]
nsim <- ncol(sim)
## choose function as reference
has.theo <- ("theo" %in% names(X))
use.theo <- identical(attr(X, "einfo")$use.theory, TRUE)
if(use.theo && !has.theo)
warning("No theoretical function available; use.theory ignored")
if(use.theo && has.theo) {
# theo.used <- TRUE
reference <- with(X, theo)
leaveout <- 0
} else {
# theo.used <- FALSE
if(leaveout == 2) {
## use sample mean of simulations only
reference <- with(X, mmean)
} else {
## use sample mean of simulations *and* observed
reference <- (nsim * with(X, mmean) + obs)/(nsim + 1)
}
}
## restrict range
if(rmin > 0) {
if(sum(R >= rmin) < 2)
stop("rmin is too large for the available range of r values")
nskip <- sum(R < rmin)
} else nskip <- 0
## determine rescaling if any
if(is.null(scale)) {
scaling <- NULL
scr <- 1
} else if(is.function(scale)) {
scaling <- scale(R)
sname <- "scale(r)"
ans <- check.nvector(scaling, length(R), things="values of r",
fatal=FALSE, vname=sname)
if(!ans)
stop(attr(ans, "whinge"), call.=FALSE)
if(any(bad <- (scaling <= 0))) {
## issue a warning unless this only happens at r=0
if(any(bad[R > 0]))
warning(paste("Some values of", sname, "were negative or zero:",
"scale was reset to 1 for these values"),
call.=FALSE)
scaling[bad] <- 1
}
scr <- scaling
} else stop("Argument scale should be a function")
## compute deviations
rawdevDat <- Deviation(obs, reference, leaveout, nsim, sim[,1])
rawdevSim <- Deviation(sim, reference, leaveout, nsim)
## evaluate signed/absolute deviation relevant to alternative
ddat <- RelevantDeviation(rawdevDat, alternative, clamp, scaling)
dsim <- RelevantDeviation(rawdevSim, alternative, clamp, scaling)
## compute test statistics
if(is.infinite(exponent)) {
## MAD
devdata <- cummaxskip(ddat, nskip)
devsim <- apply(dsim, 2, cummaxskip, nskip=nskip)
if(deflate) {
devdata <- scr * devdata
devsim <- scr * devsim
}
testname <- "Maximum absolute deviation test"
} else {
dR <- c(0, diff(R))
if(clamp || (alternative == "two.sided")) {
## deviations are nonnegative
devdata <- cumsumskip(dR * ddat^exponent, nskip)
devsim <- apply(dR * dsim^exponent, 2, cumsumskip, nskip=nskip)
} else {
## sign of deviations should be retained
devdata <- cumsumskip(dR * sign(ddat) * abs(ddat)^exponent,
nskip=nskip)
devsim <- apply(dR * sign(dsim) * abs(dsim)^exponent,
2, cumsumskip, nskip=nskip)
}
if(normalize) {
devdata <- devdata/R
devsim <- sweep(devsim, 1, R, "/")
}
if(deflate) {
devdata <- scr * sign(devdata) * abs(devdata)^(1/exponent)
devsim <- scr * sign(devsim) * abs(devsim)^(1/exponent)
}
testname <- if(exponent == 2) "Diggle-Cressie-Loosmore-Ford test" else
if(exponent == 1) "Integral absolute deviation test" else
paste("Integrated", ordinal(exponent), "Power Deviation test")
}
result <- list(R=R, devdata=devdata, devsim=devsim, testname=testname,
scaleR=scr, clamp=clamp)
if(save.envelope)
result$envelope <- X
return(result)
}
cumsumskip <- function(x, nskip=0) {
if(nskip == 0) cumsum(x) else c(rep(NA, nskip), cumsum(x[-seq_len(nskip)]))
}
cummaxskip <- function(x, nskip=0) {
if(nskip == 0) cummax(x) else c(rep(NA, nskip), cummax(x[-seq_len(nskip)]))
}
envelopeProgressData
})
dg.progress <- function(X, fun=Lest, ...,
exponent=2, nsim=19, nsimsub=nsim-1, nrank=1, alpha,
leaveout=1, interpolate=FALSE, rmin=0,
savefuns=FALSE, savepatterns=FALSE,
verbose=TRUE) {
env.here <- sys.frame(sys.nframe())
if(!missing(nsimsub) && !relatively.prime(nsim, nsimsub))
stop("nsim and nsimsub must be relatively prime")
## determine 'alpha' and 'nrank'
if(missing(alpha)) {
if((nrank %% 1) != 0)
stop("nrank must be an integer")
alpha <- nrank/(nsim + 1)
} else {
check.1.real(alpha)
stopifnot(alpha > 0 && alpha < 1)
if(!interpolate) {
if(!missing(nrank))
warning("nrank was ignored because alpha was given", call.=FALSE)
nrank <- alpha * (nsim + 1)
if(abs(nrank - round(nrank)) > 1e-2)
stop("alpha should be a multiple of 1/(nsim + 1)", call.=FALSE)
nrank <- as.integer(round(nrank))
}
}
if(verbose)
cat("Computing first-level test data...")
## generate or extract simulated patterns and functions
E <- envelope(X, fun=fun, ..., nsim=nsim,
savepatterns=TRUE, savefuns=TRUE,
verbose=FALSE,
envir.simul=env.here)
## get progress data
PD <- envelopeProgressData(E, fun=fun, ..., rmin=rmin, nsim=nsim,
exponent=exponent, leaveout=leaveout,
verbose=FALSE)
## get first level MC test significance trace
T1 <- mctest.sigtrace(E, fun=fun, nsim=nsim,
exponent=exponent,
leaveout=leaveout,
interpolate=interpolate, rmin=rmin,
confint=FALSE, verbose=FALSE, ...)
R <- T1$R
phat <- T1$pest
if(verbose) {
cat("Done.\nComputing second-level data... ")
state <- list()
}
## second level traces
simpat <- attr(E, "simpatterns")
phat2 <- matrix(, length(R), nsim)
for(j in seq_len(nsim)) {
simj <- simpat[[j]]
sigj <- mctest.sigtrace(simj,
fun=fun, nsim=nsimsub,
exponent=exponent,
interpolate=interpolate,
leaveout=leaveout,
rmin=rmin,
confint=FALSE, verbose=FALSE, ...)
phat2[,j] <- sigj$pest
if(verbose) state <- progressreport(j, nsim, state=state)
}
if(verbose) cat("Done.\n")
## Dao-Genton procedure
dgcritrank <- 1 + rowSums(phat > phat2)
dgcritrank <- pmin(dgcritrank, nsim)
devsim.sort <- t(apply(PD$devsim, 1, sort, decreasing=TRUE, na.last=TRUE))
ii <- cbind(seq_along(dgcritrank), dgcritrank)
devcrit <- devsim.sort[ii]
devdata <- PD$devdata
## create fv object
fname <- if(is.infinite(exponent)) "mad" else
if(exponent == 2) "T" else paste("D[",exponent,"]", sep="")
ylab <- if(is.infinite(exponent)) quote(mad(R)) else
if(exponent == 2) quote(T(R)) else
eval(substitute(quote(D[p](R)), list(p=exponent)))
df <- data.frame(R=R, obs=devdata, crit=devcrit, zero=0)
mcname <- if(interpolate) "interpolated Monte Carlo" else "Monte Carlo"
p <- fv(df,
argu="R", ylab=ylab, valu="obs", fmla = . ~ R,
desc = c("Interval endpoint R",
"observed value of test statistic %s",
paste(mcname, paste0(100 * alpha, "%%"), "critical value for %s"),
"zero"),
labl=c("R", "%s(R)", "%s[crit](R)", "0"),
unitname = unitname(X), fname = fname)
fvnames(p, ".") <- c("obs", "crit", "zero")
fvnames(p, ".s") <- c("zero", "crit")
if(savefuns || savepatterns)
p <- hasenvelope(p, E)
return(p)
}
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