fliplmoms | R Documentation |
This function flips the L-moments by a flip attribute within an L-moment object such as that returned by lmomsRCmark
. The function will attempt to identify the L-moment object and lmorph
as necessary, but this support is not guaranteed. The flipping process is used to support left-tail censoring using the right-tail censoring alogrithms of lmomco. The odd order (seq(3,n,by2)
) \lambda_r
and \tau_r
are negated. The mean \hat\lambda_1
is computed by subtracting the \lambda_1
from the lmom argument from the flip M: \hat\lambda_1 = M - \lambda_1
and the \tau_2
is subsequently adjusted by the new mean. This function is written to provide a convenient method to re-transform or back flip the L-moments computed by lmomsRCmark
. Detailed review of the example problem listed here is recommended.
fliplmoms(lmom, flip=NULL, checklmom=TRUE)
lmom |
A L-moment object created by |
flip |
|
checklmom |
Should the |
An R list
is returned that matches the structure of the lmom argument (unless an lmorph
was attempted). The structure is intended to match that coming from lmomsRCmark
.
W.H. Asquith
Wang, Dongliang, Hutson, A.D., and Miecznikowski, J.C., 2010, L-moment estimation for parametric survival models given censored data: Statistical Methodology, v. 7, no. 6, pp. 655–667.
Helsel, D.R., 2005, Nondetects and data analysis—Statistics for censored environmental data: Hoboken, New Jersey, John Wiley, 250 p.
lmomsRCmark
# Create some data with **multiple detection limits**
# This is a left-tail censoring problem, and flipping will be required.
fakedat1 <- rnorm(50, mean=16, sd=5)
fake1.left.censor.indicator <- fakedat1 < 14
fakedat1[fake1.left.censor.indicator] <- 14
fakedat2 <- rnorm(50, mean=16, sd=5)
fake2.left.censor.indicator <- fakedat2 < 10
fakedat2[fake2.left.censor.indicator] <- 10
# combine the data sets
fakedat <- c(fakedat1, fakedat2);
fake.left.censor.indicator <- c(fake1.left.censor.indicator,
fake2.left.censor.indicator)
ix <- order(fakedat)
fakedat <- fakedat[ix]
fake.left.censor.indicator <- fake.left.censor.indicator[ix]
lmr.usual <- lmoms(fakedat)
lmr.flipped <- lmomsRCmark(fakedat, flip=TRUE,
rcmark=fake.left.censor.indicator)
lmr.backflipped <- fliplmoms(lmr.flipped); # re-transform
pch <- as.numeric(fake.left.censor.indicator)*15 + 1
F <- nonexceeds()
plot(pp(fakedat), sort(fakedat), pch=pch,
xlab="NONEXCEEDANCE PROBABILITY", ylab="DATA VALUE")
lines(F, qlmomco(F, parnor(lmr.backflipped)), lwd=2)
lines(F, qlmomco(F, parnor(lmr.usual)), lty=2)
legend(0,20, c("Uncensored", "Left-tail censored"), pch=c(1,16))
# The solid line represented the Normal distribution fit by
# censoring indicator on the multiple left-tail detection limits.
## Not run:
# see example in pwmRC
H <- c(3,4,5,6,6,7,8,8,9,9,9,10,10,11,11,11,13,13,13,13,13,
17,19,19,25,29,33,42,42,51.9999,52,52,52)
# 51.9999 was really 52, a real (noncensored) data point.
flip <- 100
F <- flip - H #
RCpwm <- pwmRC(H, threshold=52)
lmorph(pwm2lmom(vec2pwm(RCpwm$Bbetas))) # OUTPUT1 STARTS HERE
LCpwm <- pwmLC(F, threshold=(flip - 52))
LClmr <- pwm2lmom(vec2pwm(LCpwm$Bbetas))
LClmr <- lmorph(LClmr)
#LClmr$flip <- 100; fliplmoms(LClmr) # would also work
fliplmoms(LClmr, flip=flip) # OUTPUT2 STARTS HERE
# The two outputs are the same showing how the flip argument works
## End(Not run)
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