cu.ucl: Censored UCLs
In ebailey78/rucl: Upper Confidence Limit (UCL) functions for R
Usage
1
cu.ucl(x, d, confidence = as.numeric(Sys.getenv("rucl.confidence")), N = as.numeric(Sys.getenv("rucl.N")))
Arguments
x
d
confidence
N
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (x, d, confidence = as.numeric(Sys.getenv("rucl.confidence")),
N = as.numeric(Sys.getenv("rucl.N")))
{
data.name <- deparse(substitute(x))
i <- !is.na(x)
x <- x[i]
d <- d[i]
n <- length(x)
dx <- x[d]
dy <- log(dx)
dn <- length(dx)
detect.dist <- suppressWarnings(dist.test(dx))
r <- ros(x, d)
d.mle <- gamma.mle(dx)
r.mle <- gamma.mle(r$gamma)
dl2n <- x
dl2n[!d] <- dl2n[!d]/2
dl2l <- log(dl2n)
dl1 <- x
dl1[dl1 <= max(x[!d])] = max(x[!d])
dl1d <- dl1 != max(x[!d])
km <- ple(x, d)
km.boot <- cu.bootstrap(x, d, N)
ln.ros.bs <- uu.bootstrap(r$ln, N, func = function(x, n,
...) {
sum(x)/length(x)
})
ss.desc <- list("Sample Size", "# of Distinct Detections",
"# of Detections", "# of Non-Detects", "Detection Rate",
"Minimum Detection", "Maximum Detection", "Arith. Mean of Detections",
"SD of Detections", "Arith. Mean using 1/2 DL", "SD using 1/2 DL",
"SE using 1/2 DL", "Normal ROS Mean", "Normal ROS SD",
"Normal ROS SE", "Kaplan-Meier Mean", "Kaplan-Meier SD",
"Kaplan-Meier SE", "Minimum of Log Detections", "Maximum of Log Detections",
"Mean of Log Detections", "SD of Log Detections", "Mean of Log Data using 1/2 DL",
"SD of Log Data using 1/2 DL", "SE of Log Data using 1/2 DL",
"Log ROS-based Mean - Log", "Log ROS-based SD - Log",
"Log ROS-based Mean", "Log ROS-based SD", "Log ROS-based SE",
"MLE of Gamma Shape - Detects", "MLE of Gamma Scale - Detects",
"MLE of Gamma Rate - Detects", "MLE of Gamma Mean - ROS",
"MLE of Gamma Shape - ROS", "MLE of Gamma Scale - ROS",
"MLE of Gamma Rate - ROS")
ss <- list(n = n, distinct.n = length(unique(dx)), detect.n = dn,
nondetect.n = n - dn, detect.rate = dn/n, detect.min = min(dx),
detect.max = max(dx), detect.mean = sum(dx)/dn, detect.sd = sqrt(sum((dx -
(sum(dx)/dn))^2)/(dn - 1)), halfdl.mean = sum(dl2n)/n,
halfdl.sd = sqrt(sum((dl2n - (sum(dl2n)/n))^2)/(n - 1)),
halfdl.se = sqrt(sum((dl2n - (sum(dl2n)/n))^2)/(n - 1))/sqrt(n),
norm.ros.mean = sum(r$norm)/n, norm.ros.sd = sqrt(sum((r$norm -
(sum(r$norm)/n))^2)/(n - 1)), norm.ros.se = sqrt(sum((r$norm -
(sum(r$norm)/n))^2)/(n - 1))/sqrt(n), km.mean = km$mean,
km.sd = km$se * sqrt(n), km.se = km$se, ln.detect.min = min(dy),
ln.detect.max = max(dy), ln.detect.mean = sum(dy)/dn,
ln.detect.sd = sqrt(sum((dy - (sum(dy)/dn))^2)/(dn -
1)), ln.halfdl.mean = sum(dl2l)/n, ln.halfdl.sd = sqrt(sum((dl2l -
(sum(dl2l)/n))^2)/(n - 1)), ln.halfdl.se = sqrt(sum((dl2l -
(sum(dl2l)/n))^2)/(n - 1))/sqrt(n), ln.ros.mean.log = sum(log(r$ln))/n,
ln.ros.sd.log = sd(log(r$ln)), ln.ros.mean = sum(r$ln)/n,
ln.ros.sd = sqrt(sum((r$ln - (sum(r$ln)/n))^2)/(n - 1)),
ln.ros.se = sqrt(sum((r$ln - (sum(r$ln)/n))^2)/(n - 1))/sqrt(n),
g.detect.shape = d.mle$shape, g.detect.scale = d.mle$scale,
g.detect.rate = d.mle$rate, g.ros.mean = sum(r$gamma)/n,
g.ros.shape = r.mle$shape, g.ros.scale = r.mle$scale,
g.ros.rate = r.mle$scale)
ucl.desc <- list("Student's t UCL (1/2 DL)", "Student's t UCL (Normal ROS)",
"CLT UCL (Normal ROS)", "Land's H UCL (1/2 DL)", "Student's t UCL (LN ROS)",
"Percentile Bootstrap UCL (LN ROS)", "BCA Bootstrap (LN ROS)",
"Land's H UCL (LN ROS)", "Approximate Gamma UCL (Gamma ROS)",
"Adjusted Gamma UCL (Gamma ROS)", "Student's t UCL (KM)",
"CLT UCL (KM)", "Bootstrap-t UCL (KM)", "BCA Percentile Bootstrap UCL (KM)",
"Percentile Bootstrap UCL (KM)", "95% Chebyshev UCL (KM)",
"97.5% Chebyshev UCL (KM)", "99% Chebyshev UCL (KM)")
ucls <- list(n.halfdl.t = b.tucl(ss$halfdl.mean, ss$halfdl.se,
n, confidence), n.ros.t = b.tucl(ss$norm.ros.mean, ss$norm.ros.se,
n, confidence), n.ros.z = b.zucl(ss$norm.ros.mean, ss$norm.ros.se,
confidence), ln.halfdl.h = b.hucl(ss$ln.halfdl.mean,
ss$ln.halfdl.sd, n, confidence), ln.ros.t = b.tucl(ss$ln.ros.mean,
ss$ln.ros.se, n, confidence), ln.ros.pboot = b.pboot(ln.ros.bs,
confidence), ln.ros.bcaboot = uu.bcaboot(ln.ros.bs, r$ln,
ss$ln.ros.mean, n, confidence, N), ln.ros.h = b.hucl(ss$ln.ros.mean.log,
ss$ln.ros.sd.log, n, confidence), g.ros.appgamma = b.appgamma(ss$g.ros.mean,
ss$g.ros.shape, n, 1 - confidence), g.ros.adjgamma = b.adjgamma(ss$g.ros.mean,
ss$g.ros.shape, n, 1 - confidence), o.km.t = b.tucl(km$mean,
km$se, n, confidence), o.km.z = b.zucl(km$mean, km$se,
confidence), o.km.tboot = cu.tboot(x, d, km$mean, km$se,
confidence, N), o.km.bcaboot = cu.bcaboot(km.boot, x,
d, km$mean, n, confidence, N), o.km.pboot = b.pboot(km.boot,
confidence), o.km.cheb95 = b.cheb(km$mean, km$se, 0.05),
o.km.cheb975 = b.cheb(km$mean, km$se, 0.025), o.km.cheb99 = b.cheb(km$mean,
km$se, 0.01))
rec.name <- uc.pick(detect.dist$Recommend, ss$ln.detect.sd,
ss$g.detect.shape, n, ss$detect.rate)
rec.desc <- ucl.desc[names(ucls) %in% rec.name]
rec <- ucls[rec.name]
o <- list(type = "censored", confidence = confidence, N = N,
data = x, detects = d, data.name = data.name, summary.statistics = ss,
ss.desc = ss.desc, distribution.tests = detect.dist,
ros = r, ucls = ucls, ucl.desc = ucl.desc, rec = rec,
rec.desc = rec.desc)
class(o) <- "rucl"
o
}
ebailey78/rucl documentation built on May 15, 2019, 7:29 p.m.
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Usage
1 | cu.ucl(x, d, confidence = as.numeric(Sys.getenv("rucl.confidence")), N = as.numeric(Sys.getenv("rucl.N")))
|
Arguments
x |
|
d |
|
confidence |
|
N |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (x, d, confidence = as.numeric(Sys.getenv("rucl.confidence")),
N = as.numeric(Sys.getenv("rucl.N")))
{
data.name <- deparse(substitute(x))
i <- !is.na(x)
x <- x[i]
d <- d[i]
n <- length(x)
dx <- x[d]
dy <- log(dx)
dn <- length(dx)
detect.dist <- suppressWarnings(dist.test(dx))
r <- ros(x, d)
d.mle <- gamma.mle(dx)
r.mle <- gamma.mle(r$gamma)
dl2n <- x
dl2n[!d] <- dl2n[!d]/2
dl2l <- log(dl2n)
dl1 <- x
dl1[dl1 <= max(x[!d])] = max(x[!d])
dl1d <- dl1 != max(x[!d])
km <- ple(x, d)
km.boot <- cu.bootstrap(x, d, N)
ln.ros.bs <- uu.bootstrap(r$ln, N, func = function(x, n,
...) {
sum(x)/length(x)
})
ss.desc <- list("Sample Size", "# of Distinct Detections",
"# of Detections", "# of Non-Detects", "Detection Rate",
"Minimum Detection", "Maximum Detection", "Arith. Mean of Detections",
"SD of Detections", "Arith. Mean using 1/2 DL", "SD using 1/2 DL",
"SE using 1/2 DL", "Normal ROS Mean", "Normal ROS SD",
"Normal ROS SE", "Kaplan-Meier Mean", "Kaplan-Meier SD",
"Kaplan-Meier SE", "Minimum of Log Detections", "Maximum of Log Detections",
"Mean of Log Detections", "SD of Log Detections", "Mean of Log Data using 1/2 DL",
"SD of Log Data using 1/2 DL", "SE of Log Data using 1/2 DL",
"Log ROS-based Mean - Log", "Log ROS-based SD - Log",
"Log ROS-based Mean", "Log ROS-based SD", "Log ROS-based SE",
"MLE of Gamma Shape - Detects", "MLE of Gamma Scale - Detects",
"MLE of Gamma Rate - Detects", "MLE of Gamma Mean - ROS",
"MLE of Gamma Shape - ROS", "MLE of Gamma Scale - ROS",
"MLE of Gamma Rate - ROS")
ss <- list(n = n, distinct.n = length(unique(dx)), detect.n = dn,
nondetect.n = n - dn, detect.rate = dn/n, detect.min = min(dx),
detect.max = max(dx), detect.mean = sum(dx)/dn, detect.sd = sqrt(sum((dx -
(sum(dx)/dn))^2)/(dn - 1)), halfdl.mean = sum(dl2n)/n,
halfdl.sd = sqrt(sum((dl2n - (sum(dl2n)/n))^2)/(n - 1)),
halfdl.se = sqrt(sum((dl2n - (sum(dl2n)/n))^2)/(n - 1))/sqrt(n),
norm.ros.mean = sum(r$norm)/n, norm.ros.sd = sqrt(sum((r$norm -
(sum(r$norm)/n))^2)/(n - 1)), norm.ros.se = sqrt(sum((r$norm -
(sum(r$norm)/n))^2)/(n - 1))/sqrt(n), km.mean = km$mean,
km.sd = km$se * sqrt(n), km.se = km$se, ln.detect.min = min(dy),
ln.detect.max = max(dy), ln.detect.mean = sum(dy)/dn,
ln.detect.sd = sqrt(sum((dy - (sum(dy)/dn))^2)/(dn -
1)), ln.halfdl.mean = sum(dl2l)/n, ln.halfdl.sd = sqrt(sum((dl2l -
(sum(dl2l)/n))^2)/(n - 1)), ln.halfdl.se = sqrt(sum((dl2l -
(sum(dl2l)/n))^2)/(n - 1))/sqrt(n), ln.ros.mean.log = sum(log(r$ln))/n,
ln.ros.sd.log = sd(log(r$ln)), ln.ros.mean = sum(r$ln)/n,
ln.ros.sd = sqrt(sum((r$ln - (sum(r$ln)/n))^2)/(n - 1)),
ln.ros.se = sqrt(sum((r$ln - (sum(r$ln)/n))^2)/(n - 1))/sqrt(n),
g.detect.shape = d.mle$shape, g.detect.scale = d.mle$scale,
g.detect.rate = d.mle$rate, g.ros.mean = sum(r$gamma)/n,
g.ros.shape = r.mle$shape, g.ros.scale = r.mle$scale,
g.ros.rate = r.mle$scale)
ucl.desc <- list("Student's t UCL (1/2 DL)", "Student's t UCL (Normal ROS)",
"CLT UCL (Normal ROS)", "Land's H UCL (1/2 DL)", "Student's t UCL (LN ROS)",
"Percentile Bootstrap UCL (LN ROS)", "BCA Bootstrap (LN ROS)",
"Land's H UCL (LN ROS)", "Approximate Gamma UCL (Gamma ROS)",
"Adjusted Gamma UCL (Gamma ROS)", "Student's t UCL (KM)",
"CLT UCL (KM)", "Bootstrap-t UCL (KM)", "BCA Percentile Bootstrap UCL (KM)",
"Percentile Bootstrap UCL (KM)", "95% Chebyshev UCL (KM)",
"97.5% Chebyshev UCL (KM)", "99% Chebyshev UCL (KM)")
ucls <- list(n.halfdl.t = b.tucl(ss$halfdl.mean, ss$halfdl.se,
n, confidence), n.ros.t = b.tucl(ss$norm.ros.mean, ss$norm.ros.se,
n, confidence), n.ros.z = b.zucl(ss$norm.ros.mean, ss$norm.ros.se,
confidence), ln.halfdl.h = b.hucl(ss$ln.halfdl.mean,
ss$ln.halfdl.sd, n, confidence), ln.ros.t = b.tucl(ss$ln.ros.mean,
ss$ln.ros.se, n, confidence), ln.ros.pboot = b.pboot(ln.ros.bs,
confidence), ln.ros.bcaboot = uu.bcaboot(ln.ros.bs, r$ln,
ss$ln.ros.mean, n, confidence, N), ln.ros.h = b.hucl(ss$ln.ros.mean.log,
ss$ln.ros.sd.log, n, confidence), g.ros.appgamma = b.appgamma(ss$g.ros.mean,
ss$g.ros.shape, n, 1 - confidence), g.ros.adjgamma = b.adjgamma(ss$g.ros.mean,
ss$g.ros.shape, n, 1 - confidence), o.km.t = b.tucl(km$mean,
km$se, n, confidence), o.km.z = b.zucl(km$mean, km$se,
confidence), o.km.tboot = cu.tboot(x, d, km$mean, km$se,
confidence, N), o.km.bcaboot = cu.bcaboot(km.boot, x,
d, km$mean, n, confidence, N), o.km.pboot = b.pboot(km.boot,
confidence), o.km.cheb95 = b.cheb(km$mean, km$se, 0.05),
o.km.cheb975 = b.cheb(km$mean, km$se, 0.025), o.km.cheb99 = b.cheb(km$mean,
km$se, 0.01))
rec.name <- uc.pick(detect.dist$Recommend, ss$ln.detect.sd,
ss$g.detect.shape, n, ss$detect.rate)
rec.desc <- ucl.desc[names(ucls) %in% rec.name]
rec <- ucls[rec.name]
o <- list(type = "censored", confidence = confidence, N = N,
data = x, detects = d, data.name = data.name, summary.statistics = ss,
ss.desc = ss.desc, distribution.tests = detect.dist,
ros = r, ucls = ucls, ucl.desc = ucl.desc, rec = rec,
rec.desc = rec.desc)
class(o) <- "rucl"
o
}
|
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