tolIntLnormCensored.RdConstruct a \(\beta\)-content or \(\beta\)-expectation tolerance interval for a lognormal distribution based on Type I or Type II censored data.
tolIntLnormCensored(x, censored, censoring.side = "left", coverage = 0.95,
cov.type = "content", ti.type = "two-sided", conf.level = 0.95,
method = "mle", ti.method = "exact.for.complete", seed = NULL,
nmc = 1000)numeric vector of positive observations. Missing (NA), undefined (NaN), and
infinite (Inf, -Inf) values are allowed but will be removed.
numeric or logical vector indicating which values of x are censored. This must be the
same length as x. If the mode of censored is "logical", TRUE values
correspond to elements of x that are censored, and FALSE values correspond to
elements of x that are not censored. If the mode of censored is "numeric",
it must contain only 1's and 0's; 1 corresponds to TRUE and
0 corresponds to FALSE. Missing (NA) values are allowed but will be removed.
character string indicating on which side the censoring occurs. The possible values are
"left" (the default) and "right".
a scalar between 0 and 1 indicating the desired coverage of the tolerance interval.
The default value is coverage=0.95.
character string specifying the coverage type for the tolerance interval.
The possible values are "content" (\(\beta\)-content; the default), and
"expectation" (\(\beta\)-expectation). See the DETAILS section for more
information.
character string indicating what kind of tolerance interval to compute.
The possible values are "two-sided" (the default), "lower", and
"upper".
a scalar between 0 and 1 indicating the confidence level associated with the tolerance
interval. The default value is conf.level=0.95.
character string indicating the method to use for parameter estimation on the log-scale.
For singly censored data, possible values are "mle" (the default), "bcmle",
"qq.reg", "qq.reg.w.cen.level", "impute.w.qq.reg", "impute.w.qq.reg.w.cen.level", "impute.w.mle", "iterative.impute.w.qq.reg",
"m.est", and "half.cen.level". See the help file for enormCensored
for details.
For multiply censored data, possible values are "mle" (the default), "qq.reg",
"impute.w.qq.reg", and "half.cen.level". See the help file for enormCensored for details.
character string specifying the method for constructing the tolerance
interval. Possible values are: "exact.for.complete" (the default), "gpq" (Generalized Pivotal Quantity), and "wald.wolfowitz.for.complete" (only available for a two-sided tolerance interval,
i.e., when ti.type="two-sided").
See the DETAILS section for more information.
for the case when ti.method="gpq", a positive integer to pass to the
function gpqTolIntNormSinglyCensored or
gpqTolIntNormMultiplyCensored. This argument is
ignored if seed=NULL (the default). Using the seed
argument lets you reproduce the exact same result if all other
arguments stay the same.
for the case when ti.method="gpq", a positive integer \(\ge 10\)
indicating the number of Monte Carlo trials to run in order to compute the
GPQ(s).
A tolerance interval for a lognormal distribution is constructed by taking the
natural logarithm of the observations and constructing a tolerance interval
based on the normal (Gaussian) distribution by calling tolIntNormCensored.
These tolerance limits are then exponentiated to produce a tolerance interval on
the original scale of the data.
A list of class "estimateCensored" containing the estimated
parameters, the tolerance interval, and other information.
See estimateCensored.object for details.
Berthouex, P.M., and L.C. Brown. (2002). Statistics for Environmental Engineers. Lewis Publishers, Boca Raton.
Draper, N., and H. Smith. (1998). Applied Regression Analysis. Third Edition. John Wiley and Sons, New York.
Ellison, B.E. (1964). On Two-Sided Tolerance Intervals for a Normal Distribution. Annals of Mathematical Statistics 35, 762-772.
Gibbons, R.D., D.K. Bhaumik, and S. Aryal. (2009). Statistical Methods for Groundwater Monitoring, Second Edition. John Wiley & Sons, Hoboken.
Guttman, I. (1970). Statistical Tolerance Regions: Classical and Bayesian. Hafner Publishing Co., Darien, CT.
Hahn, G.J. (1970b). Statistical Intervals for a Normal Population, Part I: Tables, Examples and Applications. Journal of Quality Technology 2(3), 115-125.
Hahn, G.J. (1970c). Statistical Intervals for a Normal Population, Part II: Formulas, Assumptions, Some Derivations. Journal of Quality Technology 2(4), 195-206.
Hahn, G.J., and W.Q. Meeker. (1991). Statistical Intervals: A Guide for Practitioners. John Wiley and Sons, New York.
Krishnamoorthy K., and T. Mathew. (2009). Statistical Tolerance Regions: Theory, Applications, and Computation. John Wiley and Sons, Hoboken.
Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with S-PLUS. CRC Press, Boca Raton.
Odeh, R.E., and D.B. Owen. (1980). Tables for Normal Tolerance Limits, Sampling Plans, and Screening. Marcel Dekker, New York.
Owen, D.B. (1962). Handbook of Statistical Tables. Addison-Wesley, Reading, MA.
Singh, A., R. Maichle, and N. Armbya. (2010a). ProUCL Version 4.1.00 User Guide (Draft). EPA/600/R-07/041, May 2010. Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C.
Singh, A., N. Armbya, and A. Singh. (2010b). ProUCL Version 4.1.00 Technical Guide (Draft). EPA/600/R-07/041, May 2010. Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C.
USEPA. (2009). Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities, Unified Guidance. EPA 530/R-09-007, March 2009. Office of Resource Conservation and Recovery Program Implementation and Information Division. U.S. Environmental Protection Agency, Washington, D.C.
USEPA. (2010). Errata Sheet - March 2009 Unified Guidance. EPA 530/R-09-007a, August 9, 2010. Office of Resource Conservation and Recovery, Program Information and Implementation Division. U.S. Environmental Protection Agency, Washington, D.C.
Wald, A., and J. Wolfowitz. (1946). Tolerance Limits for a Normal Distribution. Annals of Mathematical Statistics 17, 208-215.
Tolerance intervals have long been applied to quality control and life testing problems (Hahn, 1970b,c; Hahn and Meeker, 1991; Krishnamoorthy and Mathew, 2009). References that discuss tolerance intervals in the context of environmental monitoring include: Berthouex and Brown (2002, Chapter 21), Gibbons et al. (2009), Millard and Neerchal (2001, Chapter 6), Singh et al. (2010b), and USEPA (2009).
# Generate 20 observations from a lognormal distribution with parameters
# mean=10 and cv=1, censor the observations less than 5,
# then create a one-sided upper tolerance interval with 90%
# coverage and 95% confidence based on these Type I left, singly
# censored data.
# (Note: the call to set.seed allows you to reproduce this example.)
set.seed(250)
dat <- rlnormAlt(20, mean = 10, cv = 1)
sort(dat)
#> [1] 2.608298 3.185459 4.196216 4.383764 4.569752 5.136130 5.209538
#> [8] 5.916284 6.199076 6.214755 6.255779 6.778361 7.074972 7.100494
#> [15] 8.930845 10.388766 11.402769 14.247062 14.559506 15.437340
# [1] 2.608298 3.185459 4.196216 4.383764 4.569752 5.136130
# [7] 5.209538 5.916284 6.199076 6.214755 6.255779 6.778361
#[13] 7.074972 7.100494 8.930845 10.388766 11.402769 14.247062
#[19] 14.559506 15.437340
censored <- dat < 5
dat[censored] <- 5
tolIntLnormCensored(dat, censored, coverage = 0.9, ti.type="upper")
#>
#> Results of Distribution Parameter Estimation
#> Based on Type I Censored Data
#> --------------------------------------------
#>
#> Assumed Distribution: Lognormal
#>
#> Censoring Side: left
#>
#> Censoring Level(s): 5
#>
#> Estimated Parameter(s): meanlog = 1.8993686
#> sdlog = 0.4804343
#>
#> Estimation Method: MLE
#>
#> Data: dat
#>
#> Censoring Variable: censored
#>
#> Sample Size: 20
#>
#> Percent Censored: 25%
#>
#> Assumed Sample Size: 20
#>
#> Tolerance Interval Coverage: 90%
#>
#> Coverage Type: content
#>
#> Tolerance Interval Method: Exact for
#> Complete Data
#>
#> Tolerance Interval Type: upper
#>
#> Confidence Level: 95%
#>
#> Tolerance Interval: LTL = 0.00000
#> UTL = 16.85556
#>
#Results of Distribution Parameter Estimation
#Based on Type I Censored Data
#--------------------------------------------
#
#Assumed Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 5
#
#Estimated Parameter(s): meanlog = 1.8993686
# sdlog = 0.4804343
#
#Estimation Method: MLE
#
#Data: dat
#
#Censoring Variable: censored
#
#Sample Size: 20
#
#Percent Censored: 25%
#
#Assumed Sample Size: 20
#
#Tolerance Interval Coverage: 90%
#
#Coverage Type: content
#
#Tolerance Interval Method: Exact for
# Complete Data
#
#Tolerance Interval Type: upper
#
#Confidence Level: 95%
#
#Tolerance Interval: LTL = 0.00000
# UTL = 16.85556
if (FALSE) { # \dontrun{
# Note: The true 90'th percentile is 20.55231
#---------------------------------------------
qlnormAlt(0.9, mean = 10, cv = 1)
#[1] 20.55231
# Compare the result using the method "gpq"
tolIntLnormCensored(dat, censored, coverage = 0.9, ti.type="upper",
ti.method = "gpq", seed = 432)$interval$limits
# LTL UTL
# 0.00000 17.85474
# Clean Up
#---------
rm(dat, censored)
#--------------------------------------------------------------
# Example 15-1 of USEPA (2009, p. 15-10) shows how to estimate
# the mean and standard deviation using log-transformed multiply
# left-censored manganese concentration data. Here we'll construct a
# 95
EPA.09.Ex.15.1.manganese.df
# Sample Well Manganese.Orig.ppb Manganese.ppb Censored
# 1 1 Well.1 <5 5.0 TRUE
# 2 2 Well.1 12.1 12.1 FALSE
# 3 3 Well.1 16.9 16.9 FALSE
# ...
# 23 3 Well.5 3.3 3.3 FALSE
# 24 4 Well.5 8.4 8.4 FALSE
# 25 5 Well.5 <2 2.0 TRUE
with(EPA.09.Ex.15.1.manganese.df,
tolIntLnormCensored(Manganese.ppb, Censored, coverage = 0.9,
ti.type = "upper"))
#Results of Distribution Parameter Estimation
#Based on Type I Censored Data
#--------------------------------------------
#
#Assumed Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 2 5
#
#Estimated Parameter(s): meanlog = 2.215905
# sdlog = 1.356291
#
#Estimation Method: MLE
#
#Data: Manganese.ppb
#
#Censoring Variable: censored
#
#Sample Size: 25
#
#Percent Censored: 24
#
#Assumed Sample Size: 25
#
#Tolerance Interval Coverage: 90
#
#Coverage Type: content
#
#Tolerance Interval Method: Exact for
# Complete Data
#
#Tolerance Interval Type: upper
#
#Confidence Level: 95
#
#Tolerance Interval: LTL = 0.0000
# UTL = 110.9305
} # }