predIntNparSimultaneousN {EnvStats}  R Documentation 
Compute the sample size necessary for a nonparametric simultaneous prediction interval to achieve a specified confidence level based on one of three possible rules: kofm, California, or Modified California. Observations are assumed to come from from a continuous distribution.
predIntNparSimultaneousN(n.median = 1, k = 1, m = 2, r = 1, rule = "k.of.m",
lpl.rank = ifelse(pi.type == "upper", 0, 1),
n.plus.one.minus.upl.rank = ifelse(pi.type == "lower", 0, 1), pi.type = "upper",
conf.level = 0.95, n.max = 5000, integrate.args.list = NULL, maxiter = 1000)
n.median 
vector of positive odd integers specifying the sample size associated with the
future medians. The default value is 
k 
for the 
m 
vector of positive integers specifying the maximum number of future observations (or
medians) on one future sampling “occasion”.
The default value is 
r 
vector of positive integers specifying the number of future sampling
“occasions”. The default value is 
rule 
character string specifying which rule to use. The possible values are

lpl.rank 
vector of positive integers indicating the rank of the order statistic to use for
the lower bound of the prediction interval. When 
n.plus.one.minus.upl.rank 
vector of positive integers related to the rank of the order statistic to use for
the upper bound of the prediction interval. A value of 
pi.type 
character string indicating what kind of prediction interval to compute.
The possible values are 
conf.level 
numeric vector of values between 0 and 1 indicating the confidence level
associated with the prediction interval. The default value is 
n.max 
numeric scalar indicating the maximum sample size to consider. This argument
is used in the search algorithm to determine the required sample size. The
default value is 
integrate.args.list 
list of arguments to supply to the 
maxiter 
positive integer indicating the maximum number of iterations to use in the

If the arguments k
, m
, r
, lpl.rank
, and
n.plus.one.minus.upl.rank
are not all the same length, they are replicated
to be the same length as the length of the longest argument.
The function predIntNparSimultaneousN
computes the required sample size
n
by solving Equation (8), (9), or (10) in the help file for
predIntNparSimultaneous
for n
, depending on the value of the
argument rule
.
Note that when rule="k.of.m"
and r=1
, this is equivalent to a
standard nonparametric prediction interval and you can use the function
predIntNparN
instead.
vector of positive integers indicating the required sample size(s) for the specified nonparametric simultaneous prediction interval(s).
See the help file for predIntNparSimultaneous
.
Steven P. Millard (EnvStats@ProbStatInfo.com)
See the help file for predIntNparSimultaneous
.
predIntNparSimultaneous
,
predIntNparSimultaneousConfLevel
,
plotPredIntNparSimultaneousDesign
,
predIntNparSimultaneousTestPower
,
predIntNpar
, tolIntNpar
.
# For the 1of2 rule, look at how the required sample size for a onesided
# upper simultaneous nonparametric prediction interval for r=20 future
# sampling occasions increases with increasing confidence level:
seq(0.5, 0.9, by = 0.1)
#[1] 0.5 0.6 0.7 0.8 0.9
predIntNparSimultaneousN(r = 20, conf.level = seq(0.5, 0.9, by = 0.1))
#[1] 4 5 7 10 17
#
# For the 1ofm rule, look at how the required sample size for a onesided
# upper simultaneous nonparametric prediction interval decreases with increasing
# number of future observations (m), given r=20 future sampling occasions:
predIntNparSimultaneousN(k = 1, m = 1:5, r = 20)
#[1] 380 26 11 7 5
#
# For the 1of3 rule, look at how the required sample size for a onesided
# upper simultaneous nonparametric prediction interval increases with number
# of future sampling occasions (r):
predIntNparSimultaneousN(k = 1, m = 3, r = c(5, 10, 15, 20))
#[1] 7 8 10 11
#
# For the 1of3 rule, look at how the required sample size for a onesided
# upper simultaneous nonparametric prediction interval increases as the rank
# of the upper prediction limit decreases, given r=20 future sampling occasions:
predIntNparSimultaneousN(k = 1, m = 3, r = 20, n.plus.one.minus.upl.rank = 1:5)
#[1] 11 19 26 34 41
#
# Compare the required sample size for r=20 future sampling occasions based
# on the 1of3 rule, the CA rule with m=3, and the Modified CA rule.
predIntNparSimultaneousN(k = 1, m = 3, r = 20, rule = "k.of.m")
#[1] 11
predIntNparSimultaneousN(m = 3, r = 20, rule = "CA")
#[1] 36
predIntNparSimultaneousN(r = 20, rule = "Modified.CA")
#[1] 15
#==========
# Example 195 of USEPA (2009, p. 1933) shows how to compute nonparametric upper
# simultaneous prediction limits for various rules based on trace mercury data (ppb)
# collected in the past year from a site with four background wells and 10 compliance
# wells (data for two of the compliance wells are shown in the guidance document).
# The facility must monitor the 10 compliance wells for five constituents
# (including mercury) annually.
# Here we will modify the example to compute the required number of background
# observations for two different sampling plans:
# 1) the 1of2 retesting plan for a median of order 3 using the background maximum and
# 2) the 1of4 plan on individual observations using the 3rd highest background value.
# The data for this example are stored in EPA.09.Ex.19.5.mercury.df.
# There are 10 compliance wells and we will monitor 5 different
# constituents at each well annually. For this example, USEPA (2009)
# recommends setting r to the product of the number of compliance wells and
# the number of evaluations per year.
# To determine the minimum confidence level we require for
# the simultaneous prediction interval, USEPA (2009) recommends
# setting the maximum allowed individual Type I Error level per constituent to:
# 1  (1  SWFPR)^(1 / Number of Constituents)
# which translates to setting the confidence limit to
# (1  SWFPR)^(1 / Number of Constituents)
# where SWFPR = sitewide false positive rate. For this example, we
# will set SWFPR = 0.1. Thus, the required individual Type I Error level
# and confidence level per constituent are given as follows:
# nw = 10 Compliance Wells
# nc = 5 Constituents
# ne = 1 Evaluation per year
nw < 10
nc < 5
ne < 1
# Set number of future sampling occasions r to
# Number Compliance Wells x Number Evaluations per Year
r < nw * ne
conf.level < (1  0.1)^(1 / nc)
conf.level
#[1] 0.9791484
# So the required confidence level is 0.98, or 98%.
# Now determine the required number of background observations for each plan.
# 1) the 1of2 retesting plan for a median of order 3 using the
# background maximum
predIntNparSimultaneousN(n.median = 3, k = 1, m = 2, r = r,
conf.level = conf.level)
#[1] 14
# 2) the 1of4 plan on individual observations using the 3rd highest
# background value.
predIntNparSimultaneousN(k = 1, m = 4, r = r,
n.plus.one.minus.upl.rank = 3, conf.level = conf.level)
#[1] 18
#==========
# Cleanup
#
rm(nw, nc, ne, r, conf.level)