Description Usage Arguments Details Value Note Author(s) References See Also Examples

Estimate quantiles of a lognormal distribution, and optionally construct a confidence interval for a quantile.

1 2 3 |

`x` |
a numeric vector of positive observations, or an object resulting from a call to an
estimating function that assumes a lognormal distribution
(i.e., |

`p` |
numeric vector of probabilities for which quantiles will be estimated.
All values of |

`method` |
character string indicating what method to use to estimate the quantile(s).
The possible values are |

`ci` |
logical scalar indicating whether to compute a confidence interval for the quantile.
The default value is |

`ci.method` |
character string indicating what method to use to construct the confidence interval
for the quantile. The possible values are |

`ci.type` |
character string indicating what kind of confidence interval for the quantile to compute.
The possible values are |

`conf.level` |
a scalar between 0 and 1 indicating the confidence level of the confidence interval.
The default value is |

`digits` |
an integer indicating the number of decimal places to round to when printing out
the value of |

If `x`

contains any missing (`NA`

), undefined (`NaN`

) or
infinite (`Inf`

, `-Inf`

) values, they will be removed prior to
performing the estimation.

Quantiles and their associated confidence intervals are constructed by calling
the function `eqnorm`

using the log-transformed data and then
exponentiating the quantiles and confidence limits.

In the special case when `p=0.5`

and `method="mvue"`

, the estimated
median is computed using the method given in Gilbert (1987, p.172) and
Bradu and Mundlak (1970).

If `x`

is a numeric vector, `eqlnorm`

returns a list of class
`"estimate"`

containing the estimated quantile(s) and other information.
See `estimate.object`

for details.

If `x`

is the result of calling an estimation function, `eqlnorm`

returns a list whose class is the same as `x`

. The list contains the same
components as `x`

, as well as components called `quantiles`

and
`quantile.method`

. In addition, if `ci=TRUE`

, the returned list
contains a component called `interval`

containing the confidence interval
information. If `x`

already has a component called `interval`

, this
component is replaced with the confidence interval information.

Percentiles are sometimes used in environmental standards and regulations. For example, Berthouex and Brown (2002, p.71) note that England has water quality limits based on the 90th and 95th percentiles of monitoring data not exceeding specified levels. They also note that the U.S. EPA has specifications for air quality monitoring, aquatic standards on toxic chemicals, and maximum daily limits for industrial effluents that are all based on percentiles. Given the importance of these quantities, it is essential to characterize the amount of uncertainty associated with the estimates of these quantities. This is done with confidence intervals.

Steven P. Millard (EnvStats@ProbStatInfo.com)

Berthouex, P.M., and L.C. Brown. (2002). *Statistics for Environmental Engineers*.
Lewis Publishers, Boca Raton.

Bradu, D., and Y. Mundlak. (1970). Estimation in Lognormal Linear Models.
*Journal of the American Statistical Association* **65**, 198-211.

Conover, W.J. (1980). *Practical Nonparametric Statistics*. Second Edition.
John Wiley and Sons, New York.

Gibbons, R.D., D.K. Bhaumik, and S. Aryal. (2009).
*Statistical Methods for Groundwater Monitoring*, Second Edition.
John Wiley & Sons, Hoboken.

Gilbert, R.O. (1987). *Statistical Methods for Environmental Pollution Monitoring*.
Van Nostrand Reinhold, New York.

Helsel, D.R., and R.M. Hirsch. (1992). *Statistical Methods in Water Resources Research*.
Elsevier, New York, NY, pp.88-90.

Johnson, N.L., and B.L. Welch. (1940). Applications of the Non-Central t-Distribution.
*Biometrika* **31**, 362-389.

Millard, S.P., and Neerchal, N.K. (2001). *Environmental Statistics with S-PLUS*.
CRC Press, Boca Raton, Florida.

Owen, D.B. (1962). *Handbook of Statistical Tables*. Addison-Wesley, Reading, MA.

Stedinger, J. (1983). Confidence Intervals for Design Events.
*Journal of Hydraulic Engineering* **109**(1), 13-27.

Stedinger, J.R., R.M. Vogel, and E. Foufoula-Georgiou. (1993).
Frequency Analysis of Extreme Events. In: Maidment, D.R., ed. *Handbook of Hydrology*.
McGraw-Hill, New York, Chapter 18, pp.29-30.

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.

`eqnorm`

, `Lognormal`

, `elnorm`

,
`estimate.object`

.

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 | ```
# Generate 20 observations from a lognormal distribution with
# parameters meanlog=3 and sdlog=0.5, then estimate the 90th
# percentile and create a one-sided upper 95% confidence interval
# for that percentile.
# (Note: the call to set.seed simply allows you to reproduce this
# example.)
set.seed(47)
dat <- rlnorm(20, meanlog = 3, sdlog = 0.5)
eqlnorm(dat, p = 0.9, ci = TRUE, ci.type = "upper")
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Lognormal
#
#Estimated Parameter(s): meanlog = 2.9482139
# sdlog = 0.4553215
#
#Estimation Method: mvue
#
#Estimated Quantile(s): 90'th %ile = 34.18312
#
#Quantile Estimation Method: qmle
#
#Data: dat
#
#Sample Size: 20
#
#Confidence Interval for: 90'th %ile
#
#Confidence Interval Method: Exact
#
#Confidence Interval Type: upper
#
#Confidence Level: 95%
#
#Confidence Interval: LCL = 0.00000
# UCL = 45.84008
#----------
# Compare these results with the true 90'th percentile:
qlnorm(p = 0.9, meanlog = 3, sdlog = 0.5)
#[1] 38.1214
#----------
# Clean up
rm(dat)
#--------------------------------------------------------------------
# Example 17-3 of USEPA (2009, p. 17-17) shows how to construct a
# beta-content upper tolerance limit with 95% coverage and 95%
# confidence using chrysene data and assuming a lognormal
# distribution.
# A beta-content upper tolerance limit with 95% coverage and 95%
# confidence is equivalent to the 95% upper confidence limit for the
# 95th percentile.
attach(EPA.09.Ex.17.3.chrysene.df)
Chrysene <- Chrysene.ppb[Well.type == "Background"]
eqlnorm(Chrysene, p = 0.95, ci = TRUE, ci.type = "upper")
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Lognormal
#
#Estimated Parameter(s): meanlog = 2.5085773
# sdlog = 0.6279479
#
#Estimation Method: mvue
#
#Estimated Quantile(s): 95'th %ile = 34.51727
#
#Quantile Estimation Method: qmle
#
#Data: Chrysene
#
#Sample Size: 8
#
#Confidence Interval for: 95'th %ile
#
#Confidence Interval Method: Exact
#
#Confidence Interval Type: upper
#
#Confidence Level: 95%
#
#Confidence Interval: LCL = 0.0000
# UCL = 90.9247
#----------
# Clean up
rm(Chrysene)
detach("EPA.09.Ex.17.3.chrysene.df")
``` |

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