eqexp.RdEstimate quantiles of an exponential distribution.
eqexp(x, p = 0.5, method = "mle/mme", digits = 0)a numeric vector of observations, or an object resulting from a call to an
estimating function that assumes an exponential distribution
(e.g., eexp). If x is a numeric vector,
missing (NA), undefined (NaN), and infinite (Inf, -Inf)
values are allowed but will be removed.
numeric vector of probabilities for which quantiles will be estimated.
All values of p must be between 0 and 1. The default value is p=0.5.
character string specifying the method to use to estimate the rate parameter.
Currently the only possible value is "mle/mme"
(maximum likelihood/method of moments; the default). See the DETAILS section of
the help file for eexp for more information.
an integer indicating the number of decimal places to round to when printing out
the value of 100*p. The default value is digits=0.
The function eqexp returns estimated quantiles as well as
the estimate of the rate parameter.
Quantiles are estimated by 1) estimating the rate parameter by
calling eexp, and then 2) calling the function
qexp and using the estimated value for
rate.
If x is a numeric vector, eqexp 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, eqexp
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.
Forbes, C., M. Evans, N. Hastings, and B. Peacock. (2011). Statistical Distributions. Fourth Edition. John Wiley and Sons, Hoboken, NJ.
Johnson, N. L., S. Kotz, and N. Balakrishnan. (1994). Continuous Univariate Distributions, Volume 1. Second Edition. John Wiley and Sons, New York.
The exponential distribution is a special case of the gamma distribution, and takes on positive real values. A major use of the exponential distribution is in life testing where it is used to model the lifetime of a product, part, person, etc.
The exponential distribution is the only continuous distribution with a “lack of memory” property. That is, if the lifetime of a part follows the exponential distribution, then the distribution of the time until failure is the same as the distribution of the time until failure given that the part has survived to time \(t\).
The exponential distribution is related to the double exponential (also called Laplace) distribution, and to the extreme value distribution.
# Generate 20 observations from an exponential distribution with parameter
# rate=2, then estimate the parameter and estimate the 90th percentile.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(250)
dat <- rexp(20, rate = 2)
eqexp(dat, p = 0.9)
#>
#> Results of Distribution Parameter Estimation
#> --------------------------------------------
#>
#> Assumed Distribution: Exponential
#>
#> Estimated Parameter(s): rate = 2.260587
#>
#> Estimation Method: mle/mme
#>
#> Estimated Quantile(s): 90'th %ile = 1.018578
#>
#> Quantile Estimation Method: Quantile(s) Based on
#> mle/mme Estimators
#>
#> Data: dat
#>
#> Sample Size: 20
#>
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Exponential
#
#Estimated Parameter(s): rate = 2.260587
#
#Estimation Method: mle/mme
#
#Estimated Quantile(s): 90'th %ile = 1.018578
#
#Quantile Estimation Method: Quantile(s) Based on
# mle/mme Estimators
#
#Data: dat
#
#Sample Size: 20
#
#----------
# Clean up
#---------
rm(dat)