Estimate quantiles of a geometric distribution.
eqgeom(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 a geometric distribution
(e.g., egeom). 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 probability parameter.
Possible values are "mle/mme" (maximum likelihood and method of moments;
the default) and "mvue" (minimum variance unbiased). You cannot use
method="mvue" if length(x)=1. See the DETAILS section of the help file
for egeom for more information on these estimation methods.
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.
If x is a numeric vector, eqgeom 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, eqgeom
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.
The function eqgeom returns estimated quantiles as well as
the estimate of the rate parameter.
Quantiles are estimated by 1) estimating the probability parameter by
calling egeom, and then 2) calling the function
qgeom and using the estimated value for
the probability parameter.
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 A. Kemp. (1992). Univariate Discrete Distributions. Second Edition. John Wiley and Sons, New York, Chapter 5.
# NOT RUN {
# Generate an observation from a geometric distribution with parameter
# prob=0.2, then estimate the parameter prob and the 90'th percentile.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(250)
dat <- rgeom(1, prob = 0.2)
dat
#[1] 4
eqgeom(dat, p = 0.9)
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Geometric
#
#Estimated Parameter(s): prob = 0.2
#
#Estimation Method: mle/mme
#
#Estimated Quantile(s): 90'th %ile = 10
#
#Quantile Estimation Method: Quantile(s) Based on
# mle/mme Estimators
#
#Data: dat
#
#Sample Size: 1
#----------
# Clean up
#---------
rm(dat)
# }
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