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Estimate the shape parameters of a beta distribution.
ebeta(x, method = "mle")
numeric vector of observations. All observations must be between greater than 0 and less than 1.
character string specifying the method of estimation. The possible values are
"mle"
(maximum likelihood; the default), "mme"
(method of moments),
and "mmue"
(method of moments based on the unbiased estimator of variance).
See the DETAILS section for more information on these estimation methods.
a list of class "estimate"
containing the estimated parameters and other information.
See
estimate.object
for details.
If x
contains any missing (NA
), undefined (NaN
) or
infinite (Inf
, -Inf
) values, they will be removed prior to
performing the estimation.
Let shape1=
shape2=
Maximum Likelihood Estimation (method="mle"
)
The maximum likelihood estimators (mle's) of the shape parameters
Method of Moments Estimators (method="mme"
)
The method of moments estimators (mme's) of the shape parameters
Method of Moments Estimators Based on the Unbiased Estimator of Variance (method="mmue"
)
These estimators are the same as the method of moments estimators except that
the method of moments estimator of variance is replaced with the unbiased estimator
of variance:
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. (1995). Continuous Univariate Distributions, Volume 2. Second Edition. John Wiley and Sons, New York.
Beta.
# NOT RUN {
# Generate 20 observations from a beta distribution with parameters
# shape1=2 and shape2=4, then estimate the parameters via
# maximum likelihood.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(250)
dat <- rbeta(20, shape1 = 2, shape2 = 4)
ebeta(dat)
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Beta
#
#Estimated Parameter(s): shape1 = 5.392221
# shape2 = 11.823233
#
#Estimation Method: mle
#
#Data: dat
#
#Sample Size: 20
#==========
# Repeat the above, but use the method of moments estimators:
ebeta(dat, method = "mme")
#Results of Distribution Parameter Estimation
#--------------------------------------------
#
#Assumed Distribution: Beta
#
#Estimated Parameter(s): shape1 = 5.216311
# shape2 = 11.461341
#
#Estimation Method: mme
#
#Data: dat
#
#Sample Size: 20
#==========
# Clean up
#---------
rm(dat)
# }
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