EnvStats (version 2.1.0)

eqbeta: Estimate Quantiles of a Beta Distribution

Description

Estimate quantiles of a beta distribution.

Usage

eqbeta(x, p = 0.5, method = "mle", digits = 0)

Arguments

x

a numeric vector of observations, or an object resulting from a call to an estimating function that assumes a beta distribution (e.g., ebeta). If x is a numeric vector, missing (

p

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.

method

character string specifying the method to use to estimate the shape and scale parameters of the distribution. The possible values are "mle" (maximum likelihood; the default), "mme" (method of moments), and

digits

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.

Value

• If x is a numeric vector, eqbeta 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, eqbeta 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.

Details

The function eqbeta returns estimated quantiles as well as estimates of the shape1 and shape2 parameters. Quantiles are estimated by 1) estimating the shape1 and shape2 parameters by calling ebeta, and then 2) calling the function qbeta and using the estimated values for shape1 and shape2.

References

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.

See Also

ebeta, Beta, estimate.object.

Examples

# Generate 20 observations from a beta distribution with parameters 
  # shape1=2 and shape2=4, then estimate the parameters via 
  # maximum likelihood and estimate the 90'th percentile.
  # (Note: the call to set.seed simply allows you to reproduce this example.)

  set.seed(250) 
  dat <- rbeta(20, shape1 = 2, shape2 = 4) 
  eqbeta(dat, p = 0.9)

  #Results of Distribution Parameter Estimation
  #--------------------------------------------
  #
  #Assumed Distribution:            Beta
  #
  #Estimated Parameter(s):          shape1 =  5.392221
  #                                 shape2 = 11.823233
  #
  #Estimation Method:               mle
  #
  #Estimated Quantile(s):           90'th %ile = 0.4592796
  #
  #Quantile Estimation Method:      Quantile(s) Based on
  #                                 mle Estimators
  #
  #Data:                            dat
  #
  #Sample Size:                     20

  #----------
  # Clean up

  rm(dat)