EnvStats (version 2.1.0)

eqbinom: Estimate Quantiles of a Binomial Distribution

Description

Estimate quantiles of a binomial distribution.

Usage

eqbinom(x, size = NULL, p = 0.5, method = "mle/mme/mvue", digits = 0)

Arguments

x

numeric or logical vector of observations, or an object resulting from a call to an estimating function that assumes a binomial distribution (e.g., ebinom). If x is a vector of observat

size

positive integer indicating the of number of trials; size must be at least as large as the value of x.

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 of estimation. The only possible value is "mle/mme/mvue" (maximum likelihood, method of moments, and minimum variance unbiased). See the DETAILS section of the help file for

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, eqbinom 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, eqbinom 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 eqbinom returns estimated quantiles as well as estimates of the prob parameter. Quantiles are estimated by 1) estimating the prob parameter by calling ebinom, and then 2) calling the function qbinom and using the estimated value for prob.

References

Agresti, A., and B.A. Coull. (1998). Approximate is Better than "Exact" for Interval Estimation of Binomial Proportions. The American Statistician, 52(2), 119--126. Agresti, A., and B. Caffo. (2000). Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures. The American Statistician, 54(4), 280--288. Berthouex, P.M., and L.C. Brown. (1994). Statistics for Environmental Engineers. Lewis Publishers, Boca Raton, FL, Chapters 2 and 15. Cochran, W.G. (1977). Sampling Techniques. John Wiley and Sons, New York, Chapter 3. Fisher, R.A., and F. Yates. (1963). Statistical Tables for Biological, Agricultural, and Medical Research. 6th edition. Hafner, New York, 146pp. Fleiss, J. L. (1981). Statistical Methods for Rates and Proportions. Second Edition. John Wiley and Sons, New York, Chapters 1-2. Forbes, C., M. Evans, N. Hastings, and B. Peacock. (2011). Statistical Distributions. Fourth Edition. John Wiley and Sons, Hoboken, NJ. Gilbert, R.O. (1987). Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York, NY, Chapter 11. Johnson, N. L., S. Kotz, and A.W. Kemp. (1992). Univariate Discrete Distributions. Second Edition. John Wiley and Sons, New York, Chapter 3. Millard, S.P., and Neerchal, N.K. (2001). Environmental Statistics with S-PLUS. CRC Press, Boca Raton, Florida. Newcombe, R.G. (1998a). Two-Sided Confidence Intervals for the Single Proportion: Comparison of Seven Methods. Statistics in Medicine, 17, 857--872. Ott, W.R. (1995). Environmental Statistics and Data Analysis. Lewis Publishers, Boca Raton, FL, Chapter 4. USEPA. (1989b). Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities, Interim Final Guidance. EPA/530-SW-89-026. Office of Solid Waste, U.S. Environmental Protection Agency, Washington, D.C. 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. p.6-38. Zar, J.H. (2010). Biostatistical Analysis. Fifth Edition. Prentice-Hall, Upper Saddle River, NJ, Chapter 24.

See Also

ebinom, Binomial, estimate.object.

Examples

# Generate 20 observations from a binomial distribution with 
  # parameters size=1 and prob=0.2, then estimate the 'prob' 
  # parameter and the 90'th percentile. 
  # (Note: the call to set.seed simply allows you to reproduce this example. 

  set.seed(251) 
  dat <- rbinom(20, size = 1, prob = 0.2) 
  eqbinom(dat, p = 0.9) 

  #Results of Distribution Parameter Estimation
  #--------------------------------------------
  #
  #Assumed Distribution:            Binomial
  #
  #Estimated Parameter(s):          size = 20.0
  #                                 prob =  0.1
  #
  #Estimation Method:               mle/mme/mvue for 'prob'
  #
  #Estimated Quantile(s):           90'th %ile = 4
  #
  #Quantile Estimation Method:      Quantile(s) Based on
  #                                 mle/mme/mvue for 'prob' Estimators
  #
  #Data:                            dat
  #
  #Sample Size:                     20
  #
  #
  #

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

  rm(dat)