R/Beta.R
EstMGFBetaBin.Rd
The functions will estimate the shape parameters using the maximum log likelihood method and moment generating function method for the Beta-Binomial distribution when the binomial random variables and corresponding frequencies are given.
EstMGFBetaBin(x,freq)
x | vector of binomial random variables. |
---|---|
freq | vector of frequencies. |
The output of EstMGFBetaBin
will produce the class mgf
format consisting
a
shape parameter of beta distribution representing for alpha
b
shape parameter of beta distribution representing for beta
min
Negative loglikelihood value
AIC
AIC value
call
the inputs for the function
Methods print
, summary
, coef
and AIC
can be used to extract
specific outputs.
$$a,b > 0$$ $$x = 0,1,2,...$$ $$freq \ge 0$$
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
Young-Xu, Y. & Chan, K.A., 2008. Pooling overdispersed binomial data to estimate event rate. BMC medical research methodology, 8(1), p.58.
Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2538541&tool=pmcentrez&rendertype=abstract.
Trenkler, G., 1996. Continuous univariate distributions. Computational Statistics & Data Analysis, 21(1), p.119.
Available at: http://linkinghub.elsevier.com/retrieve/pii/0167947396900158.
Hughes, G., 1993. Using the Beta-Binomial Distribution to Describe Aggregated Patterns of Disease Incidence. Phytopathology, 83(9), p.759.
Available at: http://www.apsnet.org/publications/phytopathology/backissues/Documents/1993Abstracts/Phyto_83_759.htm
No.D.D <- 0:7 #assigning the random variables Obs.fre.1 <- c(47,54,43,40,40,41,39,95) #assigning the corresponding frequencies #estimating the parameters using maximum log likelihood value and assigning it estimate <- EstMLEBetaBin(No.D.D,Obs.fre.1,a=0.1,b=0.1) bbmle::coef(estimate) #extracting the parameters#> a b #> 0.7229420 0.5808483#estimating the parameters using moment generating function methods results <- EstMGFBetaBin(No.D.D,Obs.fre.1) # extract the estimated parameters and summary coef(results)#> a b #> 0.7161628 0.5963324summary(results)#> Coefficients: #> a b #> 0.7161628 0.5963324 #> #> Negative Log-likelihood : 813.5872 #> #> AIC : 1631.174#> [1] 1631.174