EstMLEBetaBin | R Documentation |
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.
EstMLEBetaBin(x,freq,a,b,...)
x |
vector of binomial random variables. |
freq |
vector of frequencies. |
a |
single value for shape parameter alpha representing as a. |
b |
single value for shape parameter beta representing as b. |
... |
mle2 function inputs except data and estimating parameter. |
a,b > 0
x = 0,1,2,...
freq ≥ 0
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
EstMLEBetaBin
here is used as a wrapper for the mle2
function of bbmle package
therefore output is of class of mle2.
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: doi: 10.1186/1471-2288-8-58.
Trenkler, G., 1996. Continuous univariate distributions. Computational Statistics & Data Analysis, 21(1), p.119.
Available at: doi: 10.1016/0167-9473(96)90015-8.
Hughes, G., 1993. Using the Beta-Binomial Distribution to Describe Aggregated Patterns of Disease Incidence. Phytopathology, 83(9), p.759.
Available at: doi: 10.1094/PHYTO-83-759
mle2
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 #estimating the parameters using moment generating function methods EstMGFBetaBin(No.D.D,Obs.fre.1)
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