EstMGFBetaBin | 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.
EstMGFBetaBin(x,freq)
x |
vector of binomial random variables. |
freq |
vector of frequencies. |
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.
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.
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 results <- EstMGFBetaBin(No.D.D,Obs.fre.1) # extract the estimated parameters and summary coef(results) summary(results) AIC(results) #show the AIC value
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