Estimating the probability of success and alpha for Additive Binomial Distribution

The function will estimate the probability of success and alpha using the maximum log likelihood method for the Additive Binomial distribution when the binomial random variables and corresponding frequencies are given.

Usage

EstMLEAddBin(x,freq)

Arguments

x: vector of binomial random variables.
freq: vector of frequencies.

Value

The output of EstMLEAddBin will produce the class mlAB and ml with a list consisting

min Negative Log Likelihood value.

p estimated probability of success.

alpha estimated alpha parameter.

AIC AIC value.

call the inputs for the function.

Methods print, summary, coef and AIC can be used to extract specific outputs.

Details

$$freq \ge 0$$ $$x = 0,1,2,..$$

NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.

References

Johnson NL, Kemp AW, Kotz S (2005). Univariate discrete distributions, volume 444. John Wiley and Sons. Kupper LL, Haseman JK (1978). “The use of a correlated binomial model for the analysis of certain toxicological experiments.” Biometrics, 69--76. Paul SR (1985). “A three-parameter generalization of the binomial distribution.” History and Philosophy of Logic, 14(6), 1497--1506. Morel JG, Neerchal NK (2012). Overdispersion models in SAS. SAS Publishing.

Examples

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

if (FALSE) {
#estimating the probability value and alpha value
results <- EstMLEAddBin(No.D.D,Obs.fre.1)

#printing the summary of results
summary(results)

#extracting the estimated parameters
coef(results)
}