R/AddBin.R
EstMLEAddBin.Rd
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.
EstMLEAddBin(x,freq)
x | vector of binomial random variables. |
---|---|
freq | vector of frequencies. |
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.
$$freq \ge 0$$ $$x = 0,1,2,..$$
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
Johnson, N. L., Kemp, A. W., & Kotz, S. (2005). Univariate discrete distributions (Vol. 444). Hoboken, NJ: Wiley-Interscience.
L. L. Kupper, J.K.H., 1978. The Use of a Correlated Binomial Model for the Analysis of Certain Toxicological Experiments. Biometrics, 34(1), pp.69-76.
Paul, S.R., 1985. A three-parameter generalization of the binomial distribution. Communications in Statistics - Theory and Methods, 14(6), pp.1497-1506.
Available at: http://www.tandfonline.com/doi/abs/10.1080/03610928508828990 .
Jorge G. Morel and Nagaraj K. Neerchal. Overdispersion Models in SAS. SAS Institute, 2012.
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# NOT RUN { #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) # }