fitMcGBB: Fitting the McDonald Generalized beta binomial distribution...

Description Usage Arguments Details Value References See Also Examples

View source: R/Gbeta1.R

Description

The function will fit the McDonald Generalized Beta Binomial Distribution when random variables, corresponding frequencies and shape parameters are given. It will provide the expected frequencies, chi-squared test statistics value, p value, degree of freedom and over dispersion value so that it can be seen if this distribution fits the data.

Usage

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fitMcGBB(x,obs.freq,a,b,c,print)

Arguments

x

vector of binomial random variables

obs.freq

vector of frequencies

a

single value for shape parameter alpha representing a

b

single value for shape parameter beta representing b

c

single value for shape parameter gamma representing c

print

logical value for print or not

Details

0 < a,b,c

x = 0,1,2,...

obs.freq ≥ 0

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

Value

The output of fitGHGBB gives a list format consisting

bin.ran.var binomial random variables

obs.freq corresponding observed frequencies

exp.freq corresponding expected frequencies

statistic chi-squared test statistics

df degree of freedom

p.value probability value by chi-squared test statistic

over.dis.para over dispersion value.

References

Manoj, C., Wijekoon, P. & Yapa, R.D., 2013. The McDonald Generalized Beta-Binomial Distribution: A New Binomial Mixture Distribution and Simulation Based Comparison with Its Nested Distributions in Handling Overdispersion. International Journal of Statistics and Probability, 2(2), pp.24-41.

Available at: http://www.ccsenet.org/journal/index.php/ijsp/article/view/23491 .

Janiffer, N.M., Islam, A. & Luke, O., 2014. Estimating Equations for Estimation of Mcdonald Generalized Beta - Binomial Parameters. , (October), pp.702-709.

Roozegar, R., Tahmasebi, S. & Jafari, A.A., 2015. The McDonald Gompertz Distribution: Properties and Applications. Communications in Statistics - Simulation and Computation, (May), pp.0-0.

Available at: http://www.tandfonline.com/doi/full/10.1080/03610918.2015.1088024 .

See Also

mle2

Examples

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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
parameters=suppressWarnings(bbmle::mle2(EstMLEMcGBB,start = list(a=0.1,b=0.1,c=0.2),
data = list(x=No.D.D,freq=Obs.fre.1)))
aMcGBB=bbmle::coef(parameters)[1]         #assigning the estimated a
bMcGBB=bbmle::coef(parameters)[2]         #assigning the estimated b
cMcGBB=bbmle::coef(parameters)[3]         #assigning the estimated c

#fitting when the random variable,frequencies,shape parameter values are given.
fitMcGBB(No.D.D,Obs.fre.1,aMcGBB,bMcGBB,cMcGBB)
#extracting the expected frequencies
fitMcGBB(No.D.D,Obs.fre.1,aMcGBB,bMcGBB,cMcGBB,FALSE)$exp.freq

Amalan-ConStat/R-fitODBOD documentation built on Oct. 1, 2018, 7:13 p.m.