fitGHGBB: Fitting the Gaussian Hypergeometric Generalized Beta Binomial...

Description Usage Arguments Details Value References See Also Examples

View source: R/GHGbeta.R

Description

The function will fit the Gaussian Hypergeometric 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

1
fitGHGBB(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 lambda 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

Rodriguez-Avi, J., Conde-Sanchez, A., Saez-Castillo, A. J., & Olmo-Jimenez, M. J. (2007). A generalization of the beta-binomial distribution. Journal of the Royal Statistical Society. Series C (Applied Statistics), 56(1), 51-61.

Available at : http://dx.doi.org/10.1111/j.1467-9876.2007.00564.x

Pearson, J., 2009. Computation of Hypergeometric Functions. Transformation, (September), p.1–123.

See Also

hypergeo_powerseries

——————–

mle2

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
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(EstMLEGHGBB,start = list(a=0.1,b=0.1,c=0.2),
data = list(x=No.D.D,freq=Obs.fre.1)))
bbmle::coef(parameters)         #extracting the parameters
aGHGBB=bbmle::coef(parameters)[1]  #assigning the estimated a
bGHGBB=bbmle::coef(parameters)[2]  #assigning the estimated b
cGHGBB=bbmle::coef(parameters)[3]  #assigning the estimated c

#fitting when the random variable,frequencies,shape parameter values are given.
fitGHGBB(No.D.D,Obs.fre.1,aGHGBB,bGHGBB,cGHGBB)
#extracting the expected frequencies
fitGHGBB(No.D.D,Obs.fre.1,aGHGBB,bGHGBB,cGHGBB,FALSE)$exp.freq

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