R/GHGbeta.R
fitGHGBB.Rd
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.
fitGHGBB(x,obs.freq,a,b,c)
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. |
The output of fitGHGBB
gives the class format fitGB
and fit
consisting a list
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.
fitGB
fitted values of dGHGBB
.
NegLL
Negative Loglikelihood value.
a
estimated value for alpha parameter as a.
b
estimated value for beta parameter as b.
c
estimated value for gamma parameter as c.
AIC
AIC value.
over.dis.para
over dispersion value.
call
the inputs of the function.
Methods summary
, print
, AIC
, residuals
and fitted
can be used
to extract specific outputs.
$$0 < a,b,c$$ $$x = 0,1,2,...$$ $$obs.freq \ge 0$$
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
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.
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 <- EstMLEGHGBB(No.D.D,Obs.fre.1,0.1,20,1.3) bbmle::coef(parameters) #extracting the parameters#> a b c #> 1.3507011 0.3245453 0.7005190aGHGBB <- 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. results <- fitGHGBB(No.D.D,Obs.fre.1,aGHGBB,bGHGBB,cGHGBB) results#> Call: #> fitGHGBB(x = No.D.D, obs.freq = Obs.fre.1, a = aGHGBB, b = bGHGBB, #> c = cGHGBB) #> #> Chi-squared test for Gaussian Hypergeometric Generalized Beta-Binomial Distribution #> #> Observed Frequency : 47 54 43 40 40 41 39 95 #> #> expected Frequency : 47.87 50.14 46.52 42.08 38.58 37.32 41.78 94.71 #> #> estimated a parameter : 1.350701 ,estimated b parameter : 0.3245453 , #> #> estimated c parameter : 0.700519 #> #> X-squared : 1.2831 ,df : 4 ,p-value : 0.8642 #> #> over dispersion : 0.4324847#> [1] 47.87 50.14 46.52 42.08 38.58 37.32 41.78 94.71#> [1] -0.87 3.86 -3.52 -2.08 1.42 3.68 -2.78 0.29