fitGHGBB | R Documentation |
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. |
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.
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.
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.
Pearson, J., 2009. Computation of Hypergeometric Functions. Transformation, (September), p.1–123.
hypergeo_powerseries
——————–
mle2
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 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. results <- fitGHGBB(No.D.D,Obs.fre.1,aGHGBB,bGHGBB,cGHGBB) results #extracting the expected frequencies fitted(results) #extracting the residuals residuals(results)
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