R/Beta.R
fitBetaBin.Rd
The function will fit the 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.
fitBetaBin(x,obs.freq,a,b)
x | vector of binomial random variables. |
---|---|
obs.freq | vector of frequencies. |
a | single value for shape parameter alpha representing as a. |
b | single value for shape parameter beta representing as b. |
The output of fitBetaBin
gives the class format fitBB
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.
fitBB
fitted values of dBetaBin
.
NegLL
Negative Log Likelihood value.
a
estimated value for alpha parameter as a.
b
estimated value for alpha parameter as b.
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.
$$0 < a,b$$ $$x = 0,1,2,...,n$$ $$obs.freq \ge 0$$
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
Young-Xu, Y. & Chan, K.A., 2008. Pooling overdispersed binomial data to estimate event rate. BMC medical research methodology, 8(1), p.58.
Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2538541&tool=pmcentrez&rendertype=abstract.
Trenkler, G., 1996. Continuous univariate distributions. Computational Statistics & Data Analysis, 21(1), p.119.
Available at: http://linkinghub.elsevier.com/retrieve/pii/0167947396900158.
Hughes, G., 1993. Using the Beta-Binomial Distribution to Describe Aggregated Patterns of Disease Incidence. Phytopathology, 83(9), p.759.
Available at: http://www.apsnet.org/publications/phytopathology/backissues/Documents/1993Abstracts/Phyto_83_759.htm
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 <- EstMLEBetaBin(No.D.D,Obs.fre.1,0.1,0.1) bbmle::coef(parameters) #extracting the parameters a and b#> a b #> 0.7229420 0.5808483aBetaBin <- bbmle::coef(parameters)[1] #assigning the parameter a bBetaBin <- bbmle::coef(parameters)[2] #assigning the parameter b #fitting when the random variable,frequencies,shape parameter values are given. fitBetaBin(No.D.D,Obs.fre.1,aBetaBin,bBetaBin)#> Call: #> fitBetaBin(x = No.D.D, obs.freq = Obs.fre.1, a = aBetaBin, b = bBetaBin) #> #> Chi-squared test for Beta-Binomial Distribution #> #> Observed Frequency : 47 54 43 40 40 41 39 95 #> #> expected Frequency : 54.62 42 38.9 38.54 40.07 44 53.09 87.78 #> #> estimated a parameter : 0.722942 ,estimated b parameter : 0.5808483 #> #> X-squared : 9.5171 ,df : 5 ,p-value : 0.0901 #> #> over dispersion : 0.4340673#estimating the parameters using moment generating function methods results <- EstMGFBetaBin(No.D.D,Obs.fre.1) results#> Call: #> EstMGFBetaBin(x = No.D.D, freq = Obs.fre.1) #> #> Coefficients: #> a b #> 0.7161628 0.5963324aBetaBin1 <- results$a #assigning the estimated a bBetaBin1 <- results$b #assigning the estimated b #fitting when the random variable,frequencies,shape parameter values are given. BB <- fitBetaBin(No.D.D,Obs.fre.1,aBetaBin1,bBetaBin1) #extracting the expected frequencies fitted(BB)#> [1] 56.60 43.01 39.57 38.97 40.27 43.89 52.39 84.29#> [1] -9.60 10.99 3.43 1.03 -0.27 -2.89 -13.39 10.71