R/CorrBin.R
fitCorrBin.Rd
The function will fit the Correlated Binomial Distribution when random variables, corresponding frequencies, probability of success and covariance are given. It will provide the expected frequencies, chi-squared test statistics value, p value, and degree of freedom so that it can be seen if this distribution fits the data.
fitCorrBin(x,obs.freq,p,cov)
x | vector of binomial random variables. |
---|---|
obs.freq | vector of frequencies. |
p | single value for probability of success. |
cov | single value for covariance. |
The output of fitCorrBin
gives the class format fitCB
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.
corr
Correlation value.
fitCB
fitted probability values of dCorrBin
.
NegLL
Negative Log Likelihood value.
AIC
AIC value.
call
the inputs of the function.
Methods summary
, print
, AIC
, residuals
and fitted
can be used to extract specific outputs.
$$obs.freq \ge 0$$ $$x = 0,1,2,..$$ $$0 < p < 1$$ $$-\infty < cov < +\infty$$
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
Johnson, N. L., Kemp, A. W., & Kotz, S. (2005). Univariate discrete distributions (Vol. 444). Hoboken, NJ: Wiley-Interscience.
L. L. Kupper, J.K.H., 1978. The Use of a Correlated Binomial Model for the Analysis of Certain Toxicological Experiments. Biometrics, 34(1), pp.69-76.
Paul, S.R., 1985. A three-parameter generalization of the binomial distribution. Communications in Statistics - Theory and Methods, 14(6), pp.1497-1506.
Available at: http://www.tandfonline.com/doi/abs/10.1080/03610928508828990.
Jorge G. Morel and Nagaraj K. Neerchal. Overdispersion Models in SAS. SAS Institute, 2012.
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 <- EstMLECorrBin(x=No.D.D,freq=Obs.fre.1,p=0.5,cov=0.0050) pCorrBin <- bbmle::coef(parameters)[1] covCorrBin <- bbmle::coef(parameters)[2] #fitting when the random variable,frequencies,probability and covariance are given results <- fitCorrBin(No.D.D,Obs.fre.1,pCorrBin,covCorrBin) results#> Call: #> fitCorrBin(x = No.D.D, obs.freq = Obs.fre.1, p = pCorrBin, cov = covCorrBin) #> #> Chi-squared test for Correlated Binomial Distribution #> #> Observed Frequency : 47 54 43 40 40 41 39 95 #> #> expected Frequency : 10.7 50.1 79.85 45.84 24.87 74.29 84.07 29.28 #> #> estimated p value : 0.5469539 ,estimated cov value : 0.05714648 #> #> X-squared : 336.9971 ,df : 5 ,p-value : 0#> p #> 930.6631#> [1] 10.70 50.10 79.85 45.84 24.87 74.29 84.07 29.28