fitAddBin {fitODBOD} | R Documentation |
The function will fit the Additive Binomial distribution when random variables, corresponding frequencies, probability of success and alpha are given. It will provide the expected frequencies, chi-squared test statistics value, p value, and degree of freedom value so that it can be seen if this distribution fits the data.
fitAddBin(x,obs.freq,p,alpha)
x |
vector of binomial random variables. |
obs.freq |
vector of frequencies. |
p |
single value for probability of success. |
alpha |
single value for alpha. |
obs.freq \ge 0
x = 0,1,2,..
0 < p < 1
-1 < alpha < 1
The output of fitAddBin
gives the class format fitAB
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.
fitAB
fitted probability values of dAddBin
.
NegLL
Negative Log Likelihood value.
p
estimated probability value.
alpha
estimated alpha parameter value.
AIC
AIC value.
call
the inputs of the function.
Methods summary
, print
, AIC
, residuals
and fitted
can be used to extract specific outputs.
Johnson NL, Kemp AW, Kotz S (2005). Univariate discrete distributions, volume 444. John Wiley and Sons. Kupper LL, Haseman JK (1978). “The use of a correlated binomial model for the analysis of certain toxicological experiments.” Biometrics, 69–76. Paul SR (1985). “A three-parameter generalization of the binomial distribution.” History and Philosophy of Logic, 14(6), 1497–1506. Morel JG, Neerchal NK (2012). Overdispersion models in SAS. SAS Publishing.
No.D.D <- 0:7 #assigning the random variables
Obs.fre.1 <- c(47,54,43,40,40,41,39,95) #assigning the corresponding the frequencies
## Not run:
#assigning the estimated probability value
paddbin <- EstMLEAddBin(No.D.D,Obs.fre.1)$p
#assigning the estimated alpha value
alphaaddbin <- EstMLEAddBin(No.D.D,Obs.fre.1)$alpha
#fitting when the random variable,frequencies,probability and alpha are given
results <- fitAddBin(No.D.D,Obs.fre.1,paddbin,alphaaddbin)
results
#extracting the AIC value
AIC(results)
#extract fitted values
fitted(results)
## End(Not run)