fitAddBin {fitODBOD} | R Documentation |
Fitting the Additive Binomial Distribution when binomial random variable, frequency, probability of success and alpha are given
Description
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.
Usage
fitAddBin(x,obs.freq,p,alpha)
Arguments
x |
vector of binomial random variables. |
obs.freq |
vector of frequencies. |
p |
single value for probability of success. |
alpha |
single value for alpha. |
Details
obs.freq \ge 0
x = 0,1,2,..
0 < p < 1
-1 < alpha < 1
Value
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.
References
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.
Examples
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)