fitMultiBin {fitODBOD} | R Documentation |
Fitting the Multiplicative Binomial Distribution when binomial random variable, frequency, probability of success and theta parameter are given
Description
The function will fit the Multiplicative Binomial distribution when random variables, corresponding frequencies, probability of success and theta parameter 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
fitMultiBin(x,obs.freq,p,theta)
Arguments
x |
vector of binomial random variables. |
obs.freq |
vector of frequencies. |
p |
single value for probability of success. |
theta |
single value for theta parameter. |
Details
obs.freq \ge 0
x = 0,1,2,..
0 < p < 1
0 < theta
Value
The output of fitMultiBin
gives the class format fitMuB
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.
fitMuB
fitted probability values of dMultiBin
.
NegLL
Negative Log Likelihood value.
p
estimated probability value.
theta
estimated theta 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.
See Also
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 frequencies
#estimating the parameters using maximum log likelihood value and assigning it
parameters <- EstMLEMultiBin(x=No.D.D,freq=Obs.fre.1,p=0.1,theta=.3)
pMultiBin <- bbmle::coef(parameters)[1] #assigning the estimated probability value
thetaMultiBin <- bbmle::coef(parameters)[2] #assigning the estimated theta value
#fitting when the random variable,frequencies,probability and theta are given
results <- fitMultiBin(No.D.D,Obs.fre.1,pMultiBin,thetaMultiBin)
results
#extracting the AIC value
AIC(results)
#extract fitted values
fitted(results)