Fitting the Lovinson Multiplicative Binomial Distribution when binomial random variable, frequency, probability of success and theta parameter are given
Fitting the Lovinson Multiplicative Binomial Distribution when binomial random variable, frequency, probability of success and theta parameter are given
The function will fit the Lovinson Multiplicative Binomial distribution when random variables, corresponding frequencies, probability of success and phi 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.
fitLMBin(x,obs.freq,p,phi)
Arguments
x: vector of binomial random variables.
obs.freq: vector of frequencies.
p: single value for probability of success.
phi: single value for phi parameter.
Returns
The output of fitLMBin gives the class format fitLMB 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.
fitLMB fitted probability values of dLMBin.
NegLL Negative Log Likelihood value.
p estimated probability value.
phi estimated phi 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.
Details
obs.freq≥0x=0,1,2,..0<p<10<phi
Examples
No.D.D <-0:7#assigning the random variablesObs.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 itparameters <- EstMLELMBin(x=No.D.D,freq=Obs.fre.1,p=0.1,phi=.3)pLMBin=bbmle::coef(parameters)[1]#assigning the estimated probability valuephiLMBin <- bbmle::coef(parameters)[2]#assigning the estimated phi value#fitting when the random variable,frequencies,probability and phi are givenresults <- fitLMBin(No.D.D,Obs.fre.1,pLMBin,phiLMBin)results
#extracting the AIC valueAIC(results)#extract fitted valuesfitted(results)