pGBeta1 function

Generalized Beta Type-1 Distribution

Generalized Beta Type-1 Distribution

These functions provide the ability for generating probability density values, cumulative probability density values and moment about zero values for the Generalized Beta Type-1 Distribution bounded between [0,1].

pGBeta1(p,a,b,c)

Arguments

  • p: vector of probabilities.
  • a: single value for shape parameter alpha representing as a.
  • b: single value for shape parameter beta representing as b.
  • c: single value for shape parameter gamma representing as c.

Returns

The output pGBeta1 gives the cumulative density values in vector form.

Details

The probability density function and cumulative density function of a unit bounded Generalized Beta Type-1 Distribution with random variable P are given by

gP(p)=cB(a,b)pac1(1pc)b1 g_{P}(p)= \frac{c}{B(a,b)} p^{ac-1} (1-p^c)^{b-1}

; 0p10 \le p \le 1

GP(p)=pacaB(a,b)2F1(a,1b;pc;a+1) G_{P}(p)= \frac{p^{ac}}{aB(a,b)} 2F1(a,1-b;p^c;a+1)

0p10 \le p \le 1

a,b,c>0 a,b,c > 0

The mean and the variance are denoted by

E[P]=B(a+b,1c)B(a,1c) E[P]= \frac{B(a+b,\frac{1}{c})}{B(a,\frac{1}{c})} var[P]=B(a+b,2c)B(a,2c)(B(a+b,1c)B(a,1c))2 var[P]= \frac{B(a+b,\frac{2}{c})}{B(a,\frac{2}{c})}-(\frac{B(a+b,\frac{1}{c})}{B(a,\frac{1}{c})})^2

The moments about zero is denoted as

E[Pr]=B(a+b,rc)B(a,rc) E[P^r]= \frac{B(a+b,\frac{r}{c})}{B(a,\frac{r}{c})}

r=1,2,3,....r = 1,2,3,....

Defined as B(a,b)B(a,b) is Beta function. Defined as 2F1(a,b;c;d)2F1(a,b;c;d) is Gaussian Hypergeometric function.

NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.

Examples

#plotting the random variables and probability values col <- rainbow(5) a <- c(.1,.2,.3,1.5,2.15) plot(0,0,main="Probability density graph",xlab="Random variable",ylab="Probability density values", xlim = c(0,1),ylim = c(0,10)) for (i in 1:5) { lines(seq(0,1,by=0.001),dGBeta1(seq(0,1,by=0.001),a[i],1,2*a[i])$pdf,col = col[i]) } dGBeta1(seq(0,1,by=0.01),2,3,1)$pdf #extracting the pdf values dGBeta1(seq(0,1,by=0.01),2,3,1)$mean #extracting the mean dGBeta1(seq(0,1,by=0.01),2,3,1)$var #extracting the variance pGBeta1(0.04,2,3,4) #acquiring the cdf values for a=2,b=3,c=4 mazGBeta1(1.4,3,2,2) #acquiring the moment about zero values mazGBeta1(2,3,2,2)-mazGBeta1(1,3,2,2)^2 #acquiring the variance for a=3,b=2,c=2 #only the integer value of moments is taken here because moments cannot be decimal mazGBeta1(3.2,3,2,2)

References

\insertRef manoj2013mcdonaldfitODBOD

\insertRef janiffer2014estimatingfitODBOD

\insertRef roozegar2017mcdonaldfitODBOD