# R computations for a proportion using a beta prior

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Today we talked about using a beta prior to learn about a proportion. Inference about p is done by use of the beta posterior distribution and prediction about future samples is done by means of the predictive distribution.

Here are the R computations for the cell-phone example. I’ll first illustrate inference for the proportion p, and then I’ll illustrate the use of the special function pbetap (in the LearnBayes package) to compute the beta-binomial predictive distribution to learn about the number of successes in a future sample.

> library(LearnBayes)
>
> a=6.8; b=2.5 # parameters of beta prior
> n=24; y=9 # sample size and number of yes’s in sample
>
> a1=a+y; b1=b+n-y # parameters of beta posterior
>
> # I’ll illustrate different types of inferences
>
> # a point estimate is given by the posterior mean
> a1/(a1+b1)
[1] 0.4744745
>
> # or you could find the posterior median
> qbeta(.5,a1,b1)
[1] 0.4739574
>
> # a 90% interval estimate is found by use of the 5th and 95th quantiles
> # of the beta curve
> qbeta(c(.05,.95),a1,b1)
[1] 0.3348724 0.6158472
>
> # we illustrate prediction by use of the function pbetap
> # suppose we take a future sample of 20
> # how many will be driving when using a cell phone?
>
> m=20; ys=0:m
> pred.probs=pbetap(c(a1,b1),m,ys)
>
> # display the predictive probabilities
>
> cbind(ys,pred.probs)
ys pred.probs
[1,] 0 7.443708e-05
[2,] 1 6.444416e-04
[3,] 2 2.897264e-03
[4,] 3 8.968922e-03
[5,] 4 2.139155e-02
[6,] 5 4.170364e-02
[7,] 6 6.884411e-02
[8,] 7 9.841322e-02
[9,] 8 1.236003e-01
[10,] 9 1.376228e-01
[11,] 10 1.365218e-01
[12,] 11 1.208324e-01
[13,] 12 9.524434e-02
[14,] 13 6.650657e-02
[15,] 14 4.075296e-02
[16,] 15 2.159001e-02
[17,] 16 9.665296e-03
[18,] 17 3.527764e-03
[19,] 18 9.889799e-04
[20,] 19 1.901993e-04
[21,] 20 1.891124e-05
>
> # what is the probability that there are at least
> # 10 cell phone drivers in my sample?
>
> sum(pred.probs*(ys>=10))
[1] 0.4958392
>

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