teachbayes_intro.R
In TeachBayes: Teaching Bayesian Inference

## ----global_options, include=FALSE---------------------------------------
knitr::opts_chunk$set(fig.width=6, 
                      fig.height=4, fig.path='Figs/',
                      echo=TRUE, warning=FALSE, message=FALSE)

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library(TeachBayes)

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sp_regions <- c(2, 1, 1, 2)

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spinner_plot(sp_regions)

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spinner_probs(sp_regions)

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spinner_data(sp_regions, nsim=20)

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sp1 <- c(1, 1, 1)
sp2 <- c(1, 2, 2, 1)
sp3 <- c(1, 1, 1, 1)
sp4 <- c(2, 2, 3, 3, 4)

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many_spinner_plots(list(sp1, sp2, sp3, sp4))

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(LIKE <- spinner_likelihoods(list(sp1, sp2, sp3, sp4)))

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dspinner(c(1, 3, 4, 1, 2), LIKE)

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bayes_df <- data.frame(Model=paste("Spinner", 1:4),
                       Prior=rep(1/4, 4), 
                       Likelihood=dspinner(c(1, 2, 1), LIKE))
(bayes_df <- bayesian_crank(bayes_df))

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prior_post_plot(bayes_df)

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beta_draw(c(10, 5))

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beta_area(.4, .6, c(10, 5))

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beta_quantile(.7, c(10, 5))

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beta_interval(.8, c(10, 5))

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beta_data(c(10, 5), nsim=20)

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beta_prior_post(c(4, 4), c(20, 10))

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normal_area(90, 105, c(100, 15))

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normal_draw(c(100, 10))

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normal_interval(.8, c(100, 10))

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normal_quantile(.3, c(100, 10))

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many_normal_plots(list(c(100, 10), c(110, 10), c(120, 10)))

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prior <- c(100, 10)
ybar <- 120
se <- 15
normal_update(prior, c(ybar, se))

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normal_update(prior, c(ybar, se), teach=TRUE)

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testing_prior(.1, .5, 5)

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draw_two_p(testing_prior(.1, .5, 5))

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prior <- testing_prior(.1, .5, 5)
(post <- two_p_update(prior, c(2, 10), c(4, 10)))

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prior <- testing_prior(.1, .5, 5)
post <- two_p_update(prior, c(2, 10), c(4, 10))
two_p_summarize(post)

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bar_plot(spinner_data(c(1, 2, 3)))

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prob_plot(data.frame(x=1:5, prob=c(.2, .3, .4, .1, .1)))