## Probability of a girl birth given placenta previa (BDA3 p. 37)

Illustrate the effect of prior and compare posterior distributions with different parameter values for the beta prior distribution.

ggplot2 is used for plotting, tidyr for manipulating data frames

``````library(ggplot2)
theme_set(theme_minimal())
library(tidyr)``````

Observed data: 437 girls and 543 boys

``````a <- 437
b <- 543``````

Evaluate densities at evenly spaced points between 0.375 and 0.525

``df1 <- data.frame(theta = seq(0.375, 0.525, 0.001))``

Posterior with Beta(1,1), ie. uniform prior

``df1\$pu <- dbeta(df1\$theta, a+1, b+1)``

3 different choices for priors

• Beta(0.488*2,(1-0.488)*2)
• Beta(0.488*20,(1-0.488)*20)
• Beta(0.488*200,(1-0.488)*200)
``````n <- c(2, 20, 200) # prior counts
apr <- 0.488 # prior ratio of success

# helperf returns for given number of prior observations, prior ratio
# of successes, number of observed successes and failures and a data
# frame with values of theta, a new data frame with prior and posterior
# values evaluated at points theta.
helperf <- function(n, apr, a, b, df)
cbind(df, pr = dbeta(df\$theta, n*apr, n*(1-apr)), po = dbeta(df\$theta, n*apr + a, n*(1-apr) + b), n = n)
# lapply function over prior counts n and gather results into key-value pairs.
df2 <- lapply(n, helperf, apr, a, b, df1) %>% do.call(rbind, args = .) %>%
gather(grp, p, -c(theta, n), factor_key = T)
# add correct labels for plotting
df2\$title <- factor(paste0('alpha/(alpha+beta)=0.488, alpha+beta=',df2\$n))
levels(df2\$grp) <- c('Posterior with unif prior', 'Prior', 'Posterior')``````

Plot distributions

``````ggplot(data = df2) +
geom_line(aes(theta, p, color = grp)) +
geom_vline(xintercept = 0.488, linetype = 'dotted') +
facet_wrap(~title, ncol = 1) +
labs(x = '', y = '') +
scale_y_continuous(breaks = NULL) +
theme(legend.position = 'bottom', legend.title = element_blank())``````

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