Bernoulli model, Normal prior

{r, echo=FALSE}
inputPanel(
  sliderInput("p", label = "Probability (true)",
              min = 0, max = 1, value = 0.3, step = 0.05),
  sliderInput("n", label = "Sample size",
              min = 1, max = 1000, value = 10, step = 10),
  sliderInput("mu", label = "Normal prior mean",
              min = -10, max = 10, value = 0, step = 1),
  sliderInput("sig2", label = "Normal prior variance",
              min = 0.001, max = 100, value = 1, step = 10),
  radioButtons("scale", label="Scale",
             c("Probability (0, 1)" = "prob",
               "Logit (-Inf, Inf)" = "logit")),
  sliderInput("seed", label = "Random seed",
              min = 0, max = 100, value = 0, step = 10)
)
renderPlot({
    par(las = 1)
    set.seed(input$seed)
    y <- rbinom(n = 1000, size = 1, p = input$p)
    pval <- seq(0.001, 0.999, by = 0.0005)
    fLik <- function(p, y)
        prod(dbinom(y, size = 1, prob = p))
    fPri <- function(p, mu, sig2, scale) {
        if (scale == "prob")
            out <- (1/(p*(1-p))) * dnorm(qlogis(p), mu, sqrt(sig2))
        if (scale == "logit")
            out <- dnorm(qlogis(p), mu, sqrt(sig2))
        out
    }
    Lik <- sapply(pval, fLik, y=y[1:input$n])
    Pri <- sapply(pval, fPri, input$mu, input$sig2, input$scale)
    Pos <- Lik * Pri
    M <- cbind(Pri=Pri/max(Pri),
        Lik=Lik/max(Lik),
        Pos=Pos/max(Pos))
    if (input$scale == "logit") {
        p <- qlogis(input$p)
        pval <- qlogis(pval)
    } else {
        p <- input$p
    }
    Col <- c("#cccccc", "#3498db", "#f39c12")
    matplot(pval, M, type = "l", 
        col=Col, lwd=2, lty=1,
        ylab = "Density", 
        xlab=ifelse(input$scale == "logit", "logit(p)","p"),
        sub=paste0("Mean = ", round(mean(y[1:input$n]), 2), " (", 
            sum(1-y[1:input$n]), " 0s & ", sum(y[1:input$n]), " 1s)"),
        main = paste0("True value = ", round(p, 2), 
            ", Posterior mode = ", round(pval[which.max(Pos)], 2)))
    abline(v = p, lwd = 2, col = "#c7254e")
    abline(v = pval[which.max(Pos)], lwd = 2, col = "#18bc9c")
    legend("topleft",lty=1, lwd=2, col=Col, bty="n",
           legend=c("Prior","Likelihood","Posterior"))
})