Using functions like mean() or sd() to find single-value estimates.
sample(p_grid, size = n_samples, replace = TRUE, prob = posterior) Using functions like mean() or sd() to find
# Plot 2: sampling distribution of the mean (simulated) output$samplingPlot <- renderPlot( means <- replicate(1000, mean( if(dist == "Normal") rnorm(n, input$mean, input$sd) else if(dist == "Binomial") rbinom(n, input$size, input$prob) else if(dist == "Poisson") rpois(n, input$lambda) else rexp(n, input$rate) )) df_means <- data.frame(mean = means) ggplot(df_means, aes(x = mean)) + geom_histogram(aes(y = after_stat(density)), bins = 30, fill = "orange", alpha = 0.6) + stat_function(fun = dnorm, args = list(mean = mean(means), sd = sd(means)), color = "red") + labs(title = "Sampling Distribution of the Mean (CLT in action)", x = "Sample mean") ) size = n_samples
Author’s Note: All R code in this article is self-contained and was tested in R version 4.3.0. To run the examples, simply copy and paste into your R console or an R Markdown document. replace = TRUE