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@jrnold
Last active December 26, 2015 08:49
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Confidence interval simulation
library("shiny")
library("ggplot2")
library("plyr")
norm_mean <- 0
norm_sd <- 1
##' Take sample from normal dist and calculate confidence interval for normal
smpl_mean_sample_ci <- function(n, p=0.95, pop_mean=0, pop_sd=1) {
smpl <- rnorm(n, norm_mean, norm_sd)
smpl_sd <- sd(smpl)
smpl_mean <- mean(smpl)
tailprob <- (1 - p) / 2
q <- -qt(tailprob, df=(n - 1), lower.tail=TRUE)
se <- smpl_sd / sqrt(n)
ci <- data.frame(ci_lb = smpl_mean - q * se,
ci_ub = smpl_mean + q * se)
ci
}
shinyServer(function(input, output) {
sample_ci <- reactive({
input$draw
mutate(rdply(input$nsamples, smpl_mean_sample_ci(input$smplsize,
input$confidence / 100)),
n = seq_len(input$nsamples),
contains_mean = ((0 > ci_lb) & (0 < ci_ub)))
})
output$plot <- renderPlot({
print(ggplot(sample_ci(), aes(x = n, ymin = ci_lb, ymax = ci_ub,
colour = contains_mean))
+ geom_hline(xintercept = 0, colour="blue")
+ geom_linerange()
+ coord_flip()
+ scale_x_continuous("samples")
+ scale_y_continuous(sprintf("%d%% CI", input$confidence))
+ scale_colour_manual(values = c("red", "black"))
+ theme(legend.position = "none",
axis.text.y = element_blank(),
axis.ticks.y = element_blank()))
})
output$npct <- renderText({
c("Percent of CI containing the population mean:", round(mean(sample_ci()$contains_mean) * 100, 2), "%")
})
})
library("shiny")
shinyUI(pageWithSidebar(
headerPanel("Confidence Intervals"),
sidebarPanel(
actionButton("draw", "Draw samples"),
numericInput("confidence", "Confidence (%):", 95, min = 1, max = 99,
step=1),
numericInput("smplsize", "Sample sizes:", 100, min = 2, max = 1000,
step=1),
numericInput("nsamples", "Number of samples:", 100, min = 1, max = 500,
step=1)
),
mainPanel(
textOutput("npct"),
plotOutput("plot"),
p("The population has a mean of 0 and a standard deviation of 1.",
"Intervals including the population mean are colored red, those not including the population mean are colored black")
)))
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