Nothing
#Server functions for module 2 step 2
c(
######### Display results (graph) #########
get_bernoulli_data <- function(){
probs <- seq(0, 1, length.out = 11)
n <- 1000
out <- lapply(probs, function(p) rbinom(n=n, size=1, prob=p))
dat <- data.frame("p" = probs,
"mu" = sapply(out, mean),
"v" = sapply(out, var))
return(dat)
},
# Graph: mean-variance relationship in Bernoulli distribution
output$Mod2Step2_plot_bernoulli_mean <- renderPlot({
dat <- get_bernoulli_data()
ggplot2::ggplot(data=dat, aes(x=factor(p), y=mu)) +
ggplot2::geom_col() +
xlab("Probability") +
ylab("Mean")
}),
output$Mod2Step2_plot_bernoulli_var <- renderPlot({
dat <- get_bernoulli_data()
ggplot2::ggplot(data=dat, aes(x=factor(p), y=v)) +
ggplot2::geom_col() +
xlab("Probability") +
ylab("Variance")
}),
get_poisson_data <- function(){
lambdas <- c(0.5, 1, 3, 5, 7, 10, 20, 50, 75, 100)
n <- 1000
out <- lapply(lambdas, function(l) rpois(n=n, lambda=l))
dat <- data.frame("lambda" = lambdas,
"mu" = sapply(out, mean),
"v" = sapply(out, var))
return(dat)
},
# Graph: mean-variance relationship in Poisson distribution
output$Mod2Step2_plot_poisson_mean <- renderPlot({
dat <- get_poisson_data()
ggplot2::ggplot(data=dat, aes(x=factor(lambda), y=mu)) +
ggplot2::geom_col() +
xlab("Lambda") +
ylab("Mean")
}),
output$Mod2Step2_plot_poisson_var <- renderPlot({
dat <- get_poisson_data()
ggplot2::ggplot(data=dat, aes(x=factor(lambda), y=v)) +
ggplot2::geom_col() +
xlab("Lambda") +
ylab("Variance")
})
) # End
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