ht_single_prop_sim <- function(y, success, null, alternative,
nsim, seed, y_name,
show_var_types, show_summ_stats,
show_eda_plot, show_inf_plot, show_res){
# set seed
if(!is.null(seed)){ set.seed(seed) }
# calculate sample size
n <- length(y)
# calculate p-hat
p_hat <- sum(y == success) / n
# create null distribution
sim_dist <- rep(NA, nsim)
for(i in 1:nsim){
sim_samp <- sample(c(TRUE, FALSE), size = n, replace = TRUE, prob = c(null, 1 - null))
sim_dist[i] <- sum(sim_samp) / n
}
# shading cutoffs
if(alternative == "greater"){ x_min = p_hat; x_max = Inf }
if(alternative == "less"){ x_min = -Inf; x_max = p_hat }
if(alternative == "twosided"){
if(p_hat >= null){
x_min = c(null - (p_hat - null), p_hat)
x_max = c(-Inf, Inf)
}
if(p_hat <= null){
x_min = c(p_hat, null + (null - p_hat))
x_max = c(-Inf, Inf)
}
}
# calculate p-value
if(alternative == "greater"){ p_value <- sum(sim_dist >= p_hat) / nsim }
if(alternative == "less"){ p_value <- sum(sim_dist <= p_hat) / nsim }
if(alternative == "twosided"){
if(p_hat > null){
p_value <- min(2 * (sum(sim_dist >= p_hat) / nsim), 1)
}
if(p_hat < null){
p_value <- min(2 * (sum(sim_dist <= p_hat) / nsim), 1)
}
if(p_hat == null){ p_value <- 1 }
}
# print variable types
if(show_var_types == TRUE){
cat(paste0("Single categorical variable, success: ", success,"\n"))
}
# print summary statistics
if(show_summ_stats == TRUE){
cat(paste0("n = ", n, ", p-hat = ", round(p_hat, 4), "\n"))
}
# print results
if(show_res == TRUE){
if(alternative == "greater"){
alt_sign <- ">"
} else if(alternative == "less"){
alt_sign <- "<"
} else {
alt_sign <- "!="
}
cat(paste0("H0: p = ", null, "\n"))
cat(paste0("HA: p ", alt_sign, " ", null, "\n"))
p_val_to_print <- ifelse(round(p_value, 4) == 0, "< 0.0001", round(p_value, 4))
cat(paste0("p_value = ", p_val_to_print))
}
# eda_plot
d_eda <- data.frame(y = y)
eda_plot <- ggplot2::ggplot(data = d_eda, ggplot2::aes(x = y), environment = environment()) +
ggplot2::geom_bar(fill = "#8FDEE1") +
ggplot2::xlab(y_name) +
ggplot2::ylab("") +
ggplot2::ggtitle("Sample Distribution")
# inf_plot
d_inf <- data.frame(sim_dist = sim_dist)
inf_plot <- ggplot2::ggplot(data = d_inf, ggplot2::aes(x = sim_dist), environment = environment()) +
ggplot2::geom_histogram(fill = "#CCCCCC", binwidth = diff(range(sim_dist)) / 20) +
ggplot2::annotate("rect", xmin = x_min, xmax = x_max, ymin = 0, ymax = Inf,
alpha = 0.3, fill = "#FABAB8") +
ggplot2::xlab("simulated proportions") +
ggplot2::ylab("") +
ggplot2::ggtitle("Null Distribution") +
ggplot2::geom_vline(xintercept = p_hat, color = "#F57670", lwd = 1.5)
# print plots
if(show_eda_plot & !show_inf_plot){
print(eda_plot)
}
if(!show_eda_plot & show_inf_plot){
print(inf_plot)
}
if(show_eda_plot & show_inf_plot){
gridExtra::grid.arrange(eda_plot, inf_plot, ncol = 2)
}
# return
return(list(sim_dist = sim_dist, p_value = p_value))
}
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