fix proportion ci calc and theme

R/prop_conf.R

@@ -0,0 +1,70 @@
+
+
+# https://github.com/cran/binom/blob/master/R/binom.confint.R
+# Consider importing and crediting this code ^^
+# https://towardsdatascience.com/five-confidence-intervals-for-proportions-that-you-should-know-about-7ff5484c024f
+# https://andrewpwheeler.com/2020/11/30/confidence-intervals-around-proportions/
+#' Get a simple Clopper Pearson interval
+#'
+#' @param num
+#' @param den
+#' @param confint
+#'
+#' @return
+#' @export
+#'
+#' @examples
+clopper_pearson <- function(num, den, confint = 0.95) {
+  # Same results as binom.test in base R
+  quant <- (1 - confint) / 2
+  low <- qbeta(quant, num, den-num+1)
+  hi <- qbeta(1-quant, num+1, den-num)
+  obs <- num/den
+  return(c("low" = low, "observed" = obs,"high" = hi))
+}
+
+
+# TODO consider making a vectorized version
+# z_gap_test_v <- Vectorize(z_gap_test,
+#                           SIMPLIFY = TRUE)# we only want to return a scalar
+
+
+#z_gap_test(a_prop = 0.051, a_count = 2000, b_prop = 0.11, b_count = 100)
+
+
+#' Calculate a univariate z score by comparing to a population
+#'
+#' @param unit_prop proportion for the group we are comparing
+#' @param global_prop the global proportion
+#' @param unit_denom the population size for the group we are comparing
+#'
+#' @return a z-score
+#' @export
+#'
+#' @examples
+#' z_univariate(unit_prop = 0.13, global_prop = 0.11, unit_denom = 2500)
+z_univariate <- function(unit_prop, global_prop, unit_denom) {
+  num <- unit_prop - global_prop
+  denom <- sqrt(
+    (global_prop * (1-global_prop))/unit_denom
+  )
+  z = num / denom
+  return(z)
+
+}
+
+waldInterval <- function(x, n, conf.level = 0.95){
+  p <- x/n
+  sd <- sqrt(p*((1-p)/n))
+  z <- qnorm(c( (1 - conf.level)/2, 1 - (1-conf.level)/2)) #returns the value of thresholds at which conf.level has to be cut at. for 95% CI, this is -1.96 and +1.96
+  ci <- p + z*sd
+  names(ci) <- c('lwr', 'upr')
+  return(ci)
+}#example
+#waldInterval(x = 20, n =40) #this will return 0.345 and 0.655
+
+agresti_coull_interval <- function(num, den, conf.level) {
+  num <- num + 2
+  den <- den + 4
+
+}