R/standard_errors.R In confintr: Confidence Intervals

Documented in se_meanse_mean_diffse_proportionse_var

```#' Standard errors
#'
#' Functions to calculate standard errors of different statistics.
#' The availability of a standard error (or statistic proportional to it) allows to
#' apply "stud" (bootstrap t) bootstrap.
#'
#' @name se
#' @param z Numeric vector.
#' @param y Numeric vector.
#' @param na.rm Should missing values be removed before calculation? Default is `TRUE`.
#' @param var.equal Should the variances be treated as being equal? Default is `FALSE`.
#' @param ... Further arguments to be passed from other methods.
#' @returns A numeric vector of length one.
#' @examples
#' se_mean(1:100)
NULL

#' @rdname se
#' @export
se_mean <- function(z, na.rm = TRUE, ...) {
if (na.rm) {
z <- z[!is.na(z)]
}
n <- length(z)
if (n < 1L) {
stop("Not enough non-missing observations to calculate standard error.")
}
sqrt(stats::var(z) / n)
}

#' @rdname se
#' @export
se_mean_diff <- function(z, y, na.rm = TRUE, var.equal = FALSE, ...) {
if (na.rm) {
z <- z[!is.na(z)]
y <- y[!is.na(y)]
}
stats::t.test(z, y, var.equal = var.equal)\$stderr
}

#' @rdname se
#' @export
se_var <- function(z, na.rm = TRUE, ...) {
# S.S. Wilks (1962), Mathematical Statistics, Wiley & Sons.
if (na.rm) {
z <- z[!is.na(z)]
}
n <- length(z)
if (n <= 3L) {
stop("Not enough non-missing observations to calculate standard error.
Need at least four.")
}
sqrt((1 / n) * (moment(z, p = 4) - (n - 3) / (n - 1) * stats::var(z)^2))
}

#' @rdname se
#' @export
se_proportion <- function(z, na.rm = TRUE, ...) {
if (na.rm) {
z <- z[!is.na(z)]
}
p <- mean(z)
n <- length(z)
if (n < 1L) {
stop("Not enough non-missing observations to calculate standard error.")
}
sqrt(p * (1 - p) / n)
}

# SE(sample standard dev)
# https://stats.stackexchange.com/questions/631/standard-deviation-of-standard-deviation
```

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confintr documentation built on June 7, 2023, 6:24 p.m.