R/get.ng.post.df.r

In GNGTools: Tools for Go/No-Go Decision-Making Framework

#' Normal-Gamma Posterior Updating data.frame
#'
#' @param mu.0 prior mean
#' @param n.0 prior effective sample size
#' @param alpha.0 prior alpha parameter
#' @param beta.0 prior beta parameter
#' @param xbar observed sampled mean
#' @param s observed sample standard deviation
#' @param n sample size
#' @param group text string for group label
#'
#' @return Returns a data.frame with prior, data, and posterior parameters.
#' @export
#' @examples
#' my.ng.post.df <- get.ng.post.df(mu.0 = 0, n.0 = 10, alpha.0 = .25, beta.0 = 1,
#' xbar = .25, s = c(1,2,3), n = 15, group = "Control")
#' my.ng.post.df

get.ng.post.df <- function(mu.0 = 0, n.0 = 10, alpha.0 = .25, beta.0 = 1,
                           xbar = .25, s = c(1,2,3), n = 15, group = "Control"){

        data.frame(mu.0 = mu.0, n.0 = n.0, alpha.0 = alpha.0, beta.0 = beta.0,
                   xbar = xbar, s = s, n = n, group =group) %>%
                mutate(
                        # Prior NG parameters
                        mu.0 = mu.0,
                        n.0 = n.0,
                        alpha.0 = alpha.0,
                        beta.0 = beta.0,
                        # Marginal parameters of t-distribution describing mu.0
                        tdf.0 = 2 * alpha.0,
                        sigma.0 = beta.0/(alpha.0 * n.0),
                        # data
                        xbar = xbar,
                        s = s,
                        n = n,
                        # Posterior parameters
                        mu.n = (n.0 * mu.0 + n * xbar)/(n.0 + n),
                        n.n = n.0 + n,
                        alpha.n = alpha.0 + n / 2,
                        beta.n = beta.0 + (n - 1)/2 * s ^ 2 + n.0 * n * (xbar - mu.0) ^ 2/(2 * (n.0 + n)),
                        # Marginal parameters of t-distribution describing mu.n
                        tdf.n = 2 * alpha.n,
                        sigma.n = beta.n/(alpha.n * n.n)
                )
}