#' Function for calculating d prime
#'
#' This function will calculate d prime from a vector of hits
#' and a vector of false alarms.
#'
#' This metric is common in discrimination experiments.
#' Note: If your participants are at ceiling, you may want to
#' consider another analysis.
#' @param data A data frame.
#' @param h A vector of hits (0 = miss, 1 = hit).
#' @param f A vector of false alarms (0 = correct rejection, 1 = false alarm).
#' @keywords d prime
#' @importFrom stats qnorm
#' @export
#' @examples
#' # Create some data
#' set.seed(1); library(dplyr)
#' axb <- data.frame(subj = sort(rep(1:10, each = 20, times = 10)),
#' group = gl(2, 1000, labels = c("g1", "g2")),
#' hit = c(rbinom(1000, size = c(0, 1), prob = .8),
#' rbinom(1000, size = c(0, 1), prob = .6)),
#' fa = c(rbinom(1000, size = c(0, 1), prob = .3),
#' rbinom(1000, size = c(0, 1), prob = .4))
#' )
#'
#' # Calculate d prime on entire data frame
#' dPrime(axb, hit, fa)
#'
#'
#' # Calculate d prime for each subject by group, plot it,
#' # and run a linear model
#' library(dplyr)
#' axb %>%
#' group_by(subj, group) %>%
#' summarize(dp = dPrime(., hit, fa)) %T>%
#' {
#' plot(dp ~ as.numeric(group), data = .,
#' main = "d' as a function of group", xaxt = "n",
#' xlab = "Group", ylab = "d' prime")
#' axis(1, at = 1:2, labels = c("g1", "g2"))
#' abline(lm(dp ~ as.numeric(group), data = .), col = "red")
#' } %>%
#' lm(dp ~ group, data = .) %>%
#' summary()
dPrime <- function(data, h, f){
if(!is.data.frame(data)) {
stop('I am so sorry, but this function requires a dataframe\n',
'You have provided an object of class: ', class(data)[1])
}
# Make columns of dataframe available w/o quotes
arguments <- as.list(match.call())
hits = eval(arguments$h, data)
fas = eval(arguments$f, data)
dp = qnorm(mean(hits)) - qnorm(mean(fas))
return(dp)
}
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