R/plot.irtpwrresult.R

In irtpwr: Power Analysis for IRT Models Using the Wald, LR, Score, and Gradient Statistics

#' Plot Power Curves
#'
#' Plot the power curves using the resulting object of the irtpwr function.
#'
#' @param x Object of class irtpwrresult as created by the irtpwr function.
#' @param bounds integer vector. the first entry is the lower bound of the x-axis in the plot (sample size). The second entry is the upper bound. By default, these values are chosen to cover a power range of .5 to .95.
#' @param ... additional arguments to be passed.
#'
#' @return A ggplot object
#' @export
#'
#' @examples \donttest{
#'
#' library(mirt)
#' dat <- expand.table(LSAT7)
#' mirtfit <- mirt(dat,1,verbose = FALSE)
#' hyp <- setup.hypothesis(type = '1PLvs2PL', altpars = mirtfit)
#' res <- irtpwr(hyp=hyp,alpha=.05,power =.8)
#' plot(res)
#' }
plot.irtpwrresult <- function(x, bounds = NULL, ...) {

    if (is.null(bounds)) {
        # specify some default bounds
        bounds <- c(min(calc.N(x$hyp, x$ncps, power = 0.5,
            alpha = x$alpha)), max(calc.N(x$hyp, x$ncps,
            power = 0.95, alpha = x$alpha)))
    }

    ns <- seq(bounds[1], bounds[2])
    a <- lapply(x$ncps, function(ncp) calc.power(x$hyp,
        ncp, ssize = ns, alpha = x$alpha))
    a <- do.call(rbind, a)
    dat <- data.frame(power = as.numeric(a), stat = rep(names(x$ncps),
        times = length(ns)), N = rep(ns, each = length(x$ncps)))

    pl <- ggplot2::ggplot() + ggplot2::geom_line(ggplot2::aes(x = dat$N,
        y = dat$power, color = dat$stat)) + ggplot2::xlab("Sample Size") +
        ggplot2::ylab("Power") + ggplot2::labs(color = "Statistic")



    print(pl)
    invisible(pl)

}