R/SIGN.test.R

Defines functions print.htest_S SIGN.test.default SIGN.test

Documented in SIGN.test

#' Sign Test
#' 
#' This function will test a hypothesis based on the sign test and reports
#' linearly interpolated confidence intervals for one sample problems.
#' 
#' Computes a \dQuote{Dependent-samples Sign-Test} if both \code{x} and
#' \code{y} are provided.  If only \code{x} is provided, computes the
#' \dQuote{Sign-Test}.
#' 
#' @param x numeric vector; \code{NA}s and \code{Inf}s are allowed but will be
#' removed.
#' @param y optional numeric vector; \code{NA}s and \code{Inf}s are allowed but
#' will be removed.
#' @param md a single number representing the value of the population median
#' specified by the null hypothesis
#' @param alternative is a character string, one of \code{"greater"},
#' \code{"less"}, or \code{"two.sided"}, or the initial letter of each,
#' indicating the specification of the alternative hypothesis. For one-sample
#' tests, \code{alternative} refers to the true median of the parent population
#' in relation to the hypothesized value of the median.
#' @param conf.level confidence level for the returned confidence interval,
#' restricted to lie between zero and one
#' @param ... further arguments to be passed to or from methods
#' @return A list of class \code{htest_S}, containing the following components:
#' \item{statistic}{the S-statistic (the number of positive differences between
#' the data and the hypothesized median), with names attribute \dQuote{S}.}
#' \item{p.value}{the p-value for the test} 
#' \item{conf.int}{is a confidence interval (vector of length 2) for the true 
#' median based on linear interpolation. The confidence level is recorded in the attribute
#' \code{conf.level}. When the alternative is not \code{"two.sided"}, the
#' confidence interval will be half-infinite, to reflect the interpretation of
#' a confidence interval as the set of all values \code{k} for which one would
#' not reject the null hypothesis that the true mean or difference in means is
#' \code{k}. Here infinity will be represented by \code{Inf}.}
#' \item{estimate}{is avector of length 1, giving the sample median; this
#' estimates the corresponding population parameter. Component \code{estimate}
#' has a names attribute describing its elements.} 
#' \item{null.value}{is the value of the median specified by the null hypothesis. 
#' This equals the input argument \code{md}. Component \code{null.value} has a 
#' names attribute describing its elements.} 
#' \item{alternative}{records the value of the input argument alternative: 
#' \code{"greater"}, \code{"less"}, or \code{"two.sided"}} 
#' \item{data.name}{a character string (vector of length 1)
#' containing the actual name of the input vector \code{x}}
#' \item{Confidence.Intervals}{a 3 by 3 matrix containing the lower achieved 
#' confidence interval, the interpolated confidence interval, and the upper 
#' achived confidence interval}
#' 
#' @note The reported confidence interval is based on linear interpolation. The
#' lower and upper confidence levels are exact.
#' 
#' @section Null Hypothesis: For the one-sample sign-test, the null hypothesis
#' is that the median of the population from which \code{x} is drawn is
#' \code{md}. For the two-sample dependent case, the null hypothesis is that
#' the median for the differences of the populations from which \code{x} and
#' \code{y} are drawn is \code{md}. The alternative hypothesis indicates the
#' direction of divergence of the population median for \code{x} from \code{md}
#' (i.e., \code{"greater"}, \code{"less"}, \code{"two.sided"}.)
#' @author Alan T. Arnholt
#' @seealso \code{\link{z.test}}, \code{\link{zsum.test}},
#' \code{\link{tsum.test}}
#' @references Gibbons, J.D. and Chakraborti, S. (1992). \emph{Nonparametric
#' Statistical Inference}. Marcel Dekker Inc., New York.
#' 
#' Kitchens, L.J.(2003). \emph{Basic Statistics and Data Analysis}. Duxbury.
#' 
#' Conover, W. J. (1980). \emph{Practical Nonparametric Statistics, 2nd ed}.
#' Wiley, New York.
#' 
#' Lehmann, E. L. (1975). \emph{Nonparametrics: Statistical Methods Based on
#' Ranks}. Holden and Day, San Francisco.
#' 
#' @export
#' 
#' @examples
#' 
#' x <- c(7.8, 6.6, 6.5, 7.4, 7.3, 7., 6.4, 7.1, 6.7, 7.6, 6.8)
#' SIGN.test(x, md = 6.5)
#'         # Computes two-sided sign-test for the null hypothesis 
#'         # that the population median for 'x' is 6.5. The alternative 
#'         # hypothesis is that the median is not 6.5. An interpolated 95% 
#'         # confidence interval for the population median will be computed.
#'         
#' reaction <- c(14.3, 13.7, 15.4, 14.7, 12.4, 13.1, 9.2, 14.2, 
#'               14.4, 15.8, 11.3, 15.0)
#' SIGN.test(reaction, md = 15, alternative = "less")
#'         # Data from Example 6.11 page 330 of Kitchens BSDA.  
#'         # Computes one-sided sign-test for the null hypothesis 
#'         # that the population median is 15.  The alternative 
#'         # hypothesis is that the median is less than 15.  
#'         # An interpolated upper 95% upper bound for the population 
#'         # median will be computed.
#'         
#'
SIGN.test <- function(x, y = NULL, md = 0, alternative = "two.sided", conf.level = 0.95, ...){
  if(is.null(class(x))){
    class(x) <- data.class(x)
  }
  UseMethod("SIGN.test")
}


#' @export
SIGN.test.default <-
  function(x, y = NULL, md = 0, alternative = "two.sided", conf.level = 0.95, ...)
  {
    choices <- c("two.sided", "greater", "less")
    alt <- pmatch(alternative, choices)
    alternative <- choices[alt]
    if(length(alternative) > 1 || is.na(alternative))
      stop("alternative must be one \"greater\", \"less\", \"two.sided\"")
    if(!missing(md))
      if(length(md) != 1 || is.na(md))
        stop("median must be a single number")
    if(!missing(conf.level))
      if(length(conf.level) != 1 || is.na(conf.level) || conf.level < 0 || conf.level > 1)
        stop("conf.level must be a number between 0 and 1")
    
    if( is.null(y) )
    {
      # One-Sample Sign-Test Exact Test
      dname <- paste(deparse(substitute(x)))
      x <- sort(x)
      diff <- (x - md)
      n <- length(x)
      nt <- length(x) - sum(diff == 0)
      s <- sum(diff > 0)
      estimate <- median(x)
      method <- c("One-sample Sign-Test")
      names(estimate) <- c("median of x")
      names(md) <- "median"
      names(s) <- "s"
      CIS <- "Conf Intervals"
      if(alternative == "less")
      {
        # zobs <- (s-0.5*n)/sqrt(n*0.25)
        pval <- sum(dbinom(0:s, nt, 0.5))
        # Note: Code uses linear interpolation to arrive at the confidence intervals.
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)][1]
        
        if(k < 1)
        {
          conf.level <- (1 - (sum(dbinom(k, n, 0.5))))
          xl <- -Inf
          xu <- x[n]
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(-Inf, x[n - k + 1])
          acl1 <- (1 - (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(-Inf, x[n - k])
          acl2 <- (1 - (sum(dbinom(0:k, n, 0.5))))
          xl <- -Inf
          xu <- (((x[n - k + 1] - x[n - k]) * (conf.level - acl2))/(acl1 - acl2)) + x[n - k]
          ici <- c(xl, xu)
        }
        
      }
      else if(alternative == "greater")
      {
        pval <- (1 - sum(dbinom(0:s - 1, nt, 0.5)))
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)][1]
        
        if(k < 1)
        {
          conf.level <- (1 - (sum(dbinom(k, n, 0.5))))
          xl <- x[1]
          xu <- Inf
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(x[k], Inf)
          acl1 <- (1 - (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(x[k + 1], Inf)
          acl2 <- (1 - (sum(dbinom(0:k, n, 0.5))))
          xl <- (((x[k] - x[k + 1]) * (conf.level - acl2))/(acl1 - acl2)) + x[k + 1]
          xu <- Inf
          ici <- c(xl, xu)
        }
      }
      else
      {
        p1 <- sum(dbinom(0:s, nt, 0.5))
        p2 <- (1 - sum(dbinom(0:s - 1, nt, 0.5)))
        pval <- min(2 * p1, 2 * p2, 1)
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)/2][1]
        
        if(k < 1)
        {
          conf.level <- (1 - 2 * (sum(dbinom(k, n, 0.5))))
          xl <- x[1]
          xu <- x[n]
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(x[k], x[n - k + 1])
          acl1 <- (1 - 2 * (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(x[k + 1], x[n - k])
          acl2 <- (1 - 2 * (sum(dbinom(0:k, n, 0.5))))
          xl <- (((x[k] - x[k + 1]) * (conf.level - acl2))/(acl1 - acl2)) + x[k + 1]
          xu <- (((x[n - k + 1] - x[n - k]) * (conf.level - acl2))/(acl1 - acl2)) + x[n - k]
          ici <- c(xl, xu)
        }
        
      }
    }
    
    else
    {
      #   Paired-Samples Sign Test
      if(length(x)!=length(y))
        stop("Length of x must equal length of y")
      xy <- sort(x-y)
      diff <- (xy - md)
      n <- length(xy)
      nt <- length(xy) - sum(diff == 0)
      s <- sum(diff > 0)
      dname <-  paste(deparse(substitute(x)), " and ", deparse(substitute(y)), sep = "")
      estimate <- median(xy)
      method <- c("Dependent-samples Sign-Test")
      names(estimate) <- c("median of x-y")
      names(md) <- "median difference"
      names(s) <- "S"
      CIS <- "Conf Intervals"
      if(alternative == "less")
      {
        pval <- sum(dbinom(0:s, nt, 0.5))
        # Note: Code uses linear interpolation to arrive at the confidence intervals.
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)][1]
        
        if(k < 1)
        {
          conf.level <- (1 - (sum(dbinom(k, n, 0.5))))
          xl <- -Inf
          xu <- xy[n]
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(-Inf, xy[n - k + 1])
          acl1 <- (1 - (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(-Inf, xy[n - k])
          acl2 <- (1 - (sum(dbinom(0:k, n, 0.5))))
          xl <- -Inf
          xu <- (((xy[n - k + 1] - xy[n - k]) * (conf.level - acl2))/(acl1 - acl2)) + xy[n - k]
          ici <- c(xl, xu)
        }
        
      }
      else if(alternative == "greater")
      {
        pval <- (1 - sum(dbinom(0:s - 1, nt, 0.5)))
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)][1]
        
        if(k < 1)
        {
          conf.level <- (1 - (sum(dbinom(k, n, 0.5))))
          xl <- xy[1]
          xu <- Inf
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(xy[k], Inf)
          acl1 <- (1 - (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(xy[k + 1], Inf)
          acl2 <- (1 - (sum(dbinom(0:k, n, 0.5))))
          xl <- (((xy[k] - xy[k + 1]) * (conf.level - acl2))/(acl1 - acl2)) + xy[k + 1]
          xu <- Inf
          ici <- c(xl, xu)
        }
        
      }
      else
      {
        p1 <- sum(dbinom(0:s, nt, 0.5))
        p2 <- (1 - sum(dbinom(0:s - 1, nt, 0.5)))
        pval <- min(2 * p1, 2 * p2, 1)
        loc <- c(0:n)
        prov <- (dbinom(loc, n, 0.5))
        k <- loc[cumsum(prov) > (1 - conf.level)/2][1]
        if(k < 1)
        {
          conf.level <- (1 - 2 * (sum(dbinom(k, n, 0.5))))
          xl <- xy[1]
          xu <- xy[n]
          ici <- c(xl, xu)
        }
        else
        {
          ci1 <- c(xy[k], xy[n - k + 1])
          acl1 <- (1 - 2 * (sum(dbinom(0:k - 1, n, 0.5))))
          ci2 <- c(xy[k + 1], xy[n - k])
          acl2 <- (1 - 2 * (sum(dbinom(0:k, n, 0.5))))
          xl <- (((xy[k] - xy[k + 1]) * (conf.level - acl2))/(acl1 - acl2)) + xy[k + 1]
          xu <- (((xy[n - k + 1] - xy[n - k]) * (conf.level - acl2))/(acl1 - acl2)) + xy[n - k]
          ici <- c(xl, xu)
        }
      }
    }
    
    if(k < 1)
    {
      cint <- ici
      attr(cint, "conf.level") <- conf.level
      rval <- structure(list(statistic = s, parameter = NULL, p.value = pval,
                             conf.int = cint, estimate = estimate, null.value = md,
                             alternative = alternative, method = method, data.name = dname,
                             conf.int=cint, Confidence.Intervals = NULL ))
      class(rval) <- "htest_S"
      rval
    }
    else
    {
      result1 <- c(acl2, ci2)
      result2 <- c(conf.level, ici)
      result3 <- c(acl1, ci1)
      Confidence.Intervals <- round(as.matrix(rbind(result1, result2, result3)), 4)
      cnames <- c("Conf.Level", "L.E.pt", "U.E.pt")
      rnames <- c("Lower Achieved CI", "Interpolated CI", "Upper Achieved CI")
      dimnames(Confidence.Intervals) <- list(rnames, cnames)
      cint <- ici
      attr(cint, "conf.level") <- conf.level
      rval <- structure(list(statistic = s, parameter = NULL, p.value = pval,
                             conf.int = cint, estimate = estimate, null.value = md,
                             alternative = alternative, method = method, data.name = dname,
                             Confidence.Intervals = Confidence.Intervals))
      class(rval) <- "htest_S"
      rval
    }
  }


#' @export
print.htest_S <- function (x, digits = getOption("digits"), prefix = "\t", ...) 
{
  cat("\n")
  cat(strwrap(x$method, prefix = prefix), sep = "\n")
  cat("\n")
  cat("data:  ", x$data.name, "\n", sep = "")
  out <- character()
  if (!is.null(x$statistic)) 
    out <- c(out, paste(names(x$statistic), "=", format(signif(x$statistic, 
                                                               max(1L, digits - 2L)))))
  if (!is.null(x$parameter)) 
    out <- c(out, paste(names(x$parameter), "=", format(signif(x$parameter, 
                                                               max(1L, digits - 2L)))))
  if (!is.null(x$p.value)) {
    fp <- format.pval(x$p.value, digits = max(1L, digits - 
                                                3L))
    out <- c(out, paste("p-value", if (substr(fp, 1L, 1L) == 
                                       "<") fp else paste("=", fp)))
  }
  cat(strwrap(paste(out, collapse = ", ")), sep = "\n")
  if (!is.null(x$alternative)) {
    cat("alternative hypothesis: ")
    if (!is.null(x$null.value)) {
      if (length(x$null.value) == 1L) {
        alt.char <- switch(x$alternative, two.sided = "not equal to", 
                           less = "less than", greater = "greater than")
        cat("true ", names(x$null.value), " is ", alt.char, 
            " ", x$null.value, "\n", sep = "")
      }
      else {
        cat(x$alternative, "\nnull values:\n", sep = "")
        print(x$null.value, digits = digits, ...)
      }
    }
    else cat(x$alternative, "\n", sep = "")
  }
  if (!is.null(x$conf.int)) {
    cat(format(100 * attr(x$conf.int, "conf.level")), " percent confidence interval:\n", 
        " ", paste(format(c(x$conf.int[1L], x$conf.int[2L])), 
                   collapse = " "), "\n", sep = "")
  }
  if (!is.null(x$estimate)) {
    cat("sample estimates:\n")
    print(x$estimate, digits = digits, ...)
  }
  if(!is.null(x$Confidence.Intervals)){
    cat("\n")
    cat("Achieved and Interpolated Confidence Intervals: \n\n")
    print(x$Confidence.Intervals)
    cat("\n")
  }
  invisible(x)
}

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BSDA documentation built on July 30, 2017, 5:01 p.m.