R/get2SHL.R

In YandaLang/RankMeta: Rank-based Mata-analysis

#' Pooled Variance Two-sample Hodges Lehmann estimate
#' @description Calculate two-sample Hodges Lehmann estimate with pooled variance
#' @usage
#' get2SHL(dat, alpha=0.05)
#' @param dat full sample dataframe
#' @param alpha significance level
#' @author Yanda Lang, Joseph McKean
#' @examples
#' x1 <- rnorm(15)
#' x2 <- rnorm(15)
#' dat <- data.frame(x1,x2)
#' get2SHL(dat,alpha=0.05)
#' @keywords summary statistics
#' @keywords two-sample Hodges Lehmann estimate

#' @import stats Rfit
#' @export

get2SHL = function(dat,alpha=0.05){
  h = ncol(dat); K = h/2
  delta_hl2samp = rep(NA,K); tauhat_hl2samp = rep(NA,K)
  tauhatsq = rep(NA,K)
  n_1 = rep(NA,K); n_2 = rep(NA,K)

  # calculate median for ordered diff and its tau
  for(i in 1:K){
    temp1 = dat[,2*i-1]; temp2 = dat[,2*i]
    temp1 = temp1[!is.na(temp1)]; temp2 = temp2[!is.na(temp2)]
    n1 = length(temp1); n2 = length(temp2); n = n1+n2
    cr = round((n1*n2/2) - abs(qnorm(alpha/2))*sqrt(n1*n2*(n+1)/12) -.5)

    D = as.vector(outer(temp1,temp2,"-"))
    ds = sort(D)
    delta_hl2samp[i] = median(ds)
    lb = ds[cr+1]
    ub = ds[n1*n2 - cr]

    tauhat_hl2samp[i] = sqrt(n1*n2/(n-2))*((ub-lb)/(2*abs(qnorm(alpha/2))))
    tauhatsq[i] = tauhat_hl2samp[i]^2*(1/n1+1/n2)
    n_1[i] = n1; n_2[i] = n2
  }

  summaryMed = cbind(delta_hl2samp,tauhatsq,n_1,n_2)
  return(summaryMed)
}