Concordance Function for Logistic Regression Models in R

gistfile1.r

# Assuming the input is a stored binomial GLM object
Concordance = function(GLM.binomial) {
  outcome_and_fitted_col = cbind(GLM.binomial$y, GLM.binomial$fitted.values)
  # get a subset of outcomes where the event actually happened
  ones = outcome_and_fitted_col[outcome_and_fitted_col[,1] == 1,]
  # get a subset of outcomes where the event didn't actually happen
  zeros = outcome_and_fitted_col[outcome_and_fitted_col[,1] == 0,]
  # Equate the length of the event and non-event tables
  if (length(ones[,1])>length(zeros[,1])) {ones = ones[1:length(zeros[,1]),]}
    else {zeros = zeros[1:length(ones[,1]),]}
  # Following will be c(ones_outcome, ones_fitted, zeros_outcome, zeros_fitted)
  ones_and_zeros = data.frame(ones, zeros)
  # initiate columns to store concordant, discordant, and tie pair evaluations
  conc = rep(NA, length(ones_and_zeros[,1]))
  disc = rep(NA, length(ones_and_zeros[,1]))
  ties = rep(NA, length(ones_and_zeros[,1]))
  for (i in 1:length(ones_and_zeros[,1])) {
    # This tests for concordance
    if (ones_and_zeros[i,2] > ones_and_zeros[i,4])
    {conc[i] = 1
     disc[i] = 0
     ties[i] = 0}
    # This tests for a tie
    else if (ones_and_zeros[i,2] == ones_and_zeros[i,4])
    {
      conc[i] = 0
      disc[i] = 0
      ties[i] = 1
    }
    # This should catch discordant pairs.
    else if (ones_and_zeros[i,2] < ones_and_zeros[i,4])
    {
      conc[i] = 0
      disc[i] = 1
      ties[i] = 0
    }
  }
  # Here we save the various rates
  conc_rate = mean(conc, na.rm=TRUE)
  disc_rate = mean(disc, na.rm=TRUE)
  tie_rate = mean(ties, na.rm=TRUE)
  return(list(concordance=conc_rate, num_concordant=sum(conc), discordance=disc_rate, num_discordant=sum(disc), tie_rate=tie_rate,num_tied=sum(ties)))
  
}

Please tell me " Assuming the input is a stored binomial GLM object" what it mean?

Hi I just testify this code and find the outcome is wrong, and I check your code, it's wrong to calculate the pairs like yours. Maybe you use a different formula, but you should calculate the percentage of the pairs, which is applied in SAS. FYI