GameTheoryAllocation Library in R - negative values when counting conversion values (marketing attribution with R)

Question

I am trying to recreate code from this article: http://datafeedtoolbox.com/attribution-theory-the-two-best-models-for-algorithmic-marketing-attribution-implemented-in-apache-spark-and-r/ but additionaly, I'd like to count conversion value, not only number of conversions. Here is the code from article:

shap_vals = as.data.frame(coalitions(number_of_channels)$Binary)
names(shap_vals) = c("Email","Natural_Search","Affiliates",
  "Paid_Search","Display","Social_Media")
coalition_mat = shap_vals
shap_vals[2^number_of_channels,] = Shapley_value(touch_combo_conv_rate$conv_rate, game="profit")

for(i in 2:(2^number_of_channels-1)){
  if(sum(coalition_mat[i,]) == 1){
    shap_vals[i,which(shap_vals[i,]==1)] = touch_combo_conv_rate[i,"conv_rate"]
  }else if(sum(coalition_mat[i,]) > 1){
    if(sum(coalition_mat[i,]) < number_of_channels){
      channels_of_interest = which(coalition_mat[i,] == 1)
      char_func = data.frame(rates = touch_combo_conv_rate[1,"conv_rate"])
      for(j in 2:i){
        if(sum(coalition_mat[j,channels_of_interest])>0 & 
            sum(coalition_mat[j,-channels_of_interest])==0)
          char_func = rbind(char_func,touch_combo_conv_rate[j,"conv_rate"])
      }
      shap_vals[i,channels_of_interest] = 
        Shapley_value(char_func$rates, game="profit")
    }
  }
}

Do you have some ideas what approach should I take on, to calculate conversion value properly? When I tried I had a lot of negative values. My data frame looks like this: