You might be wondering what motivates me spending countless weekend hours on the MOB package. The answer is plain and simple. It is users that are driving the development work.
After I published the wts_bin() function last week showing the impact of two-value weights on the monotonic binning outcome (https://statcompute.wordpress.com/2019/04/21/binning-with-weights), a question was asked if I can write a more general weighted binning function with weights being any positive value. The function wqtl_bin() is my answer (https://github.com/statcompute/MonotonicBinning/blob/master/code/wqtl_bin.R).
Below is an example demonstrating how to use the wqtl_bin() function. First of all, let’s apply the function to the case with two-value weights that was illustrated last week. As expected, statistics from both approaches are identical. In the second use case, let’s assume that weights can be any value under the Uniform distribution between 0 and 10. With positive random weights, all statistics have changed.
It is worth mentioning that, while binning rules can be the same with or without weights in some cases, it is not necessarily true in all situations, depending on the distribution of weights across the data sample. As shown in binning outcomes for “ltv” below, there are 7 bins without weights but only 5 with weights.
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| wqtl_bin(cbind(df, w = ifelse(df$bad == 1, 1, 5)), bad, tot_derog, w) | |
| #$df | |
| # bin rule cnt freq dist mv_wt bad_freq bad_rate woe iv ks | |
| #1 00 is.na($X) 213 785 0.0322 785 70 0.0892 0.6416 0.0178 2.7716 | |
| #2 01 $X <= 1 3741 16465 0.6748 0 560 0.0340 -0.3811 0.0828 18.9469 | |
| #3 02 $X > 1 & $X <= 2 478 1906 0.0781 0 121 0.0635 0.2740 0.0066 16.5222 | |
| #4 03 $X > 2 & $X <= 4 587 2231 0.0914 0 176 0.0789 0.5078 0.0298 10.6623 | |
| #5 04 $X > 4 818 3014 0.1235 0 269 0.0893 0.6426 0.0685 0.0000 | |
| #$cuts | |
| #[1] 1 2 4 | |
| wqtl_bin(cbind(df, w = runif(nrow(df), 0, 10)), bad, tot_derog, w) | |
| #$df | |
| # bin rule cnt freq dist mv_wt bad_freq bad_rate woe iv ks | |
| #1 00 is.na($X) 213 952.32 0.0325 952.32 304.89 0.3202 0.5808 0.0128 2.1985 | |
| #2 01 $X <= 1 3741 18773.11 0.6408 0.00 2943.75 0.1568 -0.3484 0.0700 17.8830 | |
| #3 02 $X > 1 & $X <= 2 478 2425.26 0.0828 0.00 604.51 0.2493 0.2312 0.0047 15.8402 | |
| #4 03 $X > 2 & $X <= 4 587 2989.80 0.1021 0.00 882.83 0.2953 0.4639 0.0249 10.4761 | |
| #5 04 $X > 4 818 4156.29 0.1419 0.00 1373.26 0.3304 0.6275 0.0657 0.0000 | |
| #$cuts | |
| #[1] 1 2 4 | |
| wqtl_bin(cbind(df, w = runif(nrow(df), 0, 10)), bad, ltv, w) | |
| #$df | |
| # bin rule cnt freq dist mv_wt bad_freq bad_rate woe iv ks | |
| #1 01 $X <= 88 1289 6448.76 0.2202 0.00 759.93 0.1178 -0.6341 0.0724 11.4178 | |
| #2 02 $X > 88 & $X <= 98 1351 6695.88 0.2286 0.00 1211.98 0.1810 -0.1306 0.0037 14.2883 | |
| #3 03 $X > 98 & $X <= 104 1126 5662.21 0.1933 0.00 1212.52 0.2141 0.0788 0.0012 12.7295 | |
| #4 04 $X > 104 & $X <= 113 1044 5277.64 0.1802 0.00 1210.91 0.2294 0.1674 0.0053 9.5611 | |
| #5 05 $X > 113 | is.na($X) 1027 5205.38 0.1777 0.93 1497.29 0.2876 0.4721 0.0451 0.0000 | |
| qtl_bin(df, bad, ltv) | |
| #$df | |
| # bin rule freq dist mv_cnt bad_freq bad_rate woe iv ks | |
| #1 01 $X <= 84 956 0.1638 0 102 0.1067 -0.7690 0.0759 9.8728 | |
| #2 02 $X > 84 & $X <= 93 960 0.1645 0 142 0.1479 -0.3951 0.0227 15.6254 | |
| #3 03 $X > 93 & $X <= 99 876 0.1501 0 187 0.2135 0.0518 0.0004 14.8359 | |
| #4 04 $X > 99 & $X <= 103 821 0.1407 0 179 0.2180 0.0787 0.0009 13.7025 | |
| #5 05 $X > 103 & $X <= 109 773 0.1324 0 178 0.2303 0.1492 0.0031 11.6401 | |
| #6 06 $X > 109 & $X <= 117 722 0.1237 0 190 0.2632 0.3263 0.0144 7.2169 | |
| #7 07 $X > 117 | is.na($X) 729 0.1249 1 218 0.2990 0.5041 0.0364 0.0000 |
Yet Another Blog in Statistical Computing 