Somers' D as defined for example in "The predictive accuracy of credit ratings: Measurement and statistical inference" by Walter Orth is defined for the case when Y is predicted by X as $$ D_{XY} = \frac{1}{n_u} \sum_{i=1}^n\sum_{j>i} c_{ij}, $$ where $c_{ij}$ counts 1 for concordant pairs of $(X,Y)$ and -1 for discordant pairs and 0 else. So far it all rather simple. The denominator $n_u$ is defined as $$ n_u = \sum_{i=1}^n\sum_{j>i} 1_{ \{Y_i \neq Y_j \}}. $$ Thus it counts all pairs but the ones where the Y-values are equal. Thus ties in $X$ are counted but ties in $Y$ reduce the denominator and therefore increase $D_{XY}$.

How does this carry over to the case of validation of a model? Can we just set $X = \hat{Y}$ by which I mean the model prediction and $Y$ are the realized values? Then I look at concordant and discordant pars of the prediction $\hat{Y}$ and the realization. For the denominator I don't count realization pairs $i,j$ with $Y_i = Y_j$. The author says that in such case one can not judge the performance of the prediction.

If $Y$ is a default indicator of a credit scoring model, then I would interpret $X$ as the probability of default or a risk class from my model and $Y$ the default observation. In the paper linked it is mentioned that in this case the above gives the accuracy ratio (= Gini coeffient).

My question: do you agree that a useful interpretation of Somers' D is as above: $X$ is the prediction and $Y$ the observed realization with the stated consequences? Especially, on this case ties of predictions are counted in the denominator but ties in the relization not.


Somers' $D_{xy}$ is a general measure of predictive discrimination which can handle binary, ordinal, and censored time-to-event outcome variables. In the binary $Y$ case it is just $2 (c - 0.5)$ where $c$ is the concordance probability, AKA area under the ROC curve. It is a very interpretable measure of predictive discrimination but like AUROC is not sensitive enough for choosing or comparing models.

To your question, yes we compute $D_{xy}$ by playing $\hat{Y}$ against $Y$ and using the ordinary Somers' rank correlation formula you have listed, where ties on $Y$ are ignored (not penalized against). The only difference is that the estimate of $D$ will be biased when you have overfit the data patterns. For that reason we don't concentrate on the "apparent $D_{xy}$" but rather on the overfitting-corrected version of it. For most cases this is most efficiently estimated using the bootstrap. Alternatively, 100 repeats of 10-fold cross-validation may be used.

Details are in Regression Modeling Strategies book and course notes. Start with the chapter on binary logistic regression where I cover $D_{xy}$. The R rms package validate.* functions compute apparent and validated (overfitting-corrected) $D_{xy}$.

The "not sensitive enough" comment relates to rank measures not giving a sufficient reward for extreme predictions that are "right". Sensitive measures are discussed here.

  • $\begingroup$ Thank you for your answer. Seeing it it reminds me another question: I was curious for a long time does Sommers D in NONBINARY case has geometric interpretation like area under surface or similar? Might not be good place to write it under other person question, i can write a separate question if threre some answer. $\endgroup$ – Alexander Chervov Jun 19 at 14:31
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    $\begingroup$ In the general case, where $Y$ is at least ordinal, it is still the difference between concordance and discordance probabilities, but you don't have an ROC curve interpretation. You can interpret its concordance probability by showing a scatterplot and envisioning the proportion of all possible connections between two points that have a positive slope. $\endgroup$ – Frank Harrell Jun 19 at 16:37
  • $\begingroup$ Thank you! Is there any reference for 'proportion of all possible...'? $\endgroup$ – Alexander Chervov Jun 19 at 18:21
  • $\begingroup$ Best idea for now is to look at an intro nonparametric statistics text where the Wilcoxon test is introduced (or at wikipedia). The Mann-Whitney U-statistic is the concordance probability. $\endgroup$ – Frank Harrell Jun 20 at 2:40
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    $\begingroup$ Yes. You can use different rank correlation indexes if you want to penalize differently for ties, e.g. Goodman-Kruskal $\gamma$ discards ties both on $X$ and $Y$. $\endgroup$ – Frank Harrell Jun 20 at 17:34

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