Contents: |
Purpose / History / Requirements / Usage / Details / Missing Values / See Also / References |

*PURPOSE:*- The PRcurve macro plots the precision-recall curve, computes the area beneath it, and can find optimal points on the curve that correspond to maximized values of the F score and Matthews correlation coefficient criteria.
*HISTORY:*- The version of the PRcurve macro that you are using is displayed when you specify
**version**(or any string) as the first argument. For example:%prcurve(version, <

*other options*>)The PRcurve macro always attempts to check for a later version of itself. If it is unable to do this (such as if there is no active internet connection available), the macro issues the following message:

NOTE: Unable to check for newer version

The computations that are performed by the macro are not affected by the appearance of this message.

*Version**Update Notes*1.0 Initial coding *REQUIREMENTS:*- Base SAS
^{®}. Data required by the macro must be created by the LOGISTIC procedure in SAS/STAT^{®}. SAS/ETS^{®}software is required when**options=area**is specified. *USAGE:*- Follow the instructions on the
**Downloads**tab of this sample to save the PRcurve macro definition. Replace the text within quotation marks in the following statement with the location of the PRcurve macro definition file on your system. In your SAS program or in the SAS editor window, specify this statement to define the PRcurve macro and make it available for use:%inc "<

*location of your file containing the PRcurve macro*>";Following this statement, you can call the PRcurve macro. See the

**Results**tab for examples.The following parameters are available in the PRcurve macro:

**data=***data-set-name*- Specifies the name of a data set created by either the OUTROC= option or the CTABLE option in PROC LOGISTIC. If the available data consists only of actual responses and predicted event probabilities from a model or classifier, then use PROC LOGISTIC to create the OUTROC= data set by using the PRED= option in the ROC statement to read the predicted event probabilities and the NOFIT and OUTROC= options in the MODEL statement. If the
**data=**parameter is omitted, the data set that was last created is used. Running the macro does not change the last-created data set, which means that successive runs of the macro omitting**data=**can operate on the same input data set. **inpred=***data-set-name*- Specifies the name of a data set created by the OUT= and PRED= options in the OUTPUT statement of PROC LOGISTIC. It is ignored if
**options=optimal**and**pred=**are not also specified. **pred=***variable-name*- Specifies the name of the variable created by the PRED= option in the data set created by the OUT= option in the OUTPUT statement of PROC LOGISTIC. It is ignored if
**options=optimal**and**inpred=**are not also specified. **optvars=***variable-list*- Specifies the names of one or more variables in the data set created by the OUT= option in the OUTPUT statement of PROC LOGISTIC that will be used to identify optimal points on the PR curve. Separate multiple variable names with spaces. Variable lists, such as x1-x9, are not supported. It is ignored if
**options=optimal**,**inpred=,**and**pred=**are not also specified. **npoints=***integer*- Specifies a positive integer value representing the number of interpolated points between 0 and 1 that the macro will generate along the PR curve. The default value is
**npoints=100**. **beta=***value*- Specifies a positive value representing the parameter of the F score optimality criterion. Typical values are 0.5, 1, or 2 controlling the relative importance of precision and recall in the F score. It is ignored if
**options=optimal**is not also specified. The default value is**beta=1**. **sensdelta=***value*- Specifies a small positive value representing the minimum difference in sensitivity values between an interpolated point and a point from the
**data=**data set. The default value is**sensdelta=1e-10**. **sensinc=***value*- Specifies a small positive value that slightly increases successive sensitivity values in the
**data=**data set so that the area under the curve can be computed. The default value is**sensinc=1e-14**. **options=***list-of-options*- Specifies desired options separated by spaces. The default value is
**options=pprob area markers br ppvzero nooptimal**. Valid options are as follows:**pprob | nopprob**- Requests drawing a horizontal reference line at the observed, overall event proportion and displaying its value on the plot. This line represents the PR curve of a random, "no skill" model. Specifying
**nopprob**omits the line and value. **area | noarea**- Requests computation of the area below the PR curve and display of its value on the plot. Area computation requires SAS/ETS. Specify
**noarea**to not compute or display the area. **tl | tr | bl | br**- Specifies in which corner of the plot to display the area and event proportion, if requested.
**markers | nomarkers**- Requests the display of markers at each of the threshold points provided in the
**data=**data set. When there is a large number of threshold points, it might be desirable to specify**nomarkers**. **optimal | nooptimal**- Requests computation of the F score and Matthews correlation coefficient (MCC) at each threshold point in the
**data=**data set, and if**optvars=**is specified, it displays a table of the optimal values and corresponding values of the variables that are specified in**optvars=**. Specify**nooptimal**if computation of optimal points is not desired. **ppvzero | noppvzero**- Requests that the PR curve include a point at (0,0) when a threshold point in the
**data=**data set contains zero true positives and nonzero false positives. Specifying**noppvzero**ignores such points, resulting in the PR curve being constant (flat) from recall=0 to the first precision (PPV) of the first threshold point in which the number of true positives exceeds zero. The area, if requested, is affected by this choice.

*DETAILS:*- The Receiver Operating Characteristic (ROC) curve and the area beneath it (AUC-ROC) are perhaps the most commonly used graphic and statistic for assessing the performance of binary-response models or classifiers. However, as described by Saito and Rehmsmeier (2015) and others, the ROC curve and AUC-ROC can be misleading when the proportions of events and nonevents become very imbalanced, such as when there are very few observed events. They note that the statistics behind the ROC curve, sensitivity and specificity, are invariant to the degree of imbalance. When interest focuses on the model predicting a high proportion of true events among the predicted events, a plot involving that statistic, known as the precision or positive predictive value (PPV), can be more informative. The fact that the precision, unlike specificity, is sensitive to the degree of imbalance allows a plot of precision to more accurately reflect the measure of interest, regardless of the degree of imbalance. This is the advantage of the precision-recall (PR) curve and its area (AUC-PR).
Saito and Rehmsmeier (2015) provide a good comparison of ROC and PR curves and show that the PR curve gives a better assessment of model performance when the proportions of events and nonevents in the data are imbalanced.

Davis and Goadrich (2006) discuss how the ROC space and the PR space are related and particularly how interpolation must be done among the observed points in PR space. They note that, while linear interpolation can be used between points in ROC space, it is not the case in PR space. The PRcurve macro applies their interpolation method when plotting the PR curve and computing the area under it.

The PRcurve macro plots precision against sensitivity and computes the area under the curve (AUC-PR). It can also find optimal points on the PR curve that have maximum values on the F score and Matthews correlation coefficient (MCC). When predicted event probabilities (from

**inpred=**and**pred=**) and one or more predictor variables in the model (from**optvars=**) are specified, the macro displays a table of the optimal points based on the above optimality criteria and the predictor values corresponding to those points.The F score and MCC combine the precision and recall measures. A parameter, β, can be specified for the F score to control the relative balance of the importance of precision to recall. At the default, β=1, the F score is the harmonic mean of the two and they are treated as equally important. When β > 1 (β=2 is commonly used), more importance is given to recall. When β < 1 (β=0.5 is commonly used), precision is more important. The F score ranges between 0 and 1 and equals 1 when precision and recall both equal 1. MCC is considered by some to be a better statistic. It is the geometric mean of precision and recall and is equivalent to the phi coefficient. It ranges from -1 to 1 and equals 1 when all observations are correctly classified.

#### BY-group processing

While the PRcurve macro does not directly support BY-group processing, this capability can be provided by the RunBY macro, which can run the PRcurve macro repeatedly for each of the BY groups in your data. See the RunBY macro documentation for details about its use. Also see the example titled "BY-group processing" on the

**Results**tab.### Output data sets

Two data sets are automatically created:

**_PR**- Contains the points on the PR curve including both those from the
**data=**data set and the interpolated points that are generated by the macro. - _PROpt
- Produced if
**options=optimal**,**inpred=**,**pred=**, and**optvars=**are all specified. Contains the optimal points on the PR curve. For each, the associated values of the optimality criteria and of the variables specified in**optvars=**are given. When the**data=**data set is a CTABLE data set, the value of the threshold in the**inpred=**data set that most closely matches the threshold of the optimal point from the**data=**data set is also given.

*MISSING VALUES:*- Missing values should not appear in the
**data=**data set. Missing values are tolerated in the**inpred=**data set and**pred=**variable and do not affect the results. *SEE ALSO:*- The ROC curve, the area under the ROC curve, and comparisons of ROC curves and their areas are available in PROC LOGISTIC using the ROC and ROCCONTRAST statements. See the example in the LOGISTIC documentation. Comparison of independent ROC curves, such as curves fit to independent samples, is described in this note. Computation of optimal values on the ROC curve, using various optimality criteria, can be done using the ROCPLOT macro.
*REFERENCES:*- Davis, J., and Goadrich, M.H. 2006. "The relationship between Precision-Recall and ROC curves."
*Proceedings of the 23rd international Conference on Machine Learning*.Fawcett, T. 2004.

*ROC Graphs: Notes and Practical Considerations for Researchers*. Norwell, MA: Kluwer Academic Publishers.Saito T., and Rehmsmeier M. 2015. "The Precision-Recall Plot Is More Informative Than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets."

*PLoS ONE*. 10(3): e0118432.Williams, C.K.I. 2021. "The Effect of Class Imbalance on Precision-Recall Curves."

*Neural Computation*33(4): 853–857.

These sample files and code examples are provided by SAS Institute Inc. "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Recipients acknowledge and agree that SAS Institute shall not be liable for any damages whatsoever arising out of their use of this material. In addition, SAS Institute will provide no support for the materials contained herein.

These sample files and code examples are provided by SAS Institute Inc. "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Recipients acknowledge and agree that SAS Institute shall not be liable for any damages whatsoever arising out of their use of this material. In addition, SAS Institute will provide no support for the materials contained herein.

**EXAMPLE 1: Plot PR curve, compute AUC, and optimal points for logistic model**- The following uses the data from the example titled "Logistic Regression" in the GENMOD documentation. PROC LOGISTIC fits the logistic model, plots the ROC curve, and saves the ROC plot data in the data set OR. The PRcurve macro then plots the precision-recall (PR) curve and computes the area under the PR curve.
proc logistic data=drug; class drug; model r/n = drug x / outroc=or; run; %prcurve()

The resulting ROC plot and area under the ROC curve (AUC = 0.84) show evidence of moderately good model fit. Note that an ROC curve lying close to the diagonal line with AUC near 0.5 indicates a poorly fitting model that essentially randomly classifies observations as positive or negative. The ROC curve of a perfectly fitting model that classifies all observations correctly is a curve from (0,0) to (0,1) to (1,1) with AUC = 1.

Note that the overall proportion of positives in the data is 0.42. That indicates that the positive and negative responses are reasonably balanced, making the PR curve less necessary for assessing the model. As with the ROC curve and its AUC, the PR curve and AUC = 0.75 suggest a moderately good fitting model. Note that a PR curve lying close to the horizontal, positive proportion line with AUC equal to that proportion indicates a poor, randomly classifying model. The PR curve of a model that fits and classifies perfectly is a curve from (0,1) to (1,1) to (1,0) with AUC = 1.

The following macro call computes and identifies optimal points on the PR curve and displays their corresponding thresholds on the predicted probabilities.

%prcurve(options=optimal)

Points at the maximum F(1) score and Matthews correlation coefficient (MCC) are identified with colors on the PR curve. The achieved values on these optimality criteria and the predicted probability thresholds at which they occur are given in the legend below the plot. Note that the F score using the default parameter value, β = 1, is the harmonic mean of precision and recall.

In order to display the predictor values corresponding to the optimal PR points, the data set containing predicted probabilities under the fitted model must be provided in

**inpred=**. The variable containing the predicted probabilities is specified in**pred=**. Also, specify some or all of the predictor variables to display in**optvars=**. The following statements refit the model, save the necessary data sets, and repeat the previous plot:proc logistic data=drug; class drug; model r/n = drug x / outroc=or; output out=preds pred=p; run; %prcurve(data=or, inpred=preds, pred=p, options=optimal, optvars=drug x)

In addition to repeating the plot above, the following table of optimal points is displayed.

Optimal Points on the P-R Curve

Optimality

StatisticOptimality

Valuedrug x Max F(1) 0.74074 B 0.78 Max MCC 0.54406 D 0.34 **EXAMPLE 2: PR curve for data from previous model or classifier**- If a model or classifier has already been used providing a data set of actual responses and predicted event probabilities, you can use PROC LOGISTIC to create the OUTROC= data set needed by the PRcurve macro. Specify the variable containing the predicted event probabilities in the PRED= option in the ROC statement. In the MODEL statement, specify the value representing the event of interest in the EVENT= option following the response variable. Also specify the NOFIT and OUTROC= options. No predictors need to be specified.
The following simple example illustrates how this is done:

data x; input p y; datalines; 0.9 1 0.8 1 0.7 0 0.6 1 0.55 1 0.54 1 0.53 0 0.52 0 0.51 1 0.505 0 ; proc logistic data=x; model y(event='1')= / nofit outroc=xor; roc pred=p; run; %prcurve()

Below are the ROC and PR curves for these data.

The same can be done for a model fit by another SAS procedure that uses any modeling method appropriate for a binary response and provides a data set of the predicted event probabilities and actual responses. The ROC curve and AUC can be obtained as illustrated in this note. The following produces the PR curve for the Generalized Additive Model (GAM) example shown in that note. Notice that the OUTROC= option is added so that the data set can be used in the PRcurve macro. The OUT= data set from the GAM procedure is specified in

**inpred=**to obtain optimal points on the PR curve and the corresponding predictor values.proc gam data=Kyphosis; model Kyphosis(event="1") = spline(Age ,df=3) spline(StartVert,df=3) spline(NumVert ,df=3) / dist=binomial; output out=gamout predicted; run; proc logistic data=gamout; model Kyphosis(event="1") = / nofit outroc=gamor; roc "GAM model" pred=P_Kyphosis; run; %prcurve(data=gamor, inpred=gamout, pred=P_Kyphosis, optvars=age startvert numvert, options=optimal nomarkers)

Below is the ROC produced by PROC LOGISTIC and the PR curve from the PRcurve macro. Notice that the proportion of events (positive proportion) is 0.2169. Optimal points on the PR curve, identified by values on the three predictors in the generalized additive model, are provided following the plots.

Optimal Points on the P-R Curve

Optimality

StatisticOptimality

ValueAge StartVert NumVert Max F(1) 0.72340 82 14 5 Max MCC 0.65805 130 1 4 The point on the above PR curve that is optimal under the F score criterion assumes equal importance of precision (PPV) and recall (sensitivity). That is the implication of the default β = 1 parameter for the F(β) score. With β = 1, the F score computed at each threshold is the harmonic mean of precision and recall. If, however, recall is considered more important, then a larger value of β can be used. Conversely, if precision is considered more important, a smaller value of β can be used. The following finds the optimal points, under the F score criterion, for β = 0.5 and 2. For both cases, the predictor values associated with the optimal points could be identified by also specifying

**inpred=**,**pred=**,**optvars=**, and**options=optimal**. Note that there is no corresponding parameter for the MCC criterion, so the optimal point under that criterion does not change.%prcurve(data=gamor, options=optimal nomarkers, beta=0.5) %prcurve(data=gamor, options=optimal nomarkers, beta=2)

Notice that the optimal point with maximum value for F(0.5) has moved to a value with considerably larger precision. However, the point optimal under F(2) did not move from the point found under the default F(1) criterion – notice the identical threshold value, 0.5699, associated with the F(1) and F(2) maxima. That threshold has a fairly large recall value under both criteria.

**EXAMPLE 3: Compare ROC and PR curves with balanced and unbalanced data**- This example illustrates the invariance of the ROC curve to the level of imbalance among events and nonevents in the data. This invariance makes the ROC curve and AUC misleading in cases where the imbalance is strong, such as when the event is rare in the population.
The following statements generate balanced and unbalanced data sets with a predictor, X, that moderately separates the events and nonevents. The balanced data set contains 2,000 observations with equal numbers of events and nonevents. Another data set of 2,000 observations is created in which only 5% of the observations are events.

data bal; do i=1 to 1000; y=1; x=rannor(2315)+0.5; output; end; do i=1 to 1000; y=0; x=rannor(2315); output; end; run; data unbal; do i=1 to 50; y=1; x=rannor(2315)+0.5; output; end; do i=1 to 1000; y=0; x=rannor(2315); output; end; run;

These statements fit the model to the balanced data and produce the ROC and PR curves. The

**nomarkers**option is used since there are a large number of points in the OUTROC= data sets. The**tr**option is used to move the positive proportion and area information to the top right corner of the plot.proc logistic data=bal; model y(event="1")=x / outroc=or; run; %prcurve(options=nomarkers tr)

Note that for these balanced data, both curves and the areas under them give similar results. Both suggest a moderate model performance.

Balanced Data, Moderate Separation These statements fit the model to the unbalanced data:

proc logistic data=unbal; model y(event="1")=x / outroc=or; run; %prcurve(options=nomarkers tr)

For the unbalanced data, note that the ROC curve and AUC are little changed. However, the PR curve differs dramatically from the PR curve for the balanced data, and the AUC has dropped from 0.63 to 0.09. That is because the precision (or positive predictive value, PPV) at each threshold on the predicted probabilities is very low. That is, the probability at each threshold of a predicted event being an actual event is low in the unbalanced data. This information is not captured by the ROC curve.

Unbalanced Data, Moderate Separation Next, balanced and unbalanced data sets are generated with the predictor providing good separation between events and nonevents. As before, both data sets contain 2,000 observations with 50% events in the balanced data set and only 5% events in the unbalanced data set.

data bal; do i=1 to 1000; y=1; x=rannor(2315)+2; output; end; do i=1 to 1000; y=0; x=rannor(2315); output; end; run; data unbal; do i=1 to 50; y=1; x=rannor(2315)+2; output; end; do i=1 to 1000; y=0; x=rannor(2315); output; end; run;

These statements produce the ROC and PR curves and areas:

proc logistic data=bal; model y(event="1")=x / outroc=or; run; %prcurve(options=nomarkers) proc logistic data=unbal; model y(event="1")=x / outroc=or; run; %prcurve(options=nomarkers tr)

Again, the ROC and PR curves and AUCs give similar results when the data are balanced, but even in this case with relatively well-separated event and nonevent populations, the two curves give very different assessments of the model performance in the unbalanced data. The ROC curve and AUC are essentially unchanged, missing the much lower proportions of true positives among the predicted positives in the unbalanced data. The PR curve reveals those lower proportions, particularly at the moderate and higher sensitivities occurring at lower thresholds on the predicted probabilities. The reduced AUC value summarizes this decrease in performance.

Balanced Data, Good Separation Unbalanced Data, Good Separation **EXAMPLE 4: Cross-validated precision-recall curve**- The following uses the cancer remission data in the example titled "Stepwise Logistic Regression and Predicted Values" in the PROC LOGISTIC documentation. A cross-validated PR curve can be produced from the CTABLE option in PROC LOGISTIC. That option uses an approximate leave-one-out cross-validation method to produce the classification tables over a range of thresholds on the predicted probabilities. Cross validation reduces the bias that results from using the same data to both fit the model and assess its performance. By saving the table produced by the CTABLE option and specifying it as the
**data=**data set in the PRcurve macro, a cross-validated PR curve is produced.The first PROC LOGISTIC step below fits a logistic model, produces the ROC curve, and saves both the ROC and CTABLE results in data sets for use in the PRcurve macro. The second PROC LOGISTIC step applies the cross-validation method to the ROC curve and produces a cross-validated ROC curve. Creating ROC curves using both validation and cross validation is further discussed in this note. The PRcurve macro is then applied to the ROC and CTABLE data to produce ordinary and cross-validated PR curves with optimal points identified.

proc logistic data=remission; model remiss(event='1') = smear blast / ctable outroc=remor; ods output classification=remct; run; proc logistic data=remission rocoptions(crossvalidate) plots(only)=roc; Crossvalidation: model remiss(event='1') = smear blast; run; %prcurve(data=remor, options=optimal) %prcurve(data=remct, options=optimal)

Notice that the cross-validated ROC curve on the right has noticeably reduced area from the ordinary ROC curve on the left. That reflects the optimistic bias resulting from using the training data to assess the model.

Similar to the ROC curves, the cross-validated PR curve has lower area as a result of the bias-reducing cross-validation method.

**EXAMPLE 5: BY-group processing**- While the PRcurve macro does not support BY processing directly, the RunBY macro can be used to run the macro on BY groups in the data. The following uses the data in the example titled "Binomial Counts in Randomized Blocks." See the description of the RunBY macro for details about its use and links to several examples. The PROC LOGISTIC step below uses the BY statement to fit separate models to the levels of the BLOCK variable in the data and saves the ROC data. The OUTROC= data set similarly contains the BLOCK variable identifying the ROC data for each block.
proc logistic data=HessianFly; by block; model y/n = entry / outroc=blockor; run;

The RunBY macro is then used to run the PRcurve macro for each block in turn. This action is done by specifying the code to be run on each BY group in a macro that you create that is named CODE. The statements below create the CODE macro containing a DATA step that includes a subsetting WHERE statement that specifies the special macro variables, _BYx and _LVLx, which are used by the RunBY macro to process each BY group. The PRcurve macro is then called to run on the subset data. The BYlabel macro variable is also used to label the displayed results from each BY group. Since the PRcurve macro writes its own titles, a FOOTNOTE statement is used instead of a TITLE statement to provide the label.

%macro code(); data byor; set blockor; where &_BY1=&_LVL1; run; footnote "Above plot for &BYlabel"; %prcurve(data=byor) %mend; %RunBY(data=blockor, by=block)

Right-click the link below and select **Save** to save the PRcurve macro definition to a file. It is recommended that you name the file **prcurve.sas**.

The commonly used ROC curve for assessing model performance can be misleading with rare event data. The precision-recall (PR) curve is more informative in such cases. The PRcurve macro plots the PR curve, computes the area under it, and finds points corresponding to the F score and MCC criteria.

#### Operating System and Release Information

Type: | Sample |

Topic: | Analytics ==> Categorical Data Analysis Analytics ==> Regression Analytics ==> Statistical Graphics |

Date Modified: | 2021-06-29 09:11:48 |

Date Created: | 2021-06-23 14:51:48 |

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