Four ways to score compute predicted values for new observations using a previously fitted model are discussed below. Note that several conditions can make it impossible to score a new observation, resulting in a missing predicted value. These conditions are described in this note.

You can then use the SCORE statement in PROC PLM to score a data set using the saved model. This is illustrated in the example titled "Scoring with PROC PLM" in the Examples section of the PLM documentation and at the end of Example 1 below. For ordinary regression models fit using PROC REG, you can use PROC SCORE to compute predicted values for new observations. See the example titled "Regression Parameter Estimates" in the SCORE documentation.

It is not necessary to refit the model. However, PROC SCORE does not directly provide scoring for other types of models such as logistic or other generalized linear models. It also does not provide standard error estimates or confidence limits.

For a logistic or probit model, the scoring process is greatly simplified in PROC LOGISTIC. Its SCORE statement enables you to score a data set of new observations.

The FITSTAT option provides fit statistics such as the area under the ROC curve AUC and R-square beginning in SAS 9. An ROC plot and analysis for validation data can be obtained as described in this note.

The example titled "Scoring Data Sets with the SCORE Statement" in the LOGISTIC documentation illustrates the use of the SCORE statement with a nominal logistic model. See the procedure documentation for discussion and examples. See the example titled "Linear Discriminant Analysis of Remote-Sensing Data on Crops" in the DISCRIM documentation.

The CODE statement generates SAS code that can be used in a DATA step to score a data set. You can get predicted values for one or more settings of your model predictors by adding observations to the input data that you use to fit train the model.

proc genmod

The predictors in these new observations should be set to the values for which you want predicted values. For the added observations, either the response variable should be set to missing, or if the new observations have observed values then a WEIGHT variable should be created with value 1 for the training observations and value 0 or missing for the new observations.

With these new observations appended to your training data set, the fitted model should be identical to the model fit using only the training data. This is because any observation that has a missing response value or zero or missing weight is ignored when fitting the model. The exception to this is when the model includes spline effects defined in the EFFECT statement. See the Extrapolation section of this note for details.

The procedure can compute predicted values for such observations as long as they have nonmissing values for all of the model predictors and have values for CLASS predictors that existed in the training data set. This is further explained and illustrated in this note. See the procedure's documentation. Logistic Model Validation Using PROC GENMOD. Model validation often involves getting predictions for a potentially large number of observations that were held out from the original data.

That is, the original data set is split into a data set to train the model and a data set to validate the model. Validation is done by comparing the values predicted under the model to the observed values in the validation data set often called a hold-out data set.

One way this can be done is by concatenating the training and validation data sets and using the combined data set as the input data set to the modeling procedure.

It is often convenient for the output data set to contain only the validation observations, excluding the observations used to train the model. To do this, add a variable to the combined data set that indicates which observations are the training data set and which observations are the validation data set.

The following DATA step creates a SAS data set named REMISS that contains the training data for a logistic model to be fit by PROC GENMOD. This DATA step creates a validation data set, NEW. For purposes of illustration, the first eight observations of the training data set are used.

Survival analysis using SAS

The following DATA step concatenates the training and validation data sets into a single data set, BOTH, for input to PROC GENMOD. The inverse of this variable, W, is created for use as a weight variable.

W equals 1 for the training observations, 0 for the validation observations. These statements fit the model using the combined data set, BOTH. The training indicator variable, W, is used in the WEIGHT statement. The results are identical to a GENMOD analysis on just the training mumbai stock market gold set because observations in the validation data set have zero weight and are ignored in the model forex ecn broker usa process.

The OUTPUT statement produces a data set, PREDS, of predicted values. For each data set that you want to score, you would need to use this same process that involves refitting the model to the training data set. This can be avoided by using the STORE statement in PROC GENMOD and the Should i buy bbry stock statement in PROC PLM.

The following GENMOD step fits the model and the Trading futures ameritrade statement saves the model.

To score how to invest in penny stocks philippines new data set, only a PLM step is required. Two data sets NEW and NEW2 are scored in the following example. The ILINK option in the SCORE statement uses the inverse of the link function logit, in this case to obtain estimates on the mean probability scale.

Some important issues must be remembered in order to correctly and accurately compute predicted values:. Note that if the value of a CLASS variable in an observation to be yahoo forex data api does not appear in the training data set, then that observation cannot be scored. This is because, unlike a continuous predictor, there is no parameter corresponding to that value in the trained model as explained in this note and in Example 3 below.

Predicted sas genmod ods output parameterestimates can be obtained by this method, but the computations for the standard errors of the predicted values are generally more complex and cannot be computed. As a result, confidence limits for the predicted values also cannot be computed.

A Poisson Model with Offset. The following Poisson model is based on the data in the "Getting Started" section of the GENMOD documentation.

Note that the model includes a continuous variable agea CLASS variable carand their interaction. The model also includes an offset variable ln. The following statements create the training data set and fit the desired model. The XVARS and P options in the MODEL statement display the predictor values and the predicted counts Pred for the observations in the training data set, shown below. In this example, the specified model happens to be a saturated model, so the predicted values equal the actual values.

But this has no influence on the manner of scoring. Notice that the parameter estimates table was saved via the ODS OUTPUT statement. The variable containing the parameter estimates Estimate is displayed to high precision by using a FORMAT statement in the following PROC PRINT step.

These more precise values are used in the scoring computations below to more closely match what GENMOD does internally with full precision values. The following step does the scoring. In this example, the training data set is scored, so it is specified in the SET statement.

A SELECT group should appear for each predictor in the CLASS statement to create the appropriately coded design variables. This is discussed further below. By definition, the parameter associated with an offset variable equals 1. Finally, the inverse link function is applied to get a predicted mean. Since this poisson model uses the log link, the inverse link function is exponentiation that can be done with the EXP function in SAS.

For more on the various types of CLASS variable coding, see "CLASS Variable Parameterization" in the Details section of the LOGISTIC procedure documentation. The following uses data from the example titled "Logistic Modeling with Categorical Predictors" in the LOGISTIC procedure documentation. PROC GENMOD is used to fit a probit model to the data to hoodless brennan stockbrokers the probability of no pain.

Effects coding is used for the categorical predictor Treatment A, B, or P and reference coding is used for the Sex F or M with males M as the reference category.

Below are the scores predicted probabilities of no pain for the first six observations, which the scoring step below reproduces. These statements display the parameter estimates of the probit model with more precision for use in scoring. To illustrate scoring, the first six observations of the training data set are used as a validation data set.

The scores for these observations should equal the predicted values computed by the GENMOD procedure above. Two additional observations are included — one with an invalid Treatment code X and one with a missing value for Sex. The first observation cannot be scored because there is no parameter for Treatment X in the model. In order to score this point, the training data would need to include some subjects who were given Treatment X. The second observation cannot be scored since values for all predictors in the model must be nonmissing in order to make a valid computation.

See this usage note for more discussion. In the scoring step below, a SELECT group is included for each of the two categorical predictors, Treatment and Sex, using coding that matches the coding used when training the model — effects coding for Treatment and reference coding for Sex.

The "Class Level Information" table above produced by PROC GENMOD shows you how the design variables are coded.

- Scoring (computing predicted values) for new observations or a validation data set

Since the inverse of the probit link function is the probability from the standard normal distribution, you can use the PROBNORM function in SAS. Notice that the predicted probabilities for the first six observations match those computed by PROC GENMOD above, and the predicted probabilities for the last two observations are missing as expected. Note that PROC LOGISTIC can fit a probit model and also provides effects and reference coding.

Since it has built-in scoring capability via its SCORE statement, you can fit the model and score the validation data all in a single step. Any slight differences are due to minor differences in starting values and iteration methods used by GENMOD and LOGISTIC.

Scoring a model containing spline effects. See this example that discusses the types of spline transformations available in the EFFECT statement and illustrates reproducing the spline basis functions and scoring data. Scoring computing predicted values for new observations or a validation data set Contents: Scoring methods and examples 1. Use the STORE Statement and PROC PLM 2. Use Built-In Scoring Capabilities — PROC SCORE, SCORE and CODE statements PROC SCORE SCORE Statement CODE Statement 3.

Augment the Training Data Set Example 1: Logistic Model Validation Using PROC GENMOD 4. Use the Saved Parameter Estimates to Score Generalized Linear Models Example 2: A Poisson Model with Offset Example 3: A Probit Model Example 4: Scoring a model containing spline effects Four ways to score compute predicted values for new observations using a previously fitted model are discussed below.

Use Built-In Scoring Capabilities — PROC SCORE, SCORE and CODE statements Some procedures include features that make scoring new observations easier: PROC SCORE For ordinary regression models fit using PROC REG, you can use PROC SCORE to compute predicted values for new observations.

SCORE Statement For a logistic or probit model, the scoring process is greatly simplified in PROC LOGISTIC. Augment the Training Data Set You can get predicted values for one or more settings of your model predictors by adding observations to the input data that you use to fit train the model.

Logistic Model Validation Using PROC GENMOD Model validation often involves getting predictions for a potentially large number of observations that were held out from the original data. Observation Statistics Observation c ln age car Pred Xbeta Std HessWgt 1 42 6.

Treatment Sex Age Duration Pain PrNoPain P F 68 1 No 0. Treatment Sex Age Duration Pain xbeta PrNoPain P F 68 1 No Methods for applying previously-fitted models to new data are discussed including the computation of predicted values.

This content is presented in an iframe, which your browser does not support. To view the RateIT tab, click here. Obs Parameter Level1 Estimate 1 Intercept.

Class Level Information Class Value Design Variables Treatment A 1. Treatment Sex Age Duration Pain PrNoPain P. Parameter Level1 Level2 Estimate Intercept. Treatment Sex Age Duration Pain P. Treatment Sex Age Duration Pain xbeta PrNoPain P. Microsoft Windows Server Datacenter bit Edition. Microsoft Windows Server Enterprise bit Edition.