![]() When you run the Linear Regression procedure from the GUI, the predicted values are saved to a generic name like PRE_1. ![]() Type the value for the analysis cases (1 in this example) into the "Value" box in the "Set Rule" dialog box that opens and then click "Continue". Paste the selection variable (DATSET in this example) into the "Selection Variable" box and click the "Rule" button. The "Selection Variable" button is in the lower central area of the main Linear Regression dialog. the graphic user interface (GUI) for the procedure. The Selection Variable and value can be identified in the Linear Regression dialog box, i.e. The variable DVPRED will be added to the active data set and will contain the predicted values of DV for all cases, based on analysis of the cases with DATSET equal to 1. For example, suppose that the original analysis cases have a value of 1 for the variable DATSET, while the new application cases have DATSET = 2. If you can merge the original analysis file and the new cases into one SPSS data file, with a variable that identifies these two data sources, then you can use the /SELECT subcommand in REGRESSION to base the analysis on one set of cases but to compute predicted values on the dependent variable for all cases. The Scoring Wizard will generate these commands. The APPLYMODEL function would not work outside of those commands. Note that these scoring commands are enclosed within the set of MODEL HANDLE and MODEL CLOSE commands. The wizard lets you save the predicted value for each case and the standard error of the prediction. MODEL HANDLE NAME=regress_model FILE= 'C:\Apply_Stat_Model_results\regress_model.xml'ĬOMPUTE StandardError=APPLYMODEL(regress_model, 'STDDEV').ĬOMPUTE PredictedValue=APPLYMODEL(regress_model, 'PREDICT'). Here is an example set of scoring commands from the XML file that was generated with the above REGRESSION command: You can score the new data from the wizard or paste the corresponding syntax to a syntax window. There is a Browse button to let you identify the XML file to the wizard. ![]() SPSS Statistics, you can score in the GUI by opening the Utilities menu and clicking "Scoring Wizard". As of version 19.0, a scoring wizard isĪvailable with the client version of SPSS Statistics. In SPSS Statistics versions prior to 19.0, the XML file could only be applied in SPSS to score aįile if one was running the Server version of SPSS. Variable names, as with all example commands in this technote. Of course, you would change the file path and variable names to reflect your own folder and OUTFILE=MODEL('C:\Apply_Stat_Model_results\regress_model.xml'). In SPSS command syntax, the XML file can be saved with the /OUTFILE subcommand, as in the following REGRESSION command: Linear Regression allows you to export the model being estimated to an XML file that can be read and applied to new data files/ You can export the model from the Linear Regression GUI (Graphic User Interface) by clicking the Save button and entering the location for the new XML file in the box "Export model information to XML file". Method 1: Scoring from an XML file of the Linear Regression model If merging these data sets is not feasible, then Method 3 can be applied. If the analysis cases and application cases can be merged into 1 file, then use of the /SELECT subcommand is the simpler solution for SPSS versions prior to 19.0. If you have the original data set available or an XML file of the model had been saved when Linear Regression was run on the original data set, then the XML solution in Method 1 is the simplest solution. ![]() The third method involves the use of SPSS transformation commands to compute the predicted values based on the coefficients that were estimated by regression analysis with the first data set. It requires you to have the analysis cases and the application cases in the same SPSS data file. The second method uses the /SELECT subcommand in the REGRESSION procedure. For the client version of SPSS Statistics, this scoring method is only available in versions from 19.0 onward. The first method involves saving an XML file of the model when analyzing the first data set, then applying that XML file to score the newĭata set by running the model from the Scoring Wizard. Three methods for scoring the new data are described below.
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