Regression - Save Variable(s) - Predicted Values

Creates a new variable containing predicted values for each case in the data from a regressionregression. Observations with missing values in the predictors are not predicted.

Output

Running this QScript will produce a new variablevariable, which will appear in your data setdata set for use in further analyses. For a categorical outcome (Regression - Ordered Logit, Regression - Binary Logit, Regression - Multinomial Logitordered logit, binary logit, multinomial logit) the variable will be categorical containing the predicted category for each observation.

Technical details

Uses the predict method from R. Unlike the defaults in most R packages, this implementation assigns predicted values for observations not in the estimation sample. Categorical variables are predicted as discrete (e.g., in Regression - Binary Logitbinary logit, the predicted values are of the two categories). See Regression - Save Variable(s) - Fitted ValuesSave fitted values for non-discrete value predictions.

Code

var __webpack_modules__ = ({});
// The module cache
var __webpack_module_cache__ = {};

// The require function
function __webpack_require__(moduleId) {

// Check if module is in cache
var cachedModule = __webpack_module_cache__[moduleId];
if (cachedModule !== undefined) {
return cachedModule.exports;
}
// Create a new module (and put it into the cache)
var module = (__webpack_module_cache__[moduleId] = {
exports: {}
});
// Execute the module function
__webpack_modules__[moduleId](module, module.exports, __webpack_require__);

// Return the exports of the module
return module.exports;

}

// webpack/runtime/rspack_version
(() => {
__webpack_require__.rv = () => ("1.7.2")
})();
// webpack/runtime/rspack_unique_id
(() => {
__webpack_require__.ruid = "bundler=rspack@1.7.2";
})();
includeWeb('QScript R Output Functions');

saveVariables('Predicted values', 'Regression', 'predict(', ')', null, null, 'pred.val', 'Regression');