Regression - Save Variable(s) - Propensity Weight

Creates a new variable containing the Propensity Weights for each case in the data from a model that is a binary classifier (e.g., a Machine Learning or Regression model that has an outcome variable with only two categories such as a Binary Logit regression).

Technical details

Assume the outcome variable [math]\displaystyle{ Y }[/math] is an ordinal or nominal variable that has two categories (classes) labelled 0 and 1, with 1 being the label for the positive class. The propensity score is calculated based off the estimated probabilities of being in the positive class [math]\displaystyle{ \widehat{p}(x) = P(Y = 1 |X = x) }[/math] where [math]\displaystyle{ X }[/math] denotes all the predictor variables used in the Regression or Machine Learning model and [math]\displaystyle{ x }[/math] being the observed predictors for each case. Then the propensity weight is calculated based off the observed category each case belongs to in the outcome variable. Cases with [math]\displaystyle{ Y = 1 }[/math] are allocated a weight of [math]\displaystyle{ 1/\widehat{p}(x) }[/math] while cases with [math]\displaystyle{ Y = 0 }[/math] are allocated a weight of [math]\displaystyle{ 1/(1 - \widehat{p}(x)) }[/math].

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OR

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Customizing the QScript

This QScript is written in JavaScript and can be customized by copying and modifying the JavaScript.

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JavaScript

includeWeb('QScript R Output Functions');
let required_input_type = 'Binary Classification model (Logistic regression or Machine Learning ' +
                          'classification model with a binary outcome variable)';
let question_type = 'Number';
let is_weight = true;
let variable_name = 'propensity.weight';
let allowed_r_classes = ['BinaryLogitRegression', 'MultinomialLogitRegression', 'DeepLearning',
                         'SupportVectorMachine', 'GradientBoost', 'LDA', 'CART'];
saveVariables('Propensity weights', required_input_type, 'PropensityWeights(', ')',
              question_type, is_weight, variable_name, allowed_r_classes);