3 ms·
Linear models don’t need everything to be linear.
by epgui 3y ago
Linear models don’t need everything to be linear.
- iamcreasy 3y agoI presume you are implying that linear model only mandates linear relationship between predictor and regression coefficients?
- stdbrouw 3y agoA linear relationship between any transformation of the outcome and any transformation of the predictor variables — so the function is linear but the relationship between predictors and outcome can take on almost any shape.
- iamcreasy 3y agoAh, I missed 'the transformation of outcome' in my mind. Thanks for clearing it up.
- hackerlight 3y agoLinear models are a linear combination of possibly non-linear regressors. The linearity is strictly in the parameters, not in whatever you're adding up. A neural network can be pedantically referred to as a linear model of the form y = a + b*neural_network, for example. Here, y is a linear model (even though neural_network isn't).
- epgui 3y agoIMO it's not very pedantic... It's pretty much exactly what it is! (I'm not too sure about the example equation you give, however)