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4 votes

What is the difference between a) multilevel modelling and b) adding a categorical IV to a multiple regression?

Let's say you have a dataset of children grouped into classes. One way to analyze these data is to treat "class" as just another (categorical) independent variable - as a characteristic of ...
Graham Wright's user avatar
3 votes

What is the difference between a) multilevel modelling and b) adding a categorical IV to a multiple regression?

In the situation you describe, with a single grouping variable, practically speaking it can indeed be effectively the same when the grouping variable enters the model as a fixed effect (independent ...
Robert Long's user avatar
3 votes

Factor-smooth interactions in generalized additive models

The obvious difference would be that the two separate models wouldn't include the covariances between the two smooths. So there would be no way to statistically compare the two estimated smooths. FYI: ...
Gavin Simpson's user avatar
2 votes
Accepted

Exponential Regression dependent variable with dummy variables or numerical average of each category?

This is called a log-linear model in my field, since you have a logged outcome and an unlogged covariate. Exponential regression usually entails an untransformed outcome with $\exp()$ wrapped around a ...
dimitriy's user avatar
  • 37.3k
1 vote
Accepted

Why the contribution of a categorical value in SHAP trained on Catboost differs from observation to observation

There is a difference because the effect of the species ("target") is not independent of the other features: the GBM you have built has nontrivial (and perhaps even very complex) feature ...
Ben Reiniger's user avatar
  • 4,767
1 vote
Accepted

Testing dependence of two categorical variables with data separated by test subject

So, if we can assume that the datapoints are indeed independent, your first inclination of using a 2x2 contingency table (with $\chi^2$ or Fisher-exact) would work, and give you a valid result; it ...
jginestet's user avatar
  • 1,829
1 vote

Handling Composite Variables in Latent Class Analysis

Provided you are willing to use your composite variables as continuous/interval-scale variables, you could use latent profile analysis (LPA) instead of "classical" latent class analysis (LCA)...
Christian Geiser's user avatar
1 vote

Best subset selection with categorical data

As detailed so many times on this site, variable selection is a really bad idea. Spend time specifying a complete model rather than juggling models.
Frank Harrell's user avatar

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