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VIF for Categorical Variable with More Than 2 Categories

I was having the same problem with my dataset. What I found with R is that it can handle un-labelled categorical variable also with GVIF function. It means you can keep the categorical data in a ...
yashodhar pathak's user avatar
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How to perform model comparison based on multinom( ) function of nnet package in R?

This makes sense, as it is just a likelihood ratio "chunk" test of your gender and sequence variables. The ...
Dave's user avatar
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6 votes
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Interpreting interaction term

Setup Treatment: 2 levels (treated, control) Income: 3 levels (Low, ...
Robert Long's user avatar
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How to determine probabilities that maximize likelihood in logistic regression in case of categorical variable

I guess the purpose of the linked video is to show how to deal with categorical variables in logistic regression, which is fine when you need to mix categorical and continuous variables. However, for ...
Igor F.'s user avatar
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3 votes

Fitting multiple linear regression models to select molecules for which a feature of interest significantly alters concentration

A newly edited version of this question clarifies that the interest is in identifying which among 92 proteins have differential expression between 16 normal controls and 16 patients, with the patients ...
EdM's user avatar
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2 votes

Fitting multiple linear regression models to select molecules for which a feature of interest significantly alters concentration

If I understood you research question, it seems like you’re navigating a problem involving identifying molecules that are altered between two categorical classes while controlling for a continuous ...
Robert Long's user avatar
4 votes

Is duplicating dataset an augmentation?

Duplicating the dataset without changing anything is a bad idea. It does nothing useful (no augmentation is done, no new information added), but pollutes the out-of-bag (i.e. when you randomly sample ...
Björn's user avatar
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1 vote

Is duplicating dataset an augmentation?

I don't think it's reasonable to duplicate existing data and call it data augmentation. Augmentation as I understand it involves some sort of transformation to the data (rotating images, adding noise, ...
mkt's user avatar
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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

Multiple logistic regression with ordinal predictors

If your predictors have a natural order to them, then treating them as more continuous makes more sense, and can be interpreted much like any other predictor in a regression. The problem with treating ...
Shawn Hemelstrand's user avatar
0 votes
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Multiple Linear Regression/ANOVA Help in Excel

told by someone else that I cannot run a multiple linear regression if my independent variables are constant. Indeed, there is not much you can do with this dataset (unless there is more too it than ...
Robert Long's user avatar

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