10 questions linked to/from McFadden's Pseudo-$R^2$ Interpretation
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Why can't we simply use ordinary $R^2$ in logistic regression, as we do in linear regression? Domencich and McFadden seem to imply that heteroskedasticity is an issue: but I don't understand why. In ...
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### Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an alternative-...
1 vote
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### How to report Logistic Regression Pseudo-R^2 in publication or reports?

I have a Logisitc Regression model with a McFadden Pseudo-$R^2=0.7113$. Based on the answers to this question: McFadden's Pseudo-R2 Interpretation, it seems my model has a good fit. However if I ...
1 vote
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### Model Quasipoisson interpretation and validation

I am currently doing my Master thesis with evaluating my results in R. I am stuck on my analysis of my glm with quasipoisson. I am analysing influencing variables on the dormouse abundance in 2 types ...
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### Is a logit model with a pseudo-R^2 of less than 0.5 a worse model than a coin toss?

I have recently encountered the remark that if a logit model's pseudo $R^2$ is lower than $0.5$ the result is completely worthless because a coin toss is a better model. Is this interpretation correct?...
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### Presenting Logistic Regression Results (Imbalanced Data, Small Sample Size)

I have an imbalanced data set of 300 observations with an adverse event rate of 8%. I have 4 features that I believe to be relevant based on expertise in the field. I am interested only in inference (...
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### How to get the proportion variance explained by each predictor in an lmer() model?

Is there a way to get the proportion of variance explained by individual fixed effects in a mixed effects model? I thought that the partR2 package could do this, ...
1 vote
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### Why R is not significant (and very low), while all predictors are significant? [duplicate]

I used a network logistic regression to regress five predictors against a dependent. They are all significant, instead the R is not significant and it's even very low. I can understand that it may be ...