Linked Questions

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1 answer

perfect separation logistic regression [duplicate]

in continuity to the post stepwise logistic regression non significative variables(high p-values) and as demanded by matthew this is a post explaining the data i have and the problem in fact i have a ...
prep's user avatar
  • 21
0 votes
1 answer

What does error code "outcome = variable_name > 0 predicts data perfectly" indicate when using logistic regression in Stata? [duplicate]

I have a longitudinal dataset and I am trying to create two variables that correspond to two time periods based on specific date ranges (pre- and post-) to be able to analyze the effect of each of ...
user15663873's user avatar
0 votes
0 answers

Logistic regression - Exp (B) = 0? and sig is 0.999 or 1? [duplicate]

I'm having an issue with binary logistic regression for a project I'm working on. For some of the variables, I'm receiving a significance value of 0.999 and exp(B) of 0. Is this normal?
middlebutterscotch's user avatar
1 vote
0 answers

How to detect perfect separation of logistic regression? [duplicate]

I have same error message as in this post. However all my coefficients look normal with no inflated value or standard errors. My question is how can I make sure the error message is a sign of perfect ...
yliu95's user avatar
  • 371
2 votes
1 answer

Will the p value become useless in such case: logistic regression with perfect separation? [duplicate]

I think I understand the perfect separation problem in logistic regression and answered my own question in this post from optimization perspective. Is there any intuitive explanation of why logistic ...
Haitao Du's user avatar
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0 votes
1 answer

Error message in logistic regression model [duplicate]

I am trying to understand how both temperature (factor: 6 levels - 20, 23, 26, 29, 32, 35 degrees Celsius) and species (factor: 2 levels HA and AP) affects the likelihood of moving from one life stage ...
Insect_biologist's user avatar
0 votes
0 answers

A question about odds ratio: Gaussian vs binomial regression 5 [duplicate]

I am working with a binomial dependent variable (fail=1, not fail=0), and using ratios as independent variables to predict the outcome. My dataset is n=34, so it isn't. I'm using R. When I use the ...
user avatar
50 votes
2 answers

Logistic regression model does not converge

I've got some data about airline flights (in a data frame called flights) and I would like to see if the flight time has any effect on the probability of a ...
Daniel Standage's user avatar
30 votes
1 answer

Understanding complete separation for logistic regression [duplicate]

Why does logistic regression not converge for a linearly separable data set? For linear separable data sets the model parameters go to infinity when mimizing the error function (according to ...
Matthias's user avatar
  • 343
28 votes
1 answer

Is there any intuitive explanation of why logistic regression will not work for perfect separation case? And why adding regularization will fix it?

We have many good discussions about perfect separation in logistic regression. Such as, Logistic regression in R resulted in perfect separation (Hauck-Donner phenomenon). Now what? and Logistic ...
Haitao Du's user avatar
  • 37.2k
5 votes
3 answers

Unstable logistic regression when data not well separated

There are some good answers discussing convergence issues of logistic regression when the data are well separated here and here. I am wondering what can cause convergence issues when the data are not ...
mgilbert's user avatar
  • 600
4 votes
1 answer

If logistic regression is a linear classifier why does it fail on linearly separable data?

Logistic regression is a linear model, decision boundary generated is linear. If the data points are linearly separable, then why does Logistic regression fail? Shouldn't it perform better on data ...
Coderhhz's user avatar
  • 143
8 votes
1 answer

How to describe and present the issue of perfect separation?

Folks who work with logistic regression are familiar with the issue of perfect separation: if you have a variable specific values of which are associated with only one of the two outcomes (say a ...
StasK's user avatar
  • 32k
4 votes
3 answers

Binomial GLM - non-significant difference between 100% opposite groups of observations

What follows is a basic question concerning Binomial GLM's. Suppose we have a set of observations where a binary response was measured in three different treatments, A, C and D - ...
Michael Dorman's user avatar
1 vote
1 answer

Effect Size interpretation for GLM (Logit)

I am using the following code from effectsize package in R: ...
UseR2001's user avatar

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