Linked Questions

0
votes
2answers
329 views

logistic regression simulation doesn't converge [duplicate]

I want to do a logistic regression simulation using R I use this code ...
0
votes
0answers
1k views

Running a Logistic regression - overflow error [duplicate]

I am running a logit regression and the "field1" is an existing list of 1's and 0's (which I am converting into a numpy list before passing as a parameter to Logit). I am trying to come up with a ...
35
votes
4answers
10k views

Why does logistic regression become unstable when classes are well-separated?

Why is it that logistic regression becomes unstable when classes are well-separated? What does well-separated classes mean? I would really appreciate if someone can explain with an example.
57
votes
1answer
14k views

Logistic regression in R resulted in perfect separation (Hauck-Donner phenomenon). Now what?

I'm trying to predict a binary outcome using 50 continuous explanatory variables (the range of most of the variables is $-\infty$ to $\infty$). My data set has almost 24,000 rows. When I run ...
20
votes
1answer
4k views

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 ...
4
votes
2answers
2k views

Adding interactions to logistic regression leads to high SEs

I am trying to test whether there is a significant interaction between an ordinal (A) and categorical variable (B) in R using <...
5
votes
1answer
1k views

Logistic regression algorithm in Ruby

I have been using R to calculate logistic regression with many independent variables for a Ruby on Rails web application. However, I can no longer import data from the database to R using RPostgreSQL. ...
0
votes
0answers
2k views

Logistic Regression with Categorical Variables: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred

In my model, I have a Response variable, 0s or 1s. I have 15 categorical variables, some of which have 150+ levels. Should I potentially exclude them from my model? When I run ...
1
vote
0answers
255 views

Benefits of Linear Discriminant analysis over Logistic Regression [duplicate]

I'm reading the introduction to statistical learning book the following reason is one of three for why LDA maybe used over Logistic Regression. However it gives no further detail or explanation and i ...
1
vote
0answers
47 views

mice: glm.fit: algorithm did not converge

I have a dataset with about 12 categorical variables with levels ranging from 2 - 10, as well as other numerical variables. About 280 records. I'm using the mice ...