Separation occurs when some classes of a categorical outcome can be perfectly distinguished by a linear combination of other variables.
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1answer
25 views
Kullback-Leibler Divergence as the class separation measure
I am trying to use KL divergence as the separation measure between the classes. I have the positive, negative samples for 2 distributions and want to adjust the algorithm parameters to get the best ...
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0answers
37 views
other causes of perfect separation
I have a dataset of 300,000 observations. Strangely enough when I use logit I get the following error:
...
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0answers
25 views
Binary logistic regression analysis, Exp(B) is .000 and no Upper CI [duplicate]
I have done and reported bivariate analyses (chi square). As can be seen in the picture, in two cases the Yes-Yes cells have count zero. For that stage that was not a problem.
However, when I try to ...
0
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0answers
9 views
how to report marginal effects of variables omitted due to perfect separation?
i have a few variables that come up as omitted in a probit model but are necessary for a later heckman model, when i write up my results what is the custom for reporting the missing coefficients in ...
1
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0answers
9 views
any problems with Firths Logit model (to deal with separation)
I am performing a Logit Model on binary variable SALE, when running on stata some of my explanatory variables were dropped as they were "perfect predictors". after some reading i believe the problem ...
1
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1answer
31 views
Zero Centering Data
Does zero centering your data make it more linearly separable or does that not affect the separability? I read that by zero-centering the data, it allows algorithms to train faster because small ...
1
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1answer
34 views
Can I run a logistic regression with a small sample size and perfect convergence?
I'm hoping someone will be able to help me with my question. I have a dataset with 71 records and 4 variables. Three of the four variables are independent, and one is dependent. All four variables are ...
2
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1answer
62 views
R/Stata Can I include a categorical variable in a Logistic regression when one of its values perfectly predicts one of the outcomes?
Suppose I have the following data
df = data.table('y'= c(1,1,1,1,1,1,1,0,0,0,0), 'x' = c(1,1,1,1,1,1,1,1,1,0,0))
where x = 0 perfectly predicts y = 0. I ...
3
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1answer
127 views
How to tell which variable is perfectly separated in R [duplicate]
I am running a logistic regression model in R using glm. I received a warning that complete separation occurred. How do I determine which variable is causing this? R doesn't tell you what variable is ...
1
vote
1answer
281 views
Why do we sum the cost function in a logistic regression?
I get the intuition behind the loss function for linear regression, which happens to be the MSE function. We have a set of points and we try to fit a line between them so that the line is at a minimum ...
1
vote
1answer
177 views
Abnormally high standard error in binary logistic regression
I did a binary logistic regression test on SPSS with a sample size of 24. The study is about the correlation of teenage pregnancy and depressive symptoms. Of the 24, 11 are pregnant teenagers. Of the ...
4
votes
1answer
279 views
Issue with complete separation in logistic regression (in R)
I am trying to fit a logistic regression model for business defaults. Apart from the dichotomous variable default, the data set includes some performance ratios.
When estimating the model in R, the ...
1
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0answers
63 views
When logits are too large in logistic regression, are statistics and p values still interpretable?
I am using glmer() in r to run a mixed logistic regression with 3 categorical (dichotomous) predictors. The outcome measure is whether or not a participant responded correctly to a memory check. This ...
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1answer
466 views
fitted probabilities in logistic regression [closed]
Is the only reason why fitted probabilities of 0 or 1 occur is that some of your predicting variables(x) are perfect linear combinations of the target(y) variable? Is there any other reason?
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1answer
115 views
How do I deal with a perfect fitted model?
I have a data set where one independent variable perfectly predicts the outcome.
Specifically, age will perfectly predict if the user will buy the product.
However, I also have a number of other ...
5
votes
2answers
174 views
How does perfect separation in logistic regression affect the AUC?
I have been working with perfect separation in logistic regression, and I have been assessing models with the AUC statistic. I was wondering what effect perfect separation has on the AUC. My own ...
0
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0answers
34 views
How to calculate a perfect separation in a d dimensional space between n class?
Assuming in a d-dimensional space,we have samples from n class.
The best way of separating samples of each class from each other is to have the samples of each class as far as possible from every ...
1
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1answer
548 views
I think my logistic model is overfitted even with Lasso? R gives me a perfect separation warning message
I have a survey with 25 variables (160 observations for each var). Most are categorical, some are continuous. The variable of interest is categorical (binomial). If I do:
...
0
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0answers
153 views
error: GEE function in R “logistic model for probability has fitted value very close to 1” [duplicate]
I am trying to fit a GEE model for repeat measure outcome (binary). But R gave me the following error information:
...
2
votes
2answers
276 views
How to identify linearly separable datasets
Usually, when I am given a dataset of $d\le 3$, I just plot the data and observe if linearly separable behaviour exists. When the dataset is of high dimensionality, I always follow a simple trick to ...
1
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0answers
108 views
Does SVM suffer from Quasi-complete separation or perfect separation?
Are SVMs, which use the hinge loss, suffer from Quasi-complete separation or perfect separation? Why or why not? Could you please give mathematical justification?
1
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2answers
46 views
Clustering Separable but Unequally Sized Clusters
I am trying to cluster the data shown below. The clusters are clearly separable. I've tried k-means and EM clustering (Gaussian mixture), however, both techniques divide the large main cluster into ...
-1
votes
1answer
387 views
Logistic Regression: p values all '1', yet model fits perfectly [duplicate]
I was trying to help a student and was foxed by this Logistic Regression problem and seek your explanation.
Here's some economic data. All we have to do is create a model to predict whether the ...
1
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1answer
641 views
Logistic Regression is a Convex Problem but my results show otherwise?
I know that logistic regression is a convex problem. Furthermore, from Lemma 1.17 in these optimization lecture notes, if a function $f : \mathbb{R}^n \rightarrow \mathbb{R}$ is convex, then the ...
7
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4answers
792 views
Why is it desirable to have linear separability in SVM?
Ref to above image, clearly a circle can separate the two classes(left image). Why then take so much pain to map it to a function to make it linearly separable (right image) ?
Can anyone please ...
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0answers
240 views
Handling errors: “fitted probabilities numerically 0 or 1 occurred”, can these be ignored/averaged out in a simulation with many trials?
I have been getting the following errors in R because of trying to fit propensity scores that are well-stratified. I am currently looping over a model simulation ...
3
votes
1answer
173 views
How can a logistic regression model have predictors that are highly insignificant and still perform excellently in the test dataset?
I have built a classification model using a highly imbalanced dataset to be found in the ROSE package of R called hacide, containing 1,000 observations of which only 2% are positive. My model ...
8
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2answers
831 views
Is it possible to simulate logistic regression without randomness?
We can simulate linear regression without randomness, which means we make $y=X\beta$ instead of $y=X\beta+\epsilon$. Then if we fit a linear model the coefficients will be identical to the "ground ...
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0answers
162 views
A mixed effect regression tool based on bayesian priors like bayesglm
I use extensively Gelman's bayesglm for the every day use due to the great stability of the estimates especially in the case of separation.
Unfortunately I could not find an equivalent of empirical ...
4
votes
1answer
63 views
Using *EDIT* non-informative priors to account for perfect separation in logistic regression?
In generating logistic regressions for treatment-survival data, perfect separation is a problem in a few of my data sets. I've decided to use a Bayesian approach to account for the perfect separation ...
5
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1answer
1k views
Is it possible to get fitted values 0 or 1 in logistic regression when the fitting algorithm converges?
We have comprehensive coverage on the topic of perfect separation in logistic regression. When it happens in R we usually see two warnings:
Warning messages:
1: glm.fit: algorithm did not ...
1
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1answer
46 views
How to use propensity scores that exhibit separation
I am using probability scoring on a data set where one variable has very small area of common support between treated and control (a small area in the middle exists). When I run a logit regression in ...
14
votes
3answers
2k views
Intuition for Support Vector Machines and the hyperplane
In my project I want to create a logistic regression model for predicting binary classification (1 or 0).
I have 15 variables, 2 of which are categorical, while the rest are a mixture of continuous ...
0
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0answers
46 views
Logistic regression (inf) [duplicate]
While doing uni-variable analysis in logistic regression where the outcome is binary and the independent variable is continuous, I got these results for two of my variables with very big SE.
Can you ...
3
votes
2answers
95 views
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 -
...
3
votes
1answer
1k views
Multicollinearity and perfect separation in logistic regression: what should I do?
I have a dataset composed of 61 variables a qualitative one y=(0 or 1) and 60 other quantitative variables and 40000 observations. I want to do logistic regression,...
3
votes
2answers
170 views
Normally distributed estimated parameters in logistic regression
I was reading the book Introduction to Statistical learning, where in z statistic was used to for hypothesis testing for parameters, so my doubt is that as response variable is not normally ...
3
votes
1answer
349 views
Does (quasi) complete separation harm prediction in logistic regression?
A client has classified geographical areas into groups. He has done this in various ways, based on substantive concerns, so there is a 2 group division, a 3 group division and so on. He has also ...
3
votes
1answer
400 views
Connections between Logistic Regression and Linear Programming
This post Testing for Linear Separability with Linear Programming in R, discusses using linear programming to test if data is linear separable.
What's the connection (if there are any) between LP ...
2
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1answer
157 views
Complete separation in logistic regression with only one direction
In this paper Dealing with Separation in Logistic Regression Models some various types of complete separation are discussed:
direction of the separation is positive if and only if $s_i = 1 \...
28
votes
4answers
5k 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.
0
votes
1answer
339 views
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 ...
0
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1answer
111 views
Are neural networks able to separate these classes?
I know that MLP can separate classes even if the decision boundary is nonlinear. However, in all cases that I've seen the boundary was just a single 'unclosed' line.
Is the MLP able to solve the ...
6
votes
4answers
1k views
When does logistic regression not work properly?
I need to find a situation in which logistic regression does not work well. Furthermore, I would like to know when a random forest might perform better than a logistic regression model.
2
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0answers
375 views
What's the reason for large beta coefficient standard error and estimate in binary logistic regression?
I'm using SPSS for binary logistic regression. The result showed that one of the variables has a large beta coefficient and standard error.
Here are some results:
Can some explain to me what is the ...
0
votes
1answer
35 views
How to pick two features that can linearly separate two classes as much as possible
I have a few dataset, each has about 120 features/dimensions and two classes (e.g. A and B) in it. Now I'd like to visualize the dataset from just two dimensions without doing any dimensionality ...
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votes
0answers
79 views
1
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0answers
55 views
Logistic plot seems wrong [duplicate]
I'm a newbie to R and am hoping someone can show me the error of my ways. I'm getting what appears to be a logistic regression and plot but I wasn't expecting the slope around 25.2 to be so steep and ...
1
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1answer
90 views
Will the p value become useless in such case: logistic regression with perfect separation?
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 ...
18
votes
1answer
2k 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 ...
