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Questions tagged [separation]

Separation occurs when some classes of a categorical outcome can be perfectly distinguished by a linear combination of other variables.

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Logistic regression with separation and nested design in R

Data: An item can be in the state 0 or 1 (binary). Each year, it starts in the state 0 and then changes to state 1. I have data of 4 seasons (2010 -2013). Each year I sampled the (same) individuals ...
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8 views

Best method for determining tolerances based on production data

Say I am producing a wound coil which is ultimately going to be used in a colpitt's oscillator circuit. I measure the Ls and Rs of the coil, and then install it in a ferrite and then test it in the ...
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2answers
185 views

GLMM for count data using square root link in lme4

I have data from a field survey. The objective of the study is to relate number of seedling (respond variable, count data), landform (exploratory variable, categorical variable with 3 levels) and ...
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1answer
35 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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39 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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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 ...
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11 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 ...
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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 ...
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1answer
36 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 ...
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1answer
53 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 ...
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1answer
65 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 ...
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1answer
230 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 ...
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1answer
333 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 ...
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1answer
236 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 ...
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1answer
531 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 ...
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66 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
760 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 ...
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2answers
199 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 ...
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39 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 from each class as far as possible from every ...
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1answer
735 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: ...
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201 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: ...
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2answers
346 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 ...
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128 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?
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2answers
54 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 ...
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1answer
500 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 ...
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1answer
995 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 ...
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4answers
915 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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280 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 ...
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1answer
207 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 ...
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2answers
885 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
197 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 ...
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1answer
67 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 ...
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1answer
2k 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 ...
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1answer
49 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 ...
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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 ...
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50 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 ...
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2answers
98 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 - ...
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1answer
2k 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,...
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2answers
187 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 ...
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1answer
390 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 ...
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1answer
428 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 ...
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1answer
171 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 \...
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4answers
6k 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.
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1answer
398 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 ...
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1answer
131 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 ...
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4answers
2k 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.
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0answers
424 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 ...
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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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83 views