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Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression

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Longitudinal Data Modelling Strategies for Binary Outcome

For a continous outcome, ANCOVA models such a POST controlling for baseline framework: $E[Y_{i1} |X_i,Y_{i0}] = \beta_0 + \beta_1.X_i + \gamma . Y_{i0} $ or an equivalent CHANGE controlling for ...
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Research Design Using Logistic Regression After Primary Component Analysis

In the field of law, I am trying to conduct an exploratory analysis of the determinants (independent variables) of a binary event (dependent variable). And, I am examining a set of 14 independent ...
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Multinomial Logit with different choice set for each individual

I am currently trying to model a discrete choice problem using multinomial logit. The issue is that each individual faces a "different" choice set. In Stage 1 an individual is told that he can ...
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At a loss regarding feature selection vs coefficient estimation. Can you ever re-do the latter after the former?

I'm looking at a binary classification problem where p>>>n (9,000 gene expression variables for 290 patients who either have or don't have disease). I hypothesized that it would be easy to find "...
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Sufficient sampling size for logistical regression observational data

As a part of my master thesis, I'm conducting an observational study of journalists' background and the effect of this on the approach to a certain case study. I have a fixed number of observations/...
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extensions of logistic regression in the context of machine learning

I was wondering whether there exists an overview about all extensions of logisitic regression in the context of a machine learning approach. E.g. instance-based logistic rgression (Cheng and ...
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checking bi-variate relations in predictive model

Friends, I am using decision tree and logistic regression for prediction purposes (my dependent variable is a binary variable). I am just wondering whether I need to check chi-square (for categorical ...
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1answer
50 views

When was the first time that logistic regression was used to forecast an unknown outcome?

Logistic regression is originally used to predict probabilities of a binary response or further used to forecast the binary response for unknown responses based on a test data set. I was wondering ...
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Can this be solved using a binary logistic multi-level model?

Is it possible to solve the following task by using a binary logistic multi level regression? If not, how can you solve it? The concept as a diagram: I have the location of individual stores and ...
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1answer
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How to generate data for logistic regression with an independent variable that is not centred?

In this post, there is a script to generate data for a logistic regression. ...
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1answer
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Generate logistic regression error for binomial response for data simulation

I want to simulate data to test a logistic regression. The main problem I have is the error term when I generate the binomial response. Although, I know (see other posts here and here) we are ...
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1answer
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How can a variable have a positive association through logistic regression, yet a negative association through Cox regression?

I am undertaking some medical research using R. My outcome of interest is mortality in the intensive care unit. Data My data looks like this (there are ~15,000 rows). ...
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Use Logistic Regression to Predict Click Through Rate

The goal is to predict click through rate of article content. Currently, the linear regression is used and the input data set is at article level. The label is the click rate of the article. The issue ...
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1answer
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Beta hat in logistic regression?

While reviewing King & Zeng paper on rare event data in logistic regression, I'm wondering what this term means? Beta hat of the class 0.
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Can I use logistic regression to extrapolate and compare two independent variable on their outcome? [closed]

I have 1440 fecal samples and 80 sediment samples with binary outcome as yes or no positive for a bacterium e.coli. Since my sediment sample is low, can I run a logistic regression to extrapolate and ...
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1answer
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what's the difference between multinomial logistic regression and traditional regression?

Could anyone please explain to me what is the difference between multinomial logistic regression and traditional regression? I have used a method called elastic-net as the response variables are in ...
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1answer
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How can I predict logistic model? [closed]

The data is like this.(of course, I have a more data) ...
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Predicted probabilities for multiple categories of a categorical variable

I'm developing predicted probabilities for hypothetical cases in my glm (binary logit). I have a four category categorical predictor where "Single" is the reference category and "MOU", "JPA" and "AD" ...
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What are the pros and cons of different metrics for evaluating a logistic regression model?

In the data science world, I have always evaluated the performance of logistic regression models simply using ROC/AUC. However recently, I've read from some traditional statistics source about some ...
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1answer
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What explains a sudden change in the magnitude of logistic regression coefficients when increasing the sample size

Last week my team and I discovered a strange phenomenon with the coefficients of a logistic regression (LR). As we included more samples from a static dataset, the magnitude of the coefficients of the ...
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Controlling for repeated observations and clusters in logistic regression

I ran an experiment with factor X (2 levels) varied within-subjects, and factor Y (3 levels) varied between-subjects. The outcome is dichotomous. There is a control variable (Z, continuous) which I ...
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Logistic Model with Natural Response

Good Night to you all folks! I'm trying to fit a logistic regression model with a logit as link function to a set of data adding a parameter for a natural response (succes probability witout ...
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Logistic Regression fails in multiclass task using scikit-learn [closed]

I am trying to fit with scikit-learn some data using logistic regression. I have exactly three classes {1,2,3} and the distribution is {4103, 7875, 7739}. The dimension of my vectors are set to 100. ...
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Why are exponentiated logistic regression coefficients considered “odds ratios”?

Logistic regression models the log odds of an event as some set of predictors. That is, log(p/(1-p)) where p is the probability of some outcome. Thus, the interpretation of the raw logistic regression ...
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Log-loss function and divergence for extrem values [duplicate]

I was wondering how to deal with extreme values in the log-loss function. For example, if the probability of the label being 1 is null, then how to 'compute' (if implementing a gradient descent for ...
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Classification of temporal correlated data in out of fold prediction - Surprisingly high Accuracy

At the very moment, I might have awesome results or a problem. I will start with an overview about my problem setting. I have temporal correlated data (~10000 observations, ~200 features) from two ...
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23 views

Empirical Logit Plot [closed]

I need to find some theoretical and practical information on: Empirical Logits. I just have a basic idea that these are created due to probabilities being 0 and 1. We divide the observations into bins ...
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Calculating deviance on validation data

Using R, I'd like to compare three nested logistic models with a binary outcome: one with just the covariates, one with weak predictors, and one with what I think is a strong predictor. I'm using glm ...
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1answer
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Multicollinearity and predictive performance

Looking at this statement: "Multicollinearity does not affect the predictive power but individual predictor variable’s impact on the response variable could be calculated wrongly." Is this ...
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29 views

An intuitive way of understanding how logistic regression predicts on unseen combinations of variables

I'm looking for an intuitive way of explaining how logistic regression predicts on unseen combinations of variables. Consider for example I build a logistic regression model which looks at: ...
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Dependent continuous and categorial predictors in logistic regression

Given a set of variables (continuous and factors with more than two levels) I would like to show that one continuous variable (age) correlates in a certain sense with the professional status (a factor ...
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1answer
28 views

Reading the summary model from a glm logistic regression in R

I'm learning how to do logistic regression and I have some questions about verifying the model based on output in R. First below you can see the results from AIC analysis that chose the model with ...
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R GLMER with fixed and random effects including time

Suppose I have good reason to believe that values of a common lab test may allow identification of subjects with a particular genetic mutation. I want to create a model that classifies a patient into ...
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1answer
43 views

Relationship between statistical models: mcnemar test vs logistic regression

I always find useful and reliable answers in this forum and I hope this pattern will not change this time. I was spending some time to check whether an intervention program had any effect on an ...
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What happens when logistic regression does not quite capture the data? [migrated]

I have modeled the probability of an aggressive (vs indolent) form of recurrent respiratory papillomatosis as a function of age of diagnosis. Generally speaking, those who are diagnosed before the age ...
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1answer
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How to get cross-validated correlation estimate?

I have a logistic regression model trained on an outcome variable $y$ and I want to get an estimate of how good the predicted raw probabilities are in terms of aligning with $y$. I realize that ...
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Price elasticity in logistic regression with log price

I'm estimating demand and calculating price elasticity using logistic regression. In logistic regression with level price, elasticity is $$ \alpha*price*(1-share)$$ while if one uses log of price, ...
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1answer
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How to decide between additive vs. multiplicative logistic regression model?

I want to collect data in an experiment where I manipulate two treatment factors. Factor A has two possible nominal values, Factor B has three possible nominal values. The outcome is binary. ...
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Very high/low values of threshold in Logistic Regression

I am training a few logistic regression models in pyspark. Since pyspark accepts threshold as an input in the fit method I ...
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Analyzing Pre/Post Binary Outcomes - What type of model is appropriate?

I apologize for the rudimentary questions. This was not my study, and I am only tasked with analyzing the data. Background I am analyzing data on awareness of tobacco health risks before and after an ...
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Adding Feature Reduces AUC of Logistic Regression

something unexpected has come up after adding an interaction feature to a logistic regression. In a repeated k-fold cross validation,mean accuracy and AUC are much lower than without it .The variance ...
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When is it OK to calculate the AUC for a mixed-effects logistic regression model without the random intercept?

I fit a mixed-effects logistic regression model in R with glmer. There is one dependent variable, one dichotomous predictor variable, and one random intercept. The ...
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Predicted probabilities in a proportional odds model with categorical predictor

I estimate a proportional odds model in R with the polr model. The regression is basically the categorical educational achievements of parents on the categorical educational achievements of children: ...
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Modelling different types of responses to a series of drug concentrations

I have been performing dose response experiments on cancer and control patients for cell counts, apoptosis (flow cytometry DAPI-AnnexinV staining), intracellular protein staining on flow cytometry and ...
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Categorising US prescription data as 'Loading Dose' or 'Maintenance Dose'

I have a dataset of patient prescriptions that I need to label 'Loading Dose' [LD] or 'Maintenance Dose' [MD]. Only the MD part of treatment is thought effective and I need to run some models on this ...
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Error in step(Fitallreport) : number of rows in use has changed: remove missing values? [migrated]

I am trying to use a backward step regression on my data set to see which variables have the most impact one of my others. I have searched my data and there is no empty cells or NAs that I can find. I ...