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

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large difference between pearson and deviance residuals

pearson deviance 7.46917 2.8423 6.85298 2.78224 I am fitting logistic regression model in SAS. What should be reason of large difference between pearson and deviance residual as mentioned above ?
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How should I check the assumption of linearity to the logit for the continuous independent variables in logistic regression analysis?

I am confused with the assumption of linearity to the logit for continuous predictor variables in logistic regression analysis. Do we need to check for the linear relationship while screening for ...
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How to calculate the probability of success of a Logistic Regression model with a single continuous predictor?

I have a logistic regression model below, predicting a dichotomous variable type from a single continuous predictor fatigue. Using the coefficients below I can obtain the increase in the odds of a ...
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Difference between modelling and testing association

I have data set with binary outcome, with 5 continuous covariates and 4 discrete covariates. I am little confused as to how I test for association, for the discrete covariates, I used a chi sq test, ...
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multivariate logistic regression

I have a quick question. I wondering to know, whether there is a step by step guide to learn multivariate logistic regression. i have a health science paper which described the procedure, but it is a ...
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Interpretation of odds ratio in logistic regression

In the study of birth weight (low or normal) of a child, the "birth term" of the child has been considered as a risk factor. The birth term has two categories: premature and full-mature.The following ...
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association between discrete variables and continuous variable

I am having little difficulty with understanding the difference between, testing association between two variables and modelling them. Say I have binary outcome x(sold, not sold), and i have all ...
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Rare event logistic regression bias: minimal example

CrossValidated has several questions on when and how to apply the rare event bias correction by King and Zeng (2001). I am looking for something different: a minimal simulation-based demonstration ...
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BIC difference for model selection when models have different (and correlated) predictors

I have a binomial dependent variable Y and two main IVs: A is categorical (5 non ordered levels) and B is continuous. A and B are collinear (I tested the effect of A on B with a linear model, and it ...
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Separation problem in GLM

So I think I have a separation problem in my logistic regression model. The response variable (that only takes 0 or 1 values) takes 99.2% of its cases as 0 and only 0.8% as 1. This is the ROC curve ...
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Different results for chi square and logistic binary regression

When I run chi square and logistic regression for the same two variables I get very different results.The logistic regression is a binary regression so I it is adjusted for any other factor: My ...
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Goodness-of-fit test in Logistic regression; which 'fit' do we want to test?

I am referring to this question and its answers: How to compare (probability) predictive ability of models developed from logistic regression? by @Clark Chong and answers/comments by @Frank Harrell. ...
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how to calculate R-squared in glm?

I came up with below for my glm analysis but I need to calculate R-squared to cite in the paper? anyone can help me with this please? summary(lrfit) Call: ...
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Discrepancy between Wald test and score test in Type-3 analysis in PROC GENMOD

My logistic regression model has a binomial response and 3 categorical predictors, A, B and C. A is binary B is ternary C is ternary The observations are clustered under a factor R. There are ...
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Predictive Probability Plot

I am running an ordinal logit model with an interaction term (ologit DGoodIdea9B i.Frame1#i.SatisfactionYES i.Gender, or robust). I was advised to draw a predictive ...
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Logistic regression is predicting all 1, and no 0

I am running an analysis on the probability of loan default using logistic regression and random forests. When I use logistic regression, the prediction is always all '1' (which means good loan). ...
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What happens to the coefficients when we switch labels (0/1) - in practice? [migrated]

I am trying to see in practice what was explained here what happens to the coefficients once labels are switched but I am not getting what is expected. Here is my attempt: I am using the example of ...
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Difference Between Discrete Time Proportional Hazards and Logistic Regression

My data consists of one row per person, per month that person was "exposed" to an event. So the month is the discrete time and the row corresponds to one "person-month". There are a few independent ...
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coefficents estimators via “stepAIC” and “Lasso-penalization”

i hope you can help me out with this one. First I split up my data set into a training and test data set. Then I used two approaches to build a logistic regression model. The first one was via ...
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how to use the likelihood ratio test for model selection in the study with several subjects

In my study, I have 30 subjects, for each subject, I use likelihood ratio test to compare two models (nested logistic regression), and I get a chi-squared value and a p value like the result shown ...
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Seeming Unrelated Regression (SUR) for logistic regression

I would like to perform linear hypothesis testing on whether $\beta_{11}=\beta_{21}$ in the logistic regressions \begin{align*} \text{logit}[P(Y_1=1|X)]=(\beta_{10}+\beta_{11}X)\\ ...
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Ranking of categorical variables in logistic regression

I am doing some research using logistic regression. 10 variables influence the dependent variable. One of the aforementioned is categorical (e.g., express delivery, standard delivery, etc.). Now I ...
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Need help double checking results of Binary Logistic Regression in SPSS

I'm working on some of my analysis section for my Master's Thesis in MPA, and while I have consulted my professors, I'd just like to double check with some external eyes (As obviously they are on my ...
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logistic regression with an imbalanced data set, picking threshold cut point

This question relates to whether it is a good starting point for a cut point in binary classification with logistic regression to the use the mean of the binary response variable as the initial cut ...
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Logistic regression: what happens to the coefficients when we switch the labels (0/1) of the binary outcome

How to interpret the coefficients of logistic regression? To be more specific, I have a set of independent variables, and one dependant variable (let it be "rain" or "no rain" expressed as 1 and 0 ...
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Variable correlation and collinearity in logistic regression

Hoping to get some insight on the issue of correlation/collinearity in predictors for logistic regression. Let me preface this by saying I’m no statistician, but rather a GIS analyst with exposure to ...
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Can a variable become statistically significant after the addition of another variable? [duplicate]

I am doing forward stepwise logistic regression. I have heard that its common for a previously statistically significant variable to become not statistically significant when one or more variables are ...
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Can a previously insignificant variable become significant in forward stepwise regression

I am doing forward stepwise logistic regression. I have heard that its common for a previously statistically significant variable to become not statistically significant when 1 or more variables is ...
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Logistic regression with categorical predictors

I'm trying to play around with classification models and started off with logistic regression in R. When I have all the numeric variables in the data set the model works correctly and I was able to ...
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Logistic regression coefficient too high - cannot interpret odds ratio

Question of the day: I'm running a logistic regression (results below), and I come across a coefficient that is insanely large (in absolute terms). Usually, we don't care about things like that ...
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Logistic Regression in R, coding question [duplicate]

I have done a fairly comprehensive search of this topic, forgive me if this has been answered else where. I have been building logistic regression models in R, and attempting to assess them by way ...
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Logistic Regression: Classification Table, Sensitivity and Specificity

I'm currently working with binary logistic regression in SAS to predict the probability of loan default and I have a problem with sensitivity and specificity. I have a data sample of about N=3650 ...
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Choosing between Zero Inflated Negative Binomial model versus Logistic Regression

Context: this is in the field of genome wide association studies. The norm in the field is logistic regression, but we have high quality radiographic data that gives us counts of damaged joints, so I ...
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Calling predict after training with polr on multicollinear data

Take a look at the code below. This question has been asked before but shut down - presumably for lack of R code to reproduce the problem. Basically, when there is multicollinearity in the data, ...
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Binary logistic categorical variables

I am working on determining factors having significant impact on credit repayment performance of borrowers of a bank for which i wish to run a binary logistic regression model. Dependent variable is ...
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General question on the analysis design

I have the following problem. Three hospitals of similar structure have the very different mortality rate on one certain disease. I would like to analyse the data, whether the location as a factor ...
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intrepretation of ordered logit

I am trying to analyse my experimental survey results, where people are given one of two frames at random. 1.Gain is one of the frames. The dependent variable is the difference between whether ...
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Multivariate Logistic Regression in R or SAS

I was wondering whether there is a specific procedure in either R or SAS which can handle binary correlated data (multivariate logistic regression). More specifically I have a sample of 400 ...
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Logistic Growth models for Count Data

I have a dataset of monthly ridership figures by transit route from 2007 to 2015. I am analyzing this data in R. When I go to predict on a new dataset with step increases in trips (ie 1,2,3,etc.) ...
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Comparing magnitude of coefficients in a logistic regression

I have a logistic regression: ln(p/(1-p)) = a + b * Age + c * Balance + d * Tenure Let's say that: b = 1.4 c = 2.5 d = -0.7 We can compare signs of the coefficients. I.e. right away we can say ...
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What is the general procedure or general rules for grouping factor levels?

I am attempting to build a predictive (machine-learning) logistic regression model that contains mostly categorical (non-ordinal) variables. As part of a variable selection process I run a Pearson ...
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In conditional logistic regression using a mixture model, should main effects be modelled on the same subterm as interactions?

I have a question relating to modelling interactions in a conditional logistic regression model (used for matched case-control study) of the general form $$ R_i = \alpha_{s(i)} e^{\beta_1 z_{1i}} ...
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Logistic Regression : 10 events per predictor rule

I am building a marketing model based on logistic regression. It's a customer attrition model. The event rate is very low i.e 0.1%. I have more than 1000 predictors. I know there is a rule - Minimum ...
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Degrees of freedom of $\chi^2$ in Hosmer-Lemeshow test

The test statistic for the Hosmer-Lemeshow test for goodness of fit of a logistic regression model is defined as follows: the sample is split into $d=10$ deciles, $D_1, D_2, \dots , D_{d}$, per ...
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What statistical test should I run to select “explicative” features in my dataset?

I have a database with more than 500 samples with 22 quantitative features each and I would like to predict a categorical variable (0 or 1). I am trying to fit a logistic regression model and a neural ...
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How to interpret meaning of regressors in this logistic regression model?

I'm trying to understand the model in this paper where they treat the item response theory model as a form of logistic regression. In the model the probability of getting an item (question) correct ...
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178 views

Why are there huge differences in the SEs from binomial & linear regression?

I have data from a simple experiments where people put (a fixed number of) balls either to the left or to the right of them (each ball is just the same with regards to consequences of putting them to ...
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What represents the output of a logistic regression in R

I've read other similar questions on the site about logistic regression and I've read some articles/book chapters on this, but still I'm a little bit confused about that. I'll try to be as clearer as ...
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Are Decision Function and Separating Hyperplane the same?

In many machine learning algorithms such as SVM, GBM, Logistic Regression, etc., are Decision Function and Separating Hyperplane the same?
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What is a multivariate logistic regression

There are a lot paper in medical researches using multivariate logistic regression. I am wondering if the multivariate logistic regression is just the mixed effects logistic regression or something ...