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

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Why can multicollinearity be a problem for logistic regression?

Let's say that I want to run a logistic regression on a dataset with n observations and p variables and I have a bad model. I can't understand why running again a logistic regression but this time ...
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22 views

Visualising developmental binary data: plot model fit or data means?

In my disciple (developmental psychology) it is becoming common to visualise data as the fitted model with CIs, rather the actual data (means with CIs). The typical example is where we are interested ...
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14 views

How can I calculate the AUC of combined variables using SPSS

thank you for taking time out to read this. I have previously ran ROC curves to get the AUCs for single test variables but I do not know how to derive the AUC for combined variables (2 test ...
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What is the difference between deviance and residual deviance in logistic regression? [duplicate]

In order to test the significance of a logistic regression, we compare the difference in the deviance: \begin{align} G&=D(\text{model without the variable)}-D(\text{model with the variable}) \\ ...
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19 views

Is there a chi-squared test for logistic regression?

I am wondering if there is a chi-squared test in logistic regression (LR) in order to measure/test the influence of one or two specific variables on the whole model. Is the LR-Test doing this job in ...
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37 views

What is the best algorithm for predicting rare events? [on hold]

I heard somewhere that logistic regression is a good candidate for this, but it doesn't work really well for me. Instead, Random Forests proved to be very efficient in my observed population. The ...
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23 views

MLE regression that accounts for two constraints

So I am wanting to create a logistic regression that simultaneously satisfies two constraints. The link here, outlines how to use the Excel solver to maximize the value of Log-Likelihood value of a ...
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16 views

Regression model selection for gross motor function analysis

I am using the Gross Motor Function Measure (https://www.canchild.ca/en/measures/gmfm.asp) to explore the relationship between age and gross motor function in a cohort of children. I have chosen ...
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16 views

Should I use logistic mixed effects? How?

I've run an experiment in which different subjects had to make a number of decisions, which are stored in the dependent boolean variable Y (0 or 1). I have multiple independed variables which may ...
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36 views

Logistic regressions questions about line fitting vs. probabilistic interperetation

Suppose I have data points $(x_1^1, x_2^1), (x_1^2, x_2^2), (x_1^3, x_2^3), \ldots$ in $\mathbf{R}^2$ that fall in one of two classes, $y^i=0$ or $y^i=1$. I can find a linear separator for these ...
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22 views

Generative-Discriminative pairs: Naive Bayes and Logistic Regression

I'm trying to understand the something written in this paper. At the bottom of page 7: This means that if the naive Bayes model $$ p(y,\mathbf{x}) = p(y) \prod_k p(x_k|y) $$ is trained to ...
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Gradient Scores from Binary Logistic Regression

My research concerns the language of Alzheimer's patients. As the disease progresses, their language becomes more concrete and less abstract - they seem to 'lose' their abstract vocabulary more ...
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24 views

Multilevel model with responses only at level 2

I have hierarchical data of individuals nested into families. For each individual, I have independent variables such as age, gender, education, and familiarity with product. For each family unit, I ...
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26 views

Evaluating logistic regression and interpretation of Hosmer-Lemeshow Goodness of Fit

As we all know, there are 2 methods to evaluate the logistic regression model and they are testing very different things Predictive power: Get a statistic that measures how well you can predict ...
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9 views

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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30 views

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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33 views

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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162 views

Rare event logistic regression bias: how to simulate the underestimated p's with a 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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17 views

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 [on hold]

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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58 views

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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45 views

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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242 views

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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36 views

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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29 views

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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30 views

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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24 views

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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31 views

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 ...