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

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Best way to account for time lags in logistic regression (GLM or GLMM)

I am trying to determine the best, most conservative way to account of time lags in a logistic regression type analysis (a generalized linear model with or without mixed effects). I am working with ...
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7 views

Comparing logistic regression models with different predictors [duplicate]

What measure do I use to compare two logistic regression models with different predictors but the same response? y ~ x y ~ z I've used lrtest and anova before ...
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9 views

High asymmetric binary variables

I have a set of binary dependent variables where most values are concentrated in one category. Which methods are adequate to analyze such data and which restrictions or difficulties are usual in this ...
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1answer
28 views

What statistical test would be appropiate for determining the preferences of lottery players?

I'm trying to conduct a simple study: to find out if there is a significant relationship between the time perspective of a person and his propensity to buy lottery. So to keep it simple, my ...
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1answer
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Statistical Modeling with the combination of two models

I'm having a modeling problem now. Assume we have discrete random variable Y and continuous random variables X and Z. First, we assume a logistic regression between Y and Z.(Assumption One) Also, we ...
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14 views

Getting LS means of the response from logistic regression in SAS

So, I know this is more of a programming question than a stats question, but I thought I might try here anyway. I have a logistic regression model with a combination of categorical and continuous ...
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10 views

Logistic Regression Question (Adjustments)

I had a question revolving around logistic regression. I'm looking at a data-set for my work that yields somebody as approved or denied (think credit rating applying for a mortgage, similar but not ...
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8 views

Probability function for time series of logistic regressions

I'm testing out model described in this paper for time series of consumer loans Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Basically authors use ...
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27 views
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Standardization for regularized, sparse hashed logistic regression

As the question states, I'm fitting large, sparse logistic regressions (with hashed interactions, a la vowpal wabbit) for a machine learning system. The features are on different scales, and I'm a ...
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Estimating AR process for Logistic Regression

I'm fitting a time-series model with independent $X$ variables coded as months of the year (so there are 12 of them) and the dependent $y$ variable is some proportion, bounded between 0 and 1. As a ...
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15 views

K-fold cross validation for a glmer model with nested data

I'm working on a data set that contains a hierarchical data structure (i.e., GPS locations nested within individual animals). I'm using a generalized linear mixed effects modeling procedure (lme4 ...
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“Multi-Task” Logistic regression with time series data [on hold]

I'm trying to create model for consumer loan defaults that incorporates individuals payment behavior as time series. Typically this kind of problem is modeled using Cox/Allen model. Then, the other ...
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Multiple Logistic Regression confounding [on hold]

I have gone through an example of loan getting default based upon Regression on parameters such as student, credit card balance and income vs Regression on student only This is called ...
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Odds vs probability in logistic regression

I am going through Trevor Hastie's Classification Techniques. Its says Odds are traditionally used instead of probabilities in horse-racing. I still don't understand how they relate more ...
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19 views

CTR Enhancement Model

I am looking to build a model to enhance CTR. Below is the business description. We have a coupon based website. For each retailer, we have a retailer page. Each retailer page will have up-to 100 ...
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15 views

Comparing logistic regression models with paired data, population-averaged estimates and robust standard errors

I would like to ask if any of you have suggestions for comparing logistic regression models with paired data (3 observations/ID code), population-averaged estimates and robust standard errors (I am ...
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51 views

ROC curve cut off and weights

I have a dependent variable distinguishing between patients that should go to treatment A or treatment B. I want to develop a questionnaire containing binary variables that should decide if the ...
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37 views

Need advice on statistical analysis

I'm designing a study with 3 different groups that are to receive 3 different interventions for a set period of time (n = 100, month long interventions). Would this be called a randomized crossover ...
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15 views

Binary variables [closed]

What are the common error rates for predicting binary variables or in other words, what are the error paramters that are important while doing logistic regression? eg we look at AIC in MLR
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76 views

How can using Logistic Regression without regularization be better?

I'm using this Java machine learning library: https://sites.google.com/site/qianmingjie/home/toolkits/laml From the library I'm using Logistic Regression: ...
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48 views

Can we express logistic loss minimization as a maximum likelihood problem?

I have a simple question about the equivalence of loss minimization and likelihood maximization for logistic regression. Say are given some data $(x_i,y_i) \text{ for } ~i = 1,\ldots,N$ where $x_i ...
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glm using high dimensional data

glm using high dimensional data Hello all. I am new to all of this. I am trying to use GLM in R to do a logit regression. I have a high dimensional data set (each datapoint/vector has about 1000 ...
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3answers
29 views

ROC, variables, unbalanced data, where to start?

Can someone explain to me how you would know which variables to remove? And how do you know if something is accurate or not? Because when I plot an ROC curve the specificity/sensitivity curve, it ...
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1answer
140 views

Do logistic population growth models relate to binary logistic regressions?

I ask this because all resources regarding logistic regression in R involve binary outcomes, so they try to model questions like when will increase in temp cause a switch to fail (0, 1)—involving ...
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Is conditional logistic regression appropriate for comparing costs between treatments?

I am looking at previous reports done by a previous staff member comparing 2 treatments - lets call them treatment A and B for epilepsy and the total costs for a matched population. For instance the ...
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Predicting penalized multinomial logit in R (pmlr package)

I am using the pmlr package to estimate a penalized multinomial logit model, as in the example below: ...
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Hosmer Lemeshow test error in R

I am trying to perform the Hosmer-Lemeshow test in R, using the package "ResourceSelection". My glm model looks like this: ...
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1answer
37 views

Methodological question: adjusting for confounders in logistic regression

I have three attributes in a dataset (D0), representing the binary response of success or failure (R), some form of treatment or treatment group (T), and a potential confounder (C) respectively. ...
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41 views

Interpretation of logistic regression output with an interaction effect

I'm currently working on a research problem that requires the addition of an interaction term for each continuous covariate. In other words, I need to determine how the continuous variable changes ...
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Interaction term and logistic regression

I'm trying to evaluate the differences in habitat selection across 3 seasonal periods. I developed a model in R: ...
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33 views

How does the logistic regression with L-BFGS have to be initialized?

I've performed a logistic regression with L-BFGS on R and noticed that if I changed the initialization, the model retuned was different. Here is my dataset (390 obs. of 14 variables, Y is the target ...
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Logistic/Logit Regression Practice Exam Question

So I have an exam practice question which runs as follows: The logistic function of a number $x$ defined as $f(x)=\frac{1}{1+e^{-x}}$ Use this definition to write down the expected value of response ...
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37 views

Logistic regression and assessment of continuous variables by seasonal categorical variable

I'm currently working on a logistic regression analysis in R where my response variable is 1 = used animal location and 0 = random location. I am modelling non-random habitat selection for a species ...
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In feature engineering, is using x*y as an interaction term different from using x/y?

I want to engineer some new features for a classifier, say, logistic regression. Let's say I have two continuous (numeric) features $x$ and $y$. I know that you can create a new feature from $x$ and ...
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2answers
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Example of interpretation of logistic regression

I was looking at a paper by Pell JS 2009, regarding smoking and survival following acute coronary syndrome. Part of the analysis carried out in the paper was a logistic regression with results as ...
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Inverse probability weighting in logistic models - large weights irrelevant when using additional covariates?

I am using propensity scores for IPW in a logistic GLM in R. Two of the propensities are quite small and thus the resulting weights are quite large - much larger than all the others. I expected these ...
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Intercept update in logistic regression lasso using coordinate descent: how is it calculated?

I am trying to figure out how the intercept is calculated for logistic regression lasso using coordinate descent algorithm based on this seminal paper: Friedman, J., Hastie, T. & Tibshirani, R. ...
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Test if two coefficients are statistically different in logistic regression?

I am doing the logistic regression with logit link in MATLAB [b, ~, stats] = glmfit(X, [Y N], 'binomial', 'link', 'logit'); I can find the $p$-values for each ...
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Logistic regression w/ categorical predictors, and all residuals must be nonnegative

Has anyone written a package for R that can do a logistic regression over categorical variables (like glm) but with the constraint, and I do realize this is weird, ...
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Efficiently processing a large MxNx2 logistic regression, only interactions matter

I'm working with a large 3-way contingency table (roughly $180 \times 40 \times 2$) — both independent variables are categorical and the response is binary. One independent variable (X) ...
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Logistic regression performs better than SVM with poly kernel exponent = 2

I am running an experiment and I built a model initially using Logistic regression and later using SVM with poly kernel with exponent being 2. SVM model with exponent being 2 performs better than with ...
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When is logistic regression minimizing under squared error loss the same as maximizing binomial likelihood?

Implementing logistic regression and getting different results depending on whether I minimize squared error or maximize log likelihood. When are the two equivalent?
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Beta-binomial logistic regression model for binomial data with small samples

I have fitted a nonlinear beta-binomial logistic regression model on data y_i: y_i ~ beta-binom(n_i,mu_i,\Phi) where mu_i = exp(\eta_i)/(1+exp(\eta_i)) , and \eta_i=\beta_0+\beta_1/(1+exp(\beta_2x_i ...
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Some categories never predicted in ordinal logistic regression model

SUBJECT: Some of the predicted categories missing in the ordinal logistic regression output In my ordinal logistic regression model, I have a set of 7 inputs and I have Y = 2, 3, 4, ..., 19 (18 ...
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glm output in R: analysis without coefficiencts

Generally, coeficients and their p values are focussed upon while assessing the regression output. However, there are other things mentioned. How can we analyze the output of glm without the ...
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Logistic Regression Model with Non-Independent Regressors

I'm looking to create a model that takes into account multiple logistic variables in an ordered process. To illustrate, what I'm trying to do is similar to the following: ...
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deal with interaction that is composed of correlated variables in multinomial logistic regression

I'm trying to build a model between three variables: y=user interest, x1=time, and x2=space. All the three variables are categorical, with the response variable y=user interest being described by ...
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1answer
44 views

Principal component analysis / multinomial logistic regression

I'm trying to see how level of scepticism impacts willingness to change diet. To measure sceptism I've used a 7 point likert scale. The study I'm basing my research on used a principal components ...
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Odds ratio and interpretation when 95% CI overlaps 1 but is marginally statistically significant [duplicate]

I'm working on an a logistic regression analysis and using a alpha value of 0.10. I found one of my variables to be marginally significant (P = 0.098) with the odds ratio of 0.963 (0.920 - 1.007). Is ...