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

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sample size for time dependent binomial distribution or logistical regression?

Background I have a membrane of roughly 30000 individual cells that is being flexed back and forth. After some time it fatigues and individual cells start to break. for example after 2000 times being ...
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20 views

Multiple binary logit regressions vs multinomial logit regressions? [duplicate]

Lets assume we have a dependent varible which can take on three values: 1, 2 and 3. Is there any differences in running multiple binary logit regressions(ie. 1 vs 2 and 2 vs 3) or the multinomial ...
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Identifying important differences between supervised learning datasets

The training data in a multi-class supervised learning task shows a significant dependence on time that is apparently not captured well by my learners. Specifically, the two learners I used (OvR ...
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46 views

Is my understanding of regularized logistic regression correct?

I learned that regularized logistic regression helps prevent the model from over-fitting the data. I understand that the function is still technically a high-order polynomial, but the effect is ...
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13 views

Controlling for White Matter when looking at a sub-section of White Matter in the Brain - Valid Statistically?

I am interested in other people's opinion on the following matter. I have just come across a brain imaging paper that looks at differences in the volume a sub-section of white matter (SS) in the ...
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59 views

Any necessary EDA before logistic?

I wanted to know if we do EDA before logistic regression. Sure, I will look at the variables and their distributions, but is there anything specific to logistic?
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Results of xgboost binary logistic regression in R

I am having problems running logistic regression with the xgboost package. My code (the dataset is not the classic iris dataset): ...
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7 views

Possible to estimate a multinomial logit model with a first-stage multinomial logit sample selection model?

I want to estimate the effect of education type (4 categories) on an 8-category outcome variable. Since choice of education has self-selection issues, I want to correct for this using the inverse ...
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33 views

Complete separation and stepwise regression - possible in R?

I've been using stepAIC to narrow down my logistic regression model. However, I get the following warning when I run my model: glm.fit: fitted probabilities numerically 0 or 1 occurred I know this ...
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30 views

Why is the probability of my chi square statistic equal to 0

Binary logistic regression in R I have derived the chi square statistic and degrees of freedom for my model (200.7839, 8, respectively) however, when I attempt to determine the probability associated ...
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15 views

Binary Logistic Regression with multiple binary and ordinal independent variables

In my data set I have one dependent variable (dead or alive) and 37 predictor variables. 35 of my predictor variables are dichotomous (Obese: 1 or 0, Female 1 or 0, etc), however 2 of my variables are ...
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19 views

Interpreting factor effect in a logistic regression

Say I'm working with a biological system where two (or more) genotypes are reared under a series of daylength treatments, and scored for a binomial response variable y. I want to know whether the ...
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12 views

Can FTRL be applied on linear least squares? or is it just for logistic regression models?

I'm exploring follow-the-regularized-leader FTRL proximal gradient descent: paper, reference implementation. Everywhere FTRL is mentioned, the loss surface for the gradient decent is the ...
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53 views

logistic regression predictive modeling

I would like to use a logistic regression for estimating the parameters of the logit function by using the maximum likelihood estimate. This amounts to minimizing the log-loss function, instead of ...
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1answer
38 views

Best fit with GLM in R

I'm trying to know what is the best GLM fit with this simple dataset and tests in R: ...
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1answer
32 views

Slight difference in output of SAS proc genmod and R glm

I am trying to reproduce a model fit using SAS proc genmod in R glm and am able to get the same estimates and SE's for all parameters except the intercept and Distance coefficient. SAS: ...
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13 views

How do you test for departure from linear trend across ordered categorical variables with logistic regression using R? [migrated]

I'm testing for a linear trend in the log odds of a binary outcome across an ordered categorical independent variable. This is straightforwardly achieved by treating the independent variable as ...
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11 views

Logistic Regression and possibly over fitting the model [duplicate]

I have run the below model, with a binary outcome on class and multiple predictors. When I run the predictor pre_class as a binary outcome (it is originally ordinal ...
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1answer
34 views

Test/Measure for Rank Ordering a Logistic Regression model, invariant to event rate and population size

I have a model whose purpose is to rank order event risk - the output of which is split into twentiles (which have been based off the benchmark data). Currently, I'm using Somers' D calculated on ...
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Data setup: Attrition/Churn Modeling with Time Dependencies

Beginner Data Scientist here... I'm setting out to build a predictive model for our client in the hotel/hospitality industry to explain the factors contributing to the attrition of their Loyalty ...
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Loss Function for Multinomial Logistic Regression - Cannot find its derivative

For Multinomial Logistic Regression we can define the Loss Function in the following way: $J(\theta)=\frac{-1}{m}\sum\limits_{i=1}^m\sum\limits_{j=1}^k ...
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logistic regression - complete/quasi-separation

What is the implication if I don't fix a logistic regression that has complete or quasi separation? can I still read the marginal effects or are they not going to be valid? My exercise is actually ...
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68 views

How to best model interaction effect of two continuous predictor variables?

Consider the following problem: In a logistic regression model, we believe that two continuous predictor variables $X_1$ and $X_2$ impact the probability of event. It is hypothesized that the ...
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29 views

Logistic Regression interpreting results in order of importance [closed]

For prediction model using Binary Logistic Regression, is there a best sequence to interpret the resulting logistic regression output to decide whether the results are good or not? In order to select ...
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51 views

R LASSO always include some coefficient and question about data partition

I have limited statistic knowledge but I am trying to conduct logistic regression by using a data with 300+ predictors. So I decided to use glmnet and LASSO. Below please see my code: ...
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10 views

SAS Wald Chi square test to test regression parameters large sample size

I have a logistic regression model built on sample1 proc logistic data = sample1; model outcome= x ; run; I want to test whether the coefficient for x estimated from sample 2 equals the ...
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40 views

Does logistic regression determine the direction of the association?

I've conducted a logistic regression in which a binary outcome was the dependent and some continuous factors were entered as independent variables. First: Can this model determine that the ...
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77 views

ROC curve drawbacks

In the class yesterday, we were taught about logistic and subsequently the ROC curve and how to use it. My questions are: Is this the most common way to identify if the logistic model is the ...
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1answer
18 views

Logistic regression using ANOVA kernel in SKLearn?

In RapidMiner, you can run a logistic regression classifier with multiple kernel types. I see no options in sklearn.linear_models.LogisticRegression. Does anybody ...
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34 views

Logistic regression - variable transformation

I have a continues variable(EntropyDistanceFromMean) which I would like to use in a logistic regression, the problem with that variable is that it starting to effect the output (MQL) found on the ...
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16 views

Valid procedure for binary classification with cross validation

I have inherited a classification model for a binary parameter and have been asked if estimates can be improved. From this model, an equation has been put into some software for predicting this ...
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20 views

Relative Risk and Odds Ratio

I am performing logistic regression on a data set. I find that the the ML estimate for a parameter will show a significant p-value but the Confidence Interval for the Odds Ratio will go through 1. ...
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11 views

Random intercept with high ICC - interpretation

I have a feeling there is a very simple answer to this question that I am overlooking. For some reason I am having a hard time wrapping my head around this, even though it's a pretty simple situation. ...
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827 views

Why is the logistic distribution called “logistic”?

What is "logistic" about the logistic distribution, in a common sense way? What is the etymology of and the lexical rationale for the name, not just pure math definition?
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Time weighted variables

anyone please help... Variables: *Independent variable: BP in followup *Dependent variable: Change in Glucose/year (ordinal: low:<-10/year, moderate: -10/year to 10/year, high: >10/year) Time: ...
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R: Can I include random effects in Firth's penalized-likelhood regression?

I have the problem of (quasi-)complete separation in a dataset with N=500 but only 25 positive outcomes (response = binomial). Including a lot of categorical factors in my model, I face the problem of ...
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Conditional distribution/expectation shape

Let's say I have two arrays $X$ and $Y$ of the same length, and suppose that $Y$ has binary data, that is $y \in \{0, 1\}$ for ever $y\in Y$. I would like to plot probability of $y = 1$ given $x \in ...
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normalization to zero mean and variance one logistic regression & random forrests

i was just thinking how does normalization to 0 mean and variance 1 (using an affine linear mapping) can impact the classification accuracy and the choice of hyperparameters when using: logistic ...
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Application of Huber-White Variance Estimates in GLMER

I'm currently working on an analysis in R using GLMER mixed-effects model with a logistic regression framework under the lme4 package. I would like to include empirical (Huber–White sandwich) variance ...
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Is it acceptable to combine factorised variables with quantitative variables together in a logistic regression

In an attempt to reduce my variables to fit the sample size ratio in logistic regression. I used factor analysis to reduce the some of the variables under study. I have two sets of variables in a ...
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1answer
23 views

An independent variable in the logistic regression has 2.2%, 2.1% and 95.7% distribution [closed]

I have one independent variable in the logistic regression with a 2.2%, 2.1% and 95.7% distribution (three categories IV). My DV has good distribution (68% and 32%). How would this IV affect my ...
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Converting nomogram to logistic regression coefficients and intercept

My plan is to use a published nomogram to predict events in my data set. The question is, how do I derive logistic coefficients and intercept from the nomogram?
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63 views

Why use logistic regression instead of SVM?

I have a very basic question, but it's not really clear to me. When would one choose logistic regression over SVM? Maximum margin property seems more justifiable then whatever log. reg does. Is it ...
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70 views

choosing between logistic and discriminant

I am looking at regularized logistic regression, (l1 and l2 at the moment) and regularized discriminant analysis. How do I compare the two? I was thinking of doing gcv on both methods over a set of ...
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11 views

Using clusters in parameters to divide up a logistic regression?

I have a data set that is a bit involved, where I try to find the parameters that are the most helpful when I try to predict a binary response (the behaviour of human raters). The number of possible ...
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47 views

Leave-one-out cross validation output interpretation and ROC curve

I have taken plenty of time to try and help myself, but I keep reaching dead ends. I have a dataset consisting of body measurements collected from a bird species, and the sex of each bird (known by ...
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Understanding / plotting 2 variables where both are allowed to vary in logistic regression

I'm hoping someone can help clarify a few things for me. I ran some relatively simple logistic regressions in r and am having trouble with interpretation. I'm interested in the effects of elevation ...
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17 views

Bootstrapped confidence intervals for predicted probabilities seem too small (using glmer in lme4)

I am using logistic regression to examine factors affecting female reproductive status (0=inactive, 1=active) in a rodent species. My top model includes the fixed effect of "year" and a random ...
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1answer
116 views

Repeated measures - random effects for logistic regression in R?

Study design 504 individuals were all sampled 2 times. Once before and once after a celebration. The goal is to investigate if this event (Celebration) as well as working with animals (sheepdog) ...
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Imputation of predictors missing data for logistic modelling

I never used imputation of missing data and I would like to understand the effect of imputation in a specific scenario. Lets suppose that I have a dataset whit some predictors variable and one binary ...