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

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Predicted probabilities in logistic regression [on hold]

I have performed a binary logistic regression with whether or not a sports person was re-contracted or not as the DV. Draft year is a significant predictor therefore I am now trying to determine the ...
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112 views

How to handle ordinal categorical variable as independent variable

I am using a logit model. My dependent variable is binary. However I have an independent variable which is categorical and contains the responses: ...
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23 views

Binary logistic regression in R - assistance with determining odds of a predictor at different levels

I have performed a binary logistic regression in R with whether or not a sportsperson was re-contracted or not as the DV. My final model is as follows; ...
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17 views

Odds ratio discriminatory power

In logistic regression, logit Y = ax1+ bx2 +cx3. a variable has a coefficient attached to it. So we can directly measure the change in y per unit change in x. Can I get a similar relationship from ...
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ordinal logistic regression with time series

At several time points, I sample different individuals from a population (say 60 ind/time point). I assign to each of them one category (either low < middle < high). I then want to model this ...
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Can (how) you enter control variables in a binary logistic regression?

I'm running a binary logistic regression to test whether personality ratings (scale of 1-5) predict a binary outcome, in children. I want to enter factors such as age and gender as control variables, ...
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56 views

Why coefficient changes sign between linear and logistic regression?

My dependent variable $Y$ is continuous a linear model is estimated: \begin{equation} Y = \beta_{0} + \beta_{1} X + \epsilon . \end{equation} I transform the dependent variable into $Z = 1 \text{ if ...
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23 views

What is $C$ in the four parameter logistic Law?

It is well known that the four parameter logistic law has the following form $$ F(x)=D+\frac{A-D}{1+\Big(\frac{x}{C}\Big)^B} $$ What characterise this curve is its four parameters. A=starting ...
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Interpretaion of Logistic Coefficients

All, I ran a logistic Regression on a set of variables both categorical and continuous with a binary event as dependent variable. Now post modelling, I observe a set of categorical variables showing ...
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Logit model - problem with variables [duplicate]

I am a newbie in stats, but keen on learning new stuff. I am constructing a logit model to predict companys default probability. What is the best manner to add new dependant varable to the model: ...
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logistic regression or mantel-haenszel?

For a 2x2xk table, where k are strata from repeated cross-sectional studies over the course of 10 years, would it be suitable with a Mantel-Haenszel test of common odds ratio or would a logistic ...
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28 views

Assessing calibration plot

I am curious about calibrating a probability estimate, namely a logistic regression. With the example below the outputted calibration plot shows bad calibration with the estimate, or the non ...
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Almost a multinomial logistic regression

Is there a way to train a multinomial logistic model where the true classification is unknown, but summary information obtained from the true classifications is known? Illustrative Example: 5 ...
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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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27 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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59 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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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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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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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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37 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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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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1answer
16 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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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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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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61 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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40 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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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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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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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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35 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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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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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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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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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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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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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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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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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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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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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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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 ...