Questions tagged [logistic]

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

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PSPP Errors Running Binary Logistic Regression [closed]

I am running binary logistic regression on a dataset and keep getting NaN and +Infinit errors in my output. NaN in the model summary and +Infinite in the Wald, Sig, Exp(B) and so forth columns of the ...
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Regression of binomial response when the predictor range is limited

I am looking into a dataset for which I will be doing a regression. When considering the option of a logistic regression, I started doing univariate regressions to get a feeling for the possible ...
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Logistic regression simulation with respect to event occurrence (prevalence)

I am trying to simulate logistic regression data, but under the constraints of prevalence. $$\text{logit}(y_i) = \beta_0 + \beta_1 X_1 + \beta_2X_2$$ For example, I want to create a dataset that has ...
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learning curve for RF and LR comparison and selection

I am plotting learning curve to check how the model perform on training data set and the effect of the training size on the accuracy. I am using two models, random forest and logistic regression. From ...
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Optimize classification rule in multinomial logistic regression

We know that in the case of logistic regression, a classification threshold p=0.5 is generally not an optimal choice when seeking to optimise sensitivity and sensitivity. This is generally due to the ...
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Is it normal for simple logistic regression to significantly outperform any other statistical ML algorithm?

I'm working on a simple classification project with an imbalanced (minority-to-majority-ratio ~ 0.2) dataset that has ~4000 rows and ~200 features. I noticed that, for my dataset, a simple logistic ...
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Interpretation of binomial regression in R

I'm running a regression to see if theres a positive or negative relationship between cvdrst and smkcigst, but I'm not sure how to interpret this regression also why is the data for smkcigst NA, even ...
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What is the intuition behind the odds scale?

What is an intuitive explanation of the odds scale? In a logistic regression such as $$logit(p) = \beta_0 + \beta_1 x$$ we often interpret $\beta_1$ by looking at the odds ratio, $e^{\beta_1}$, which ...
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How do I do a logistic regression model in R for an outcome with multiple values?

I want to analyse the association between the outcome "Other CTR-CVD" and the independent variables would be "anthracyclines", "Her2", "VEGF", "TKI, "...
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Household vehicle ownership allocation based on vehicle sales data

I have a household travel survey data, which consisted of the number of vehicle owned for each of the household, household income, job, and household size. However, the data didn't show the brand, ...
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Interpreting Results of Logistic Regression when both x, y variables are nominal

I've been trying to analyze the result from my experiment. But since I'm new to the field of statistics, I'm struggling in every step, including the interpretation of results. I have 4 groups of ...
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R Mediation with continuous predictor and binary logistic regression models

I am running a mediation model using the r mediation package, but I am not getting the correct output for my variable types. I have a continuous predictor, but the output is treating my predictor as a ...
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Interpretation of Multinominal Logistic Regression coefficients

I am struggling to understand my Multinominal Logistic Regression. This is my first time ever tackling such a model. Note that I was following this recipe. I am trying to predict the redemption rate (...
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Why do discrete choice models (such as MNL) not require test set?

The central challenge of Machine learning models is perform well on unseen data. The data is randomly split into train and test set. The test set acts as surrogate for unseen data and is used to ...
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How do I model a predictor with multiple, mutually-exclusive possibilities? Specifically, type(s) of crime(s) charged at arrest

I am creating logistic regression models predicting outcomes of criminal arrest events, e.g., whether an arrestee hired a private attorney or not. My confusion concerns about 20 mutually exclusive ...
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Logistics model on variable with values 1, 2, 3?

I have a dataset containing traffic crash information. One variable in the set is the number of fatalities that resulted in the crash, which has the values 0, 1, 2, and 3. I am working in R and want ...
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Categorical independent variables for logistic regression

I'm currently struggling to find a appropriate method to analyze my experiment. Currently, I have 4 groups of subjects, and each subjects made a choice between 3 options(A or B or No choice). Below ...
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Predicting probability of choice across different choice sets

I have data where people saw a series of 2 forced choices. Those choices were a random selection from a set of 4. So, from a set {A, B, C, D} participants saw two random options (e.g., {A, D} or {B, C}...
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Sample size study not reflecting findings in R simulation

I have a binomial response variable (a sort of conversion rate for a search mechanism) and just one independent variable, which is the "type" of the search bar template. To illustrate, my ...
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Logistic regression: variable coefficient is statistically significant but not statistically significant as an exponentiated odds ratio? [closed]

As mentioned in the title. I came across this instance using GLM in R. Is this an error? EDIT: The p-value of the coefficient was calculated by GLM in R and is less than 0.05. I then plotted the odds ...
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How to compare the goodness of fit between linear and logit? Why linear deviance is less than logit?

How can I evaluate which model - between linear and logit - determine the best fit to the data? The models use the same input variables and I thought that comparing the deviances was the proper choice ...
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Logistic regression - One model trained on different groups

I have a logistic regression model that trains a set of binary independent variables (X) on a binary response variable (Y). The data was gathered from different individuals for who also e.g. socio-...
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Binning and WoE transformation. Reducing number of categories for high cardinality features

I'm doing a credit default risk project. I have some features like a job title that has >100000 unique titles. What is the best way to reduce cardinality in a meaningful way? The end goal is to get ...
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Standardization in Machine Learning Models

Please answer this question in two contexts: Context 1 - Performance: Which models are sensitive AND which models are insensitive to standardization? Why? Are there any edge-cases in which ...
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How to test whether a factor differs from 50%, and which levels within the factor differ from 50%?

I have a categorical factor with 100 levels and 100 different proportions. I would like to test (a) whether these proportions differ from 50%, and (b) if any of the levels in particular differ more ...
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Ordinal logistic regression with interaction

I have a dependent variable donation intention on a 7-point likert scale(1=totally disagree, 7= totally agree), an independent variable which is a dummy (0= price is not framed, 1 = price is framed) ...
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Binomial regression with non traditional dataset

I am aiming to do a binomial regression, most aspects are close to textbook situations. But not on the datasets side, while I have a traditional dataset, that will split into training, test and ...
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Biased logistic regression in pytorch

My model has decently high AUC=90%, but is biased, underestimating the probability $y=1$. This is systematic across some of the input features as well. How can I nudge the bias term, or otherwise ...
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Logit GLM and logit beta regression: Practical difference in the interpretation of the coefficients?

Terminology: By logit GLM I mean a generalized linear model with a binomial distribution and a logit link function. By beta regression I mean beta regression with a logit link function. I understand – ...
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What can be done about assumption violations in logistic regression?

I am working on a logistic regression solution, and I'm experiencing some issues with assumptions according to the diagnostic graphs.For linear regression, I am familiar with addressing similar ...
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Is there a test (in R) for predicting the proportion of "successes" to "failures"?

As I understand it, binomial logistic regression is good for predicting the probability of a "1" outcome from a discrete binomial distribution. What if instead, one wanted to predict the ...
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From OLS regression to Logistic Model

I'm currently working on my master's thesis in finance. Without going into to much detail, my goal is to regress certain predictors on first-day returns (SPAC IPO performance). However, after ...
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Regression with independent variables as percentages [duplicate]

I'm trying to run a regression analysis where my dependent is number of deaths and some of my independent variables are things like: poverty rate (Ex - 0.16, 0.09), incarceration rates (Ex 0.0042, 0....
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Dependent variable has no variance error in logit regression

I m running a logit regression with over 90,000 observations. However the case when dependent variable =1 , is only 115 observations as per the data, the rest are 0. The Eviews software shows "...
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How to reconcile model results?

I have a logistic regression in which I am evaluating animal selection for various categorical landcover types (woody, herbaceous, bare), canopy cover (continuous variable), and vegetation density (...
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How to interpret independent dummy variables in logistic regression?

I have both quantitative and dummy independent variables in my logistic regression. Dependent variable is binary. I have 2 questions. How to interpret a quantitative variable that is negative? How to ...
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AUC first drops before starting to grow

I have a very imbalanced classification problem, 99% vs 1%, and I am using logistic regression in pytorch. I often see the initial weights, randomly initialized, achieve AUC of 60-70% before any ...
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Error in wp.logistic: ‘p0’ must be numeric in (0,1)? What to do when p0 is negative?

I am trying to find out the sample size I need for a future study using a reanalysed existing dataset to enter the values in a WebPower script. I am using the intercept and odds ratio/estimate of my ...
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2 votes
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Logistic Regression on multiple classes (Shouldn't it be only on binary?)

I'm a bit confused with the usage of logistic regression for multi-class classification. My understanding is that a logistic regression is dichotomous (two possible classes), so in the example of the ...
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For all datasets with a binary outcome, will linear regression always yield betas with a smaller standard error compared to logistic regression?

Any cases where the betas' standard errors from logistic regression will be smaller than linear regression, after converting from log odds space to probability space?
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How to add dummy variables for countries in time series data

I am running a binary logit regression. However, my independent variables consists of quantitative and dummy variables. Some of them are type of loan (5 types), loan purpose (5 types) and countries (...
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Interpreting logistic regression odds ratios: proportional odds

I am not understanding the interpretation of the odds ratio from logistic regression coefficients. I understand that if $\beta_1$ is sex, with male the reference group, then $e^{\beta_1}$ gives the ...
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Is Ordinal logistic regression linear or nonlinear?

Quick question, is ordinal logistic regression a linear or nonlinear model? Finding different sources supporting the other, and the more I read the more I get confused myself. Perse, it should fall ...
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Model comparison on different datasets + Non-nested model comparison

I actually have two questions. Similar questions unfortunatelly do not answer mine. 1)I realize that with anova(model1,model2) one can only compare two models if ...
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2 answers
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Interpretation of continuous variable in an odds ratio for logistic regression

I have an odds ratio of 1.02 for x variable (Age, a continuous variable measured in units "1 year"). My response variable is Y. I would interpret this as for every increase in Age by 1 year ...
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Closed-form solution of logistic regression [duplicate]

"For logistic regression, there is no longer a closed-form solution, due to the non-linearity of the logistic sigmoid function". Can someone please explain the above quoted sentence in ...
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Logistic regression predictions dont work

I have this problem with logit, that when I want to create confusion matrix, it simply displays the real values in the first row and in the second row, there are never any numbers. I created a lot of ...
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Different types of residuals revealing different stories on model fit

I have a series of observations as y_actual and fit a glm model with binomial family using <...
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Difference between generalized logistic regression and logistic regression

I have received a weird comment from a referee of pretty decent Journal. I stated in the methods section that "The association of the exposure with the outcome was investigated in terms of odds ...
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class_weight='balanced' vs high f_beta score for imbalanced logistic regression in sklearn. Please help explain the difference

I have an imbalanced binary classification problem I am trying to solve with the LogisticRegression algorithm in sklearn. As the data is highly imbalanced I am looking at ways to treat the imbalance ...
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