Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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"LinAlgError: Singular matrix" when using statsmodels Logistic Regression [closed]
I am trying to use a Logistic Regression model to predict the Disease from the Symptoms using this dataset: https://www.kaggle.com/datasets/rabisingh/symptom-checker?select=Training.csv
I am focusing ...
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Is it possible to adjust a logistic regression model when the false positive rate is too high for observations belonging to a specific category?
This is a hypothetical scenario for self-learning purposes (not homework), so not a lot of additional details to give than what I mention below.
Let's say I have a binary logistic regression model, ...
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Strange interaction term estimate in a logistic regression with a large class imbalance between exposure groups. How to interpret?
EDIT 2
In reply to one of the commenters, here is the 2x2x2 table. Y = 1 : Y = 0.
X = 1
X = 0
M = 1
9 : 73
3 : 29
M = 0
34 : 245
1,214 : 21,204
EDIT 1
In my attempts to make things simpler when ...
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Obtaining Marginal Forecasts
I am trying to make predictions on a dummy variable. What I am predicting is whether or not a separate variable ever changes from a zero to a 1 in a year from the observation date. The dummy variable ...
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What maximum value of AUC optimism could still be allowed to confirm that logistic regression model does not overfit?
I am not sure how to define that a statistical model does not overfit based on a difference between bootstrapped AUC and AUC calculated on all training data. In the literature I saw 2 approches. The ...
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Is it right to call the values produced by emmeans coefficicents?
I have a logistic regression model with a categorical predictor with five levels.
I compute emmeans for the model, splitting by my categorical predictor.
...
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Is my regularized logistic regression model overfit?
I have a dataset with the following characteristics:
moderate sample size (~300 samples)
moderate class imbalance (~20% positives)
high-dimensional (the number of independent variables, again ~300, ...
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Is G*Power reliable for logistic regression? It does not seem to account for Hauck-Donner
I've just been introduced to G*Power. The only option I could find for analysing logistic regression is described as:
Options: Large sample z-Test, Demidenko (2007) with var corr
That seems to ...
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Alternative test instead of logistic regression for binary dependent outcome variables?
I have a dataset of patients who underwent an operation, and I collected information on post-surgery complications such as necrosis (binary outcome variable). Now, I would like to investigate whether ...
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Evaluating changes in between-individual variation in time series data
I'm working on evaluating an intervention using an interrupted time series approach, and one of our hypotheses involves evaluating its effect on between-individual variation. I'm using ...
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How to linearize logistics function?
The function is $y=L/(1+e^{(-kx)} )$. After research I found this equation to linearize it, replace y values by this: $\ln(1/(y/(L-1)))$ , then keep x values the same and plot it. But with further ...
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Relative Risk or Odds ratio using machine learning
I am thinking of using machine learning type classification models to compare them with the traditional approach of logistic regression (dichotomous outcome) in a sample of patients with diabetic foot ...
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"This parameter is redundant" - SPSS Binary Logistic mixed model
I know there is a question that asked about a similar warning, but that user's "error" had to do with dummy coding while I think my problem has to be with statistical power and understanding ...
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Second order cross-partial derivative for a binomial logistic regression in R [closed]
I have this following code in Stata:
...
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Psuedo-"Odds ratio" for multiple columns
I have a dataset with a categorical target ($y$) and multiple categorical features ($x_1$, $x_2$, ..., $x_i$). I have been able to successfully use a logistic regression model to calculate an odds ...
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Sklearn feature selection performs strangely with 2 groups (and with SVC)
Previously I've successfully performed support vector classification with recursive feature elimination in R using the e1071 package, but I'm now hoping to move over to SciKit Learn given that Python ...
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Interpretation of an interaction effect (ratio of odds ratios) when using effect coding
I ran a multilevel logistic regression model using function glmer from R package lme4. All of my predictors are binary and I have used effect coding, i.e., coding -...
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With logistic regression, how does one choose a number of predictors when preregistering a study?
Harrell's Regression Modelling Strategies suggests that the number of predictors should not exceed $m/10$, $m/15$ or $m/20$.* For logistic regression $m$ is $\textrm{min}(n_1, n_2)$, where $n_1$ and $...
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Making and reporting a logistic regression, stratified by sex and age
This question was already asked at Stackoverflow - R Language Collective, but they said this might be a better place to post this question, so here I am.
I'm doing the statistics in R. I have this ...
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Could multicollinearity be messing up my logistic regression? Can I overcome it?
My data has 5 binary dependent variables, 9 categorical independent variables, and 3 continuous independent variables, with a sample size of 1232. The 5 dependent variables are just different ways of ...
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Orthogonal polynomial contrasts in SPSS (for a binary logistic regression)
I have 4 IVs: gender (male, female), marital status (married, single), threat (continuous variable) and stress with four levels (ranging from 7 to 10 with ten being 'most stressed'). My DV is ...
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How to Handle Non-Multinormality in the Context of Exploratory Factor Analysis for Logistic Regression
I'm trying to follow the book A Step-by-Step Guide to Exploratory Factor Analysis with R and Rstudio, by Marley W. Watkins, and apply the principles in the book to a real-world data set. Ultimately, ...
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How to forecast changepoints from Gas Concentration Data?
So I'm trying to predict when gas concentrations change from sensor conductivity readings over a day. The gases randomly change concentrations around every 80-120 seconds and are kept constant between ...
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Probability threshold in ROC curve analyses
When conducting a logistic regression analysis in SPSS, a default threshold of 0.5 is used for the classification table. Consequently, individuals with a predicted probability < 0.5 are assigned to ...
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Logistic Classification using R::caret
My question stems from this one I recently posted (I've fixed the issue raised in the comments, though that wasn't the topic of the question):
Strange interpretation of odds ratio from logistic ...
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Use of simple statistics for matched study designs
This might be a silly question, but I have always found this confusing. In a case-control study one selects participants to the study based on having or not having the outcome, in addition to some set ...
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exact match on covariates or include in a model?
The scenario is I have a binary outcome, a treatment group variable (3 groups) and 3 covariates. It is possible to exact match all 3 covariates on 1:N from group 1:2 and group 1:3. Would you perform ...
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heterogenous treatment effects - a question
So I conducted a framing experiment - 4000 people received either framing A or B, and donated $ to a charity if they wanted to.
Now I want to check if framing A or B had specific efffects on ...
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Why shouldn't I run a logistic regression with dependent vars between 0 and 1?
I'm trying to predict the probability of an individual progressing all the way through a series of tasks. I have participants, they participate in Task 1, if they succeed, they move to Task 2, if they ...
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VPC for a three-level logistic regression
I am building a mixed effects logistic regression model to explore whether or not someone is diagnosed late with a specific infectious disease. My outcome is late diagnosis (yes/no). My data includes ...
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Binary logistic regression results interpretation when one IV is ordinal [closed]
I don't know how to interpret the ordinal variable 'Stress' in my binary logistic regression analysis.'Stress' was measured on a 10 point scale where 1 is 'Least stressed' and 10 is 'Most stressed'. ...
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Best way to address selection bias when outcome cannot be randomized
I have an (low incidence) binary outcome compared between 2 groups. The intervention for group 1 is coming from a specific type of center (academic) while group 2 from a different center. It is not ...
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Logit model for panel data with fixed effect [r]
I'd like to estimate the factors that determine the development of heating networks in French municipalities. To do this, I use various variables (population density, age of dwelling, ideology of the ...
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Strange interpretation of odds ratio from logistic regression
I am involved in writing a manuscript applying logistic regression to estimate rates of seafood mislabelling.
The model (fitted using glm() with the default logit ...
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Logistic regression odds vs Survival analysis odds
Why do I get significantly different answers from the logistic regression and survival analyses? How can I fix this code?
Logistic regression:
...
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Automated Code for Logistic Regression [closed]
My Y variable (output) is binary (0 or 1). I have 10 input variables in total, 3 of them are scaled variable, 2 of them are ordinal number therefore being written with C( ). Rather than running the ...
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Interpreting Logistic Regression with categorical variable
Please help! I am using binary logistic regression to model the probability of nesting in 5 different habitat types, with 1 = nest presence and 0 = random point. I have 5 different habitat types and ...
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Is this the right equation format for a logistic regression?
I was reading a paper and the authors wrote the equation for a logistic regression like this: Pr(yf = 1|X,C) = P(β1Xf + β2Cf + σs + ρr + f sr > 0)
Is this a typical way of writing a logit ...
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Implications of very low but statistically significant average marginal effects
I built a multivariate logistic regression model, which is largely a replication of a published paper (I just some different data). My regression table (with the coefficient reported as log odds) ...
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Imbalanced classes and possible ways to increase precision, recall and f1-score of the prediction model
I've just started my data science internship, and this is my first time in the field. I'm sure I'll face challenges in the future where I might need your help. It's also my first time asking a ...
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Difference between Probability and Effects in Logit Models
when I run a logistic model I get log odds that I can easily convert to probabilities. What I don't understand is how can I use percentages instead?
Here's the code:
...
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Importance of goodness of fit in Hypothesis testing
I am struggling with one question. Is goodness of fit of a model necessary when your purpose is to test hypothesis regarding a coefficient? To be specific, I am regressing formal credit access on ...
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Help with question comparing logistic regression analyses
I have accounted a problem when trying to finish my analyses. So, I want to compare the beta weights between two logistic regression analyses from independent samples (same IV and DV in both ...
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Do predictors in logistic regression need to be stationary?
For linear regression with time series predictors, it is widely accepted that both dependent and independent variables must be stationary. The reason is that we need to assume that the errors are ...
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Does marginal effects change depending on the size and heterogeneity of the population?
I am trying to predict the probability of receiving treatment using a logit regression. I have two regressions with the same explanatory variables:
Regression 1: I predict the probability of ...
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The "detectseparation" package. How to interpret its results?
I am running a logistic regression on financial data of several companies. The dependent variable is the company to be a smoother (binary variable: 0, 1), and there are several independent variables: ...
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Would ordinal logistic regression be appropriate in this case?
I have 2 independent variables with 2 categories each: 'gender' (male, female) and 'relationship status' (married, single), and one DV: 'emotional impact' measured on a 10 point scale ranging from 1 (...
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Analysis of a case-control study using ordinal logistic regression
Suppose we have data from a case-control study where for each case having had some disease or event $Y_{binary}$ a control was included that was free of $Y_{binary}$. Due to this study design we ...
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Why do my random effects estimates not match the data?
I'm running a logistic regression model in R using glmer() from lme4. I only want to get the random effects estimates, so I have an intercept-only model with just ...
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Statistical tests for knowing the effect of a campaign on sales
I am currently working on my very first real life Data Science problem and I am facing a bit of a challenge in formulating the solution.
The question is to find out if conducting a campaign has an ...
