Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Is it appropriate to present predicted probabilities from emmeans for a mixed-effects binomial logistic regression?
I am trying to understand how to analyze data for a generalized mixed model (GLMM) with a binary response. I am interested in visualizing the predicted probabilities, as well as a measure of effect ...
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How to analyse binary responses for various factors, including interactions: chi square, mixed models, logistic regression, or ANOVA on percentages?
I run an experiment where subject had to recognize an emotion from various musical stimuli (which were composed with a certain emotional intent). There were 4 levels of emotional_intent, subjects ...
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Predicting individual-level outcome with only group-level data
Suppose I have summary data from a number of different classrooms, and I want to model a binary outcome (pass/fail) for individual students. I have no individual-level data. I have some classroom ...
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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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Determining the Weight of Categorical Variable's Coefficient
Lately, I have been studying about Logistic Regression, and I came across a question on how to handle categorical variable (as opposed to numerical ones).
Let's suppose I have a data table with two ...
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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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Binary panel logistic regression (xtlogit fixed effects) is not converging in Stata, how to resolve?
I have a panel dataset with a sample of 800 groups, each having between 200-500 observations. The data looks like this:
The dependent variable is binomial: ...
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Effect Size interpretation for GLM (Logit)
I am using the following code from effectsize package in R:
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What Statistical test would be most appropriate for comparing death counts by age in two populations?
I am not a statistician by any means, and am new to this. I have "patient" level data that has a persons death status, age, and gender. I want to compare it with the general population ...
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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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how to calculate R-squared in glm? [closed]
I came up with below for my glm analysis but I need to calculate R-squared to cite in the paper? anyone can help me with this please?
summary(lrfit)
Call:
...
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A divergence that can be extended to logistic functions?
If I have data $\{(x_i, y_i)\}_{i=1}^n$ where the dependent variable is binary $(y_i = 0,1)$ I can model it using a logistic function:
$$f(x; \alpha, \beta) = \frac{1}{1 + e^{-(\alpha + \beta x)}}$$
...
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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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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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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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Checking Multicollinearity and building a classification model when dependent is a factor and other independent variables are numerical in r
Problem statement
Y - Dependent variable is a factor (with levels A, B, and C)
Independent variables are all numerical variables.
Important: I have only 70 data points.
End Goal: Building a ...
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Is standardization needed before fitting logistic regression?
My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is:
$$\frac{x_i-\min(x_i)}{\max(...
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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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theoretical basis for logistic regression
Sorry if this is obvious but why does it seem that some papers/studies (usually in economics discipline) use utility theory as a basis for discrete choice models while others (often social science) do ...
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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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"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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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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Reproduce results of bayesglm with stan_glm
As indicated in the title, I am trying to reproduce the
results of the bayesglm function with the stan_glm. In
principle, the ...
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What is the difference between logistic and logit regression?
What is the difference between logistic and logit regression? I understand that they are similar (or even the same thing) but could someone explain the difference(s) between these two? Is one about ...
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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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How to find out if there is any correlation between a yes/no question and Likert scale data?
I have used a survey to research willingness to pay regarding virtual goods in videogames. For this, I have asked the respondents a couple of questions via Likert scale, to get their opinions on ...
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Standardized coefficients logistic Regression R
Is there an analogue to the standardized logistic regression coefficients I can get in Stata for R? I know in regression it is simply lm.beta(model). Is there something like this for logistic ...
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Geometric Interpretation of Softmax Regression
I'm writing a series of blog posts on the basics of machine learning, just for fun, mostly to validate my understanding of Andrew Ng's class. As I'm currently studying generalized linear models (GLMs),...
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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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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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What is the difference between multinomial and ordinal logistic regression?
Can somebody explain it with a simple example!
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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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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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How to test proportion of each factor level against mean proportion across all levels (binary outcome)
I have a dataset with the factor region (4 levels) and a binary variable outcome (0/1). Here in wide format.
...
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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 compute effect sizes of single predictor in a logistical regression?
I have a logistic regression, with a binary response, a continuous predictor, and a categorical one. Is there any way to calculate the effect size of each of those predictors, similar to partial eta-...
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Model validity for Ordinal logistic model
I am doing a study using OLR. The model tries to assess the satisfaction of ground level stakeholders (scale of 1(extremely dissatisfied) to 5(highly satisfied) in an urban area. The independent ...
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Is there an Equivalent of "proc surveylogistic" in R?
A colleague told me about "proc surveylogistic" in SAS -- see details here -- is there an equivalent function in R?
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Odd ratio for binomial variable values
I am calculating the odd ratio of logistic regression (using statsmodel of Python). I have one independent variable (i.e. process type: faulty (1) or non-faulty (2) and one dependent variable (i.e. ...
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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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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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