Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Interpreting normalized value in logistic regression
I'm trying to interpret results from my logistic regression. I normalized the values between 0 and 100 because I had many variables with different units.
My intercept is -0.741 and I'm using that to ...
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Does increasing number of observations lead to the decreasing of Mean Square Error of consistent estimators?
I know that not all weakly consistent estimators exhibit MSE-consistency : https://stats.stackexchange.com/a/610835/397467.
Anyway, does increasing the sample size leads to a reduction in their mean ...
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"decreased", "same", "increased" outcomes : ordinal logistic vs multinomial logistic?
I am conducting a study where I have ordinal categorical outcome variables representing frequency for two distinct periods. My goal is to consolidate the responses from these periods into a single ...
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Logistic Regression Power Analysis with Multiple Predictors
So I'm working on my dissertation and I'm trying to figure out a binary logistic regression power analysis to show how many participants I should collect. My proposed analysis is as follows and the ...
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Newton's method and the Hessian for Softmax Logistic Regression
I'm having some trouble with optimising softmax regression via Newton's method. I'm not sure if the problem is arising with my equations for the Hessian and Gradient or with the code I've written.
For ...
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logistic regression for plant growth (R) [closed]
I want to model my data using a logistic regression model. However, I don't understand why it doesn't include all my data points, and I also observe structure in the residuals when working with the ...
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Logistic regression with one variable
Why does the b-value in logistical regression tell how much do the odds change when X increases by one? For example if we have people who smoke and people who don't and we are trying to find out how ...
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Calculating the 95% CI for a 10-point increase in the independent variable in a logistic regression
I am trying to calculate the 95% confidence interval for a 10-point increase in a continuous independent variable in a logistic regression. For a 1-point increase, the 95% CI would be exp(1beta +/- 1....
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Ratio of odds ratio to Cohen d
If I have two binary predictors and a binary response how can we calculate the effect size of the interaction term. I use logistic regression from statsmodels package in python.
Will it be ratio of ...
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Convergence in Logistic Regression
Hey I'm taking a deeper dive into logistic regression. Specifically the following loss function with L2 regularization,
$$l(w)=\frac{1}{n}\sum_n \log(1+\exp(-y_i \cdot x_i^Tw))+\frac{\lambda}{2}||w||^...
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Computing the variance explained by a predictor variable in logistic regression
I'm keen to know how to compute the variance explained by a particular predictor variable in the model (say component specific R squared). I went through Calculate variance explained by each predictor ...
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Is logistic regression with random effects appropriate for my problem?
My problem is the following:
I have 1/0 "conversion" outcomes.
I have samples from three groups: people who speak russian, people who speak japanese, people who speak portuguese. i expect ...
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Logistic regression with industry×year fixed effects on Panel data in Python
I would like to use Python to run a logistic regression with industry×year fixed effects on Panel data (firmID, year). data contain the following variables :
firmID: firm ID (1000)
year : Year (2010-...
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Can I use multiple linear regression with binary output?
I have a dataset with $10$ inputs containing real numbers and an output which is binary ($0$ or $1$), and I need to make predictions. So, I thought of using multiple linear regression to predict an ...
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Is there a justification for the Bernoulli deviance in the R stats package?
Using the standard glm(...) function in R for Bernoulli regression, it appears that the residual deviance has the same value as the binomial deviance where each ...
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Binary Classification - Separating data by categorical data and creating regression for each group
My and friend and I are complete beginners to statistical learning and we were playing around with a data set on loan approval using logistic regression. Each data point contains numeric variables ...
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How to run a nested logit model with fixed effects in R or Stata?
I would like to run a nested logit model with panel data in R or Stata, but I am not sure how to proceed. I want to understand which factors influenced a person's choice to work full time, work part ...
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If my logistic regression model is performing well, does it matter if my features don't pass the Box Tidwell Test?
I've built a logistic regression model for binary classification with a high F1 score, but when I run Box-Tidwell tests on continuous independent features/predictive variables, I find non-linearities ...
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Is it possible to add a predictor variable to a regression that is only relevant for some subpopulations and contains NAs for others?
I'm doing research on labour councils and their behavior regarding qualification. So I'm working with a logistic regression with "visited seminar in the last year" as a dependent variable ...
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P-value for AUC of logistic regression model vs AUC=0.50, how?
In R studio, I am running a logistic regression model with a binary variable vs binary outcome. I get a AUC and a AUC confidence interval. Now my supervisor wants a p-value to show which auc are ...
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Can I keep several features with multicollinearity in the model?
I am creating a sports prediction model with around 300 features. There is a high degree of multicollinearity which breaks an assumption for logistic regression. The issue is that several of these ...
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How to Check Linearity Assumption in Logistic Regression with a Large Dataset?
I am working with a very large dataset that essentially covers the entire population of interest. I want to assess the linearity assumption between an independent variable and the log(odds) of the ...
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Splines, logistic regression and sample size considerations
I have around 500 observations with a binary outcome at 25% prevalence and will be building an internally validated prediction model. I want to use splines to model non linearity in my continuous ...
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A policy evaluation with a dependent variable which is continuous and in (0,1) interval
I am conducting research in which I want to investigate the effect of tax incentives on research and development intensity in a group of firms. I have access to the data of a survey that:
It's only ...
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C-statistic (or AUC) for fractional logistic regression (i.e. continuous regression)
I have proportional data to which I have fit a logistic regression (i.e. fractional logistic regression). The statistician in our group wants me to provide a c-statistic for the regression.
My ...
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Cook's distance for GLM
In Applied Logistic Regression by Hosmer, Lemeshow and Sturdivant (2013), the formula for Cook's distance in a logistic regression is given as,
$$\Delta\beta_j = \frac{r_{sj}^2h_j}{1-h_j}$$
where $r_{...
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Creating a scoring system in binary logistic regression
I have used forwards binary logistic regression to determine which predictors are significant in classifying patients into two clinical categories. Three significant predictors were identified. Could ...
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How are robust standard errors applied in logistic regression
I have been reading up on robust standard errors and had a few questions regarding how their use in logistic regression.
I have read here that heteroscedasticity is not an issue in logistic regression ...
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Statistical test for nominal data, multiple variables and within-subject design in R
I have run a study comparing if a cheap ultrasound device (T) is as efficient at determining osteophytes as an expensive device (V). There was four examiners who reviewed each participant (ID) and ...
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Generalized Linear Mixed Modeling questions
I have a dataset with assessments taken at different visits: baseline (visit=1), and then various post-baseline visits (2, 3, 4), and the following variables:
DISEASE - the outcome, is an ordinal ...
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Example where the initial random state of a logistic regression matters?
I am looking into how random initialisation of a model would impact final results after tuning. This is a well known problem for deep learning (NN or gbdt), notably with random initialisation and ...
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interpreting logistic regression results
I used a continous variable in a logistic regression model. The sign level is .031. The Exp(B) is 1.000 and the B is .000. I'm not sure how to interpret since the B is .000. How could it be ...
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mixed model specification in R (interactions and nesting)
I'm working with data from an experiment that I plan to analyze using a mixed-effects logistic model. In this study, 200 participants (identified by the variable Participant) were randomly assigned to ...
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are these repeated measures?
I have a set of 30 patients. Each patient has a variable number of observations over a variable length of time(ranging from 4 to 10 data points per patient).
My outcome variable is whether they have ...
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Ridge regression gives me this plot. How to interpret it? [duplicate]
I have done this plot with cv.glmnet(), can someone help me to interpret it?
I also noticed that I get 2 different lambdas: lambda.min and lambda.1se
What is the difference between these lambdas?
Why ...
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How to account for overdispersion for GLMM with binomial distribution in R?
I am pretty new to R and am having some trouble finding a straightforward solution to overdispersion in a GLMM with binomial distribution. I have a few different questions listed here. I am mostly ...
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Interpretation of OR in logistic mixed model
I am new to mixed models and am unsure about the interpretation of the OR results. I have a significant main effect of OR = 1.08, 95% CI [1.01, 1.16], p = 0.025.
How can I interpret this? The variable ...
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Doing logistic directly to analyse factors
Is it correct to do logistic regression analysis directly to find out factors related to a disease without doing a bivariate analysis first?
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Controlling for variables in multilevel logistic regression modeling
I am new to mixed models and want to calculate a binary mixed model. However, I can't make much sense of the results and am hoping someone can help me out.
So I want to calculate the probability of a ...
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Interpretation of coefficients in logistic regression
So I have weight (kg) as a predictor variable and FEV1 groups as binary outcome (<3L is 0 and >=3 is 1).
I've centered and standardised the weight variable so weight_centered = (weight-70)/20, ...
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Mean of the means / ANOVA with percentages
I subjected aliquots of 4 replicates (microorganism cultures) to 3 different treatments + control.
In each treatment, I :
1- counted the number of cells whose colour changed in 100 cells of each ...
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Visualising log odds from logistic regression model in R
I'm working on a logistic regression analysis using R and aiming to visualize the effects of the predictor "age" on the binary dependent variable "domestic violence." My dataset ...
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Effect size in multiple logistic regression
I have one dummy independent variable, one categorical independent variable with six categories, and two continuous independent variables. The dependent variable is binary (yes/no). The interaction ...
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Why marginal odds in frequency matched case-control?
Let $D$ denote sampling with $D=1$ indicating subject being sampled and $D=0$ otherwise. $W$ denotes intrinsic variable of subjects, $X$ denotes exposure of interest and $Y$ denotes the binary outcome....
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Q-Q Residual Plots with unusual kinks for both FE OLS and Logistic Regression
I have a dataset on power plant generation, which can be found here.
I use both a FE OLS and a Logit Model (no/some generation)
...
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About centering to accommodate multicollinearity (Ordinal Logistic Regression Analysis, Logistic Regression Analysis)
When interaction terms are used in multiple regression analysis, often centralization of the variables (subtracting the mean of the variable from each variable) is used to deal with multicollinearity, ...
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Can I use quasi-binomial regression on proportion data in this way?
prop.pass = proportion of students who passed the exam
num = number of students who sat the exam
...
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Logistic Regression with fixed Effects and Interaction Term
I am trying to fit a logistic regression model in order to analyze if the fuel type has an influence on how powerplants respond to droughts (e.g. are Nuclear Powerplants more likely to shut down (...
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interaction terms to assess effect of a series of variable on the relationship between an independent variable and outcome variable
Let's say that I have an independent variable x1 which I'd like to explore its association with Y (a binary outcome variable). Furthermore I'd like to explore how x1 associated with Y when x2 ...
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How to compare Observed vs Predicted Outcomes for a Binary Dependent Variable
I have created a binary Logistic Regression model to predict if patients in a particular state, will die based on 10 or so predictors (demographics, income, physiological values, etc.) My dependent ...
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