Questions tagged [logistic]

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

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Restricting model parameters estimates to control the weights in logistic models in python/R

Referring to a similar problem as Restricting model parameters in logistic models in R, is there a method such that I can impose constraints on parameters for a logistic model in either ...
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Categorical Cross Entropy Loss Derivation

I understand the categorical cross entropy based loss function to be the following. $$J(w) = \sum_{i=1}^ny_i\ln[P(y_i|x_i,w)]$$ where $$\ln\left[P(y_i|x_i,w)\right] = \sum_{i=k}^Kr_{ik}P(y_i=k|x_i,w)$$...
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What is proper type of analysis for my data? [closed]

I have 10 independent variables (gender and 9 ordinal variables) and just 1 dependent variable consisting of 4 questions with yes/no answers. I converted "no" answers to 1 and "yes"...
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Interpretation of logit versus independent variable plot for logistic regression

I plotted the log odds of my outcome variable against my predictor variables, hwt and ist. There is a hard vertical line in my hwt plot and a hard diagonal line in my ist plot. I have two questions: (...
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Computing $Var [E(y(x))]$ where $\text{logit} y(x)\sim N(\mu(x), \sigma^2(x) + \lambda^2)$

I have a computationally expensive stochastic computer model, $y(x)$ which outputs values on $(0,1)$ and want to use a Gaussian process as a surrogate for $y(x)$. Since $0<y(x)<1$ it would be ...
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Ordinal Regression: Python vs. SPSS

I need some help understanding the coefficients produced by Python (Statsmodels) for Ordinal Regression vs. SPSS. I ran the same exact data set in both SPSS and Python, but received different output ...
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Building a Multinomial Logistic Regression Model

My DV is dichotomous. One component is binary (Y/N) while the other is a spectrum (3 Likert scale question that their z scores were summed up. All predictors are categorical. Control variables include ...
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Sensitivity / specificity with dependent observations

Suppose we have a classification scenario, where each observation is comprised from a reader, a case and the status (0 or 1). Each reader get several cases (but each case is shown to only one reader). ...
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Matched Pairs, GEE Models, and Other Regression Models

I am presenting the following hypothetical example in which the variables may or may not make sense clinically. A study has 100 matched pairs. A matched pair, in the study’s context, is defined as a ...
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Does it make sense to group variables together in a regression model?

Would it make sense to use a variable that consists of two features when estimating the effect on a dependent variable? For example, when estimating admission into a college - would having the ...
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Noise in simulating logistic regression model

I would like to simulate data for logistic regression model as follows, ...
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How to derive local variational close form for logistic regression, analytically

In Prof Bishop's book, Pattern Recognition and Machine Learning, Chapter 10, on local variational inference for logistic regression, page 501, equation 10.161 has been derived, from differentiating EM ...
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does logistic regression work with text data classification ? if not what algorithms work best?

building a simple language identification model (word level). I prepared the data and decided to use the logistic regression model. The trained model accuracy was around 70% . After trying to optimize ...
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Is there an equivalent statistic to partial R-squared in logistic regression?

My dependent variable is a binary outcome(Y); therefore, I am using logistic regression. I am interested in what proportion of variance of Y is explained by the variable X1. In other words, I am ...
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How to decide which variables to keep in logistic regression model based on anova(model, test = "Chisq") and glm output [duplicate]

I’m new to logistic regression and was hoping for some help in picking a "best fit model". Say I have a group of students who are assigned a job after college. Everyone can request their top ...
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Areal random effect logistic regression in winbugs [closed]

Hello team below is what i need to make into a code ..i e. A model in winbugs. i are the sites and j are the farms. There are 32 farms and there are 13 sites.. There are from 1 to at most 4 farms in ...
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Conditional Logistic Regression vs Stratified Cox Regression

I am working with matched case and control data (1:2 matching). And the outcome is binary. In this case, conditional logistic regression can be used to run analysis. To my knowledge, cox regression ...
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What is the best practice for logistic regression data perspective?

I'm running into an interesting problem (for me at least) right now where I'm worried that my data setup is negatively influencing the accuracy of my model. Lets use historical chess matches as our ...
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Why are there differences between the coefficients for logistic regression and SVM classifiers? Is there any interpretation for the differences?

Why are there differences between the coefficients for logistic regressions and SVM even when they give more or less the same results? Is there any interpretation for the differences? I am currently ...
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Repeated measures - predictor varies but outcome doesn't

I have been asked to help analyse some data for a research project. The outcome is binary (presence or absence of a particular disease) and the predictor of interest is a cell count. So logistic ...
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How to interpret anova(model, test = “Chisq”) for logistic regression model?

I’m new to logistic regression and was hoping for some help in picking a "best fit model". Say I have a group of students who are assigned a job after college. Everyone can request their top ...
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1answer
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How to avoid selection bias while updating lead scoring (predictive) model with new data

We developed a standard lead scoring model using logistic regression on couple of months worth data. The model has been working and we have been pushing only top 1/3 leads to sales team basis that. ...
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Interpretation of coefficients in logit model

We know that in a logit model, the coefficient $\beta_j$ for the variable $x_j$, measures the impact of the variable on the log(odds) In order to measure the impact on the Odds, we have to consider ...
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interpreting odds ratio for logistic regression

I understand the general interpretation of the coefficients for logistic regression. Say we have a regressin like this logit(p) = a + b1X1 + b2X2 If ...
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How to make a stratification in a penalized logistic model with the penalized function?

I use the R package "penalized" and I want to fit a stratified penalized logistic regression model. In the package vignettes, I found: It is possible to include an offset term in the model. ...
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Binary classification but learning the true probability

I have a dataset $D = (x_i, y_i)_{i=1}^n$ where $x_i\in \Bbb R^d$ and $y_i\in\{0, 1\}$. Suppose that $y\sim\mathrm{Bernoulli}(p(x))$ for some probability function $p:\Bbb R^d \to [0,1]$ and I would ...
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Obtaining individual level classifications from predicted probabilities

I need to produce predictions for a binary state at the individual level. The response variable is imbalanced, about 99:1, with the positive class being the minority. Each row in my dataset represents ...
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Can I use a logistic regression as a calibration curve?

Lets use basketball as our example. The use case: There is a model that predicts the probability that the favored team will win. The probability range then is necessarily constrained between 0.5 and ...
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Interpreting difference in logistic regression coefficients after data handling

I am building a multiple logistic regression model in which 10 variables (1 variable of interest and 9 covariates) are included. In total, I have 274 rows of data. 12 rows of data are unfortunately, ...
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Can you use AIC scores with mixed-effects models?

Is it possible to do model selection with AIC scores having linear mixed-effects models? How about binary logistic mixed-effects? Moreover, how should models be specified? e.g., if we have a model ...
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R: How to fit logistic regression with shared measurements?

I have 300 individuals who rated binary responses (1 or 0) on the same set of 500 questionnaires. I want to fit a logistic regression model to explore the association between the odds of rating (1 or ...
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How to check if a probability from a logistic regression model is significantly above chance level?

I've made a binary logistic regression model with three different variables X1 (three levels) X2 (five levels) X3 (two levels) to compare the chances of being sick or not (YES/NO). From the logistic ...
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Hunting "Sweetspots" In Predictive Models

I have three separate predictive models that each accept a set of inputs about an upcoming event and produce a probability of that event being true. I have domain knowledge that leads me to suspect ...
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Showing a model is logistic

If we suppose that the true relationship between the probability of disease ($D=1$) and the level of risk factor $X$ is given by the logistic model $$P(D=1|X=x) = \frac{e^{\alpha + \beta x}}{1 + e^{\...
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Interpretation of Logistic Regression Predictions

So the logistic model can be formulated as a GLM with a logit link, and inverting the logit link gives us the logistic function that outputs class probabilities. It follows then that a fit logistic ...
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1answer
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Contrast for logistic regression using rms

I am conducting a logistic regression with an interaction term involving two categorical variables. One variable (variable a) has 7 values and the other has 3 (variable b). I want to run a contrast ...
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Question on interpreting a logistic regression with information from the confusion matrix

So I have a logistic regression and the I have the following information as outputs: The probability of the y variable being either one or zero a normalized confusion matrix as follows: T zero ...
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Rescaling random intercept coefficients from hierarchical logistic regression

My logistic regression model includes an overall intercept, multiple categorical variables + and continuous covariates like so: $logit(\mu)$ = $\beta_0$ + $\alpha_{j}$ + $\gamma_k$ + $\beta$$X$ where $...
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Why does a subset of variables produce a higher AUC value than all variables in a logistic regression?

I have to predict when the soil dries out. The dependent variable is therefore binary (the soil is wet or dry). I have a lot of variables, and I have clustered them together into three main clusters. ...
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Validation of Logistic Regression

I don't have a great grasp on logistic regressions, however I am trying to analyse the factors that determine plant naturalisation Dataset can be found here https://filebin.net/yoi8hafn1jj3t2tf The ...
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Logistic Regression, ''Intercept'' instead of a Category [duplicate]

I am currently trying to analyse the effect of an ilness (0= no infection, 1= infection) on 9 different Genotypes in plants. My Dataframe consist of 2 colums Genotyp and Infection. I have 459 rows for ...
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Intercept of logit vs intercept of multinomial logit

If I simulate the success probability for a Bernoulli random variable $t_{binary}$ conditional on some regressor $x$: ...
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Logistic regression on Pooled data

I have a database with N firms, and observations for several variables (both numeric and binary) for each firm per year. As an example: ...
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Logistic regression multivariate : Odds ratio / Wald-test

I have a small problem with one of my analyses. I'm doing a multivariate logistic regression so some continuous variables have been discredited by quartiles. Here are my results: OR and IC Variable ...
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Utility functions in multinomial logit model (NLOGIT software)

NLOGIT software allow to edit the utility function for each alternative. Format looks like below, ;model U(alternative 1)=<utility function 1>=constant1+coefficientsvariables U(alternative 2)=&...
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What variant of logistic regression is correct here?

Here is my setup: I have M=200 municipalities who all rank high in corruption For each municipality, I pick a random sample of ...
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Difference-in-differences - Binary data

I am working on an exercise using conversion rate data on a travel website. The conversion rate is defined as the number of users in a given time period that make a purchase. There are two groups, A ...
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Understanding differences between binomial and multinomial models

I'm attempting to model how 2 predictor variables affect the relative proportions of 3 different categorical groups. I started off by running a binomial model for the proportion of each individual ...
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How to fit two (positive and negative) logistic functions using the same independent variable

My binary dependent variable (pollinator presence) responds to the independent variable (air temperature). With increasing temperatures there is a logistic increase in probability of pollinator ...
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Computing logistic regression on variables of different years?

I am very new to R and statistics in general, so it is likely I am adopting the wrong approach. I have a large dataframe made of binary, character and numeric variables with n observations per year. ...

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