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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 this variable suitable for a categorical regression (multinomial logistic regression)?

I have created a dataset starting from a series of multiple choice (3 choices) questions. ...
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How to change the default contrasts in R in logistic regression? [on hold]

Say I have done a logistic regression successfully. My code is fit<-glm(Y~x1+x2+x3). Variable X3 have 3 categories, namely None, one and two. When I use contrasts(x3) to get the default contrasts, ...
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Interpreting coefficient of a logarithmic coefficient in a logistic regression

I have a regression with a log-transformed independent variable, and I would like to know the proper way to explain its effect on my binary dependent variable. For example, say the equation is: (...
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Which test you recommend?

Assume in a study the dependent variable is quantitative, while most independent variables are categorical, with some of them being quantitative. We aim to evaluate the relationship between the ...
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Justification for LST model

Let’s say I have a linear model specification but I believe that a logistic smooth transition model would be better. Is there any evidence I can take from the linear model that would point me in ...
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How do you run a regression when categorical variables may be involved, using R?

I have a data set with several categorical and quantitative variables. Say A, B, and C are categorical with several levels, X and Y are quantitative. I know that X ~ A will basically just be a ...
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How to estimate confidence intervals for LC50

This is my first question, so I hope the question is properly done (my apologies if it's not...) I am using a binomial GLM model (logit) for some toxicology data investigating the effects of a ...
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Dealing with Complete Separation in Logistic Regression when Reporting

I have a question regarding how one would deal with complete separation in logistic regression when reporting the outcome for statistical analysis. For a study, we have group participants into 4 ...
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How to reduce predictors the right way for a logistic regression model

So I have been reading some books (or parts of them) on modeling (F. Harrell's "Regression Modeling Strategies" among others), since my current situation right now is that I need to do a logistic ...
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Appropriate statistics for multivariate genetic analysis

I have a question on the most appropriate statistics for our genetic project. We have a dataset consisting of genotypes at several loci (10 SNPs) and phenotypes (the same person can be a control for ...
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1answer
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Statistical tests to discriminate most important variables in populations

I have a dataset that looks like this : ID | group | feature1 | feature2 | .... 01 | 1 | 100 | cat | .... 02 | 1 | 104 | dog | .... 03 | 2 | 30 | horse | .... ..... I have around 10 ...
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What does the logistic calibration line represent on the calibration plot made by val.plot function (from rms package in R)?

I've been trying to make calibration plots/curves for a logistic regression model in R to mimic what I'm doing in a statistics course--but the class uses SAS with proc sgplot. I think I've figured it ...
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increasing accuracy of binary logistic regression by reducing type II error?

I am to use binary logistic regression predict a deadly disease from 109 cases out of 385 patients. If during the preliminary diagnosis all patients were sent to the expert doctor for secondary ...
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Interpreting GLM with logged variable

For my logistic regression model I have: glm(reconv ~ -1 + log(precon) + log(age), data = crime, family=binomial) With the following co-efficients outputted from ...
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2answers
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(Multinomial) Logistic regression with missing values

I want to do a (multinomial) logistic regression to predict 5 different physical activity classes based on different variables extracted for each subject. However, I have one variable (i.e., time ...
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Statistical testing in clinical research

I just did a univariate binary logistic regression to classify a disease based on some medical parameters. Now only one variable showed significant result (p-value < 0.05). Assuming sufficient ...
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2answers
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categorizing a variable turns it from insignificant to significant

I have a numeric variable which turns out not significant in a multivariate logistic regression model. However, when I categorize it into groups, suddenly it becomes significant. This is very counter-...
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1answer
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Survival analysis with incomplete data

I would like to estimate the association between exposure to an environmental contaminant and all-cause mortality. Unfortunately the dataset I can get access to is incomplete - exposure data are only ...
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how to reduce false positive in logistic regression [closed]

I am getting all the true positives. However, I am also getting lot of false positives. How can I reduce the number of false positives? I have dataset of 18k records out of which true positive = 10 ...
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Interpreting log transformations in a logistic regression

This stats.stackexchange post contains explanation of how to interpret transformed variables in linear regression. In particular, I found this snippet in Graham Cookson's answer (2nd answer): Y ...
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How to know whether a random effect or a cluster effect is necessary for a mixed effect logistic regression?

I have 8 variables in my model out of which I have a group which is definitely not a fixed effect. I tried checking the random effect on the basis of the log-likehood test and it seems significant. I ...
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interpretation of parameter estimates [closed]

pls clear the interpretation of negative coefficient value of independent varibale in respect to dependent variable in the following table. ...
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High Accuracy- Seems fishy [closed]

I am trying to build a Supervised Classification based Predictive Model. The data consists of 13 qualitative variables. I built a predictor based on three columns and now I am trying to apply Logistic ...
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1answer
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R: Methodologically sound way to determine which interaction effects to include in logistic regression? glmulti()?

I ran a logistic regression (in R using the glm function) and didn't find significance for a variable I expected to be significant (numerous articles have found significance). When I examined my data ...
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Do small odds ratios observed at multiple time points equate to a large odds ratio when averaged across time points?

I am testing the relative odds of two groups, placebo vs active treatment, guessing that they received active treatment. These guesses were made at four time points, 4, 8, 12, and 24 weeks into a ...
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Can CART models be used to select features for a logistic regression?

Can I use the features selected from the CART(Classification and Regression Trees) model and take those features and then model the logistic regression using those selected features? Then interpret ...
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Logistic regression including instrumental variable (“ivprobit” in R) has coefficients with much reduced significance

I am trying to run a logistic regression including instrumental variables by using "ivprobit" function in R from the package called "ivprobit". If I do not include the instrumental variable, the "glm"...
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Connections between logistic regression, information value and Kullback-Leibler

Suppose that we are interested in modeling a binary predictor $Y=0,1$ subject to $m$ predictors $x_1,...,x_m$. First, let us examine a simpler model of the impact of $x_j$ on the response $Y$. By the ...
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Bayes logistic vs. standard logistic regression model interpretation

I performed a logistic regression using Stata's bayes: wrapper and obtain the following histogram from 10,000 posterior distribution samples of the log(odds) of my ...
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1answer
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how to build multiple independent binary logistic regression classifiers?

I have to build a logistic regression classifier to predict $\mathbf{y}$ given $\mathbf{x}$ where $\mathbf{x} \in \Re^{n}$ is an image and $\mathbf{y} \in \Re^{m}$ is a binary attribute vector (of $m$ ...
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What is the logistic equation for a model with interaction [closed]

I have a GLM with four variables $Y = (X_1 + X_2) * (X_3 + X_4))$ $X_1$ = continuous variable $X_2$ = continuous variable $X_3$ = continuous variable $X_4$ = categorical variable (6 level = A, B,C,...
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Removing outliers in logistic regression

I am running a logistic regression analysis to model if a patient has a specific disease or not. I want to remove outliers because i want my model to be as accurate as possible. For the same I learnt ...
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Is adaptive lasso still unbiased for glm(such as Logistics)?

I'm doing something about penalized Logistics regression with adaptive LASSO recently. But I found that the coefficients from Logisitcs+adaptive LASSO is quite different from the normal Logistics ...
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Interpreting Pearson and Deviance residual graphs

Using a generalised linear model and predicted probabilities, I have been able to plot the Pearson residuals and Deviance residuals. I did this in order to have goodness of fit measures for the model. ...
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Mixed logit with repeated choices coefficient comparison

We are analysing data from the discrete choice experiment using mixed logit model. Data is panel, since we had repeated choices. We would like to compare if the difference in coefficients within the ...
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1answer
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GLM: Logistic Regression Fitted Probabilities Numerically 0 or 1 occurred for non-linearly separable data

I have data that I don't believe is linearly separable. See below; X = 761, 700, 3488, 555, 2784, 1336, 380 Y = 0, 1, 1, 0, 1, 1, 0 My belief is that because of the first two observations I shouldn'...
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1answer
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How do I show that a continuous variable is a predictor of categorical variable that takes the value of 1 or 0 using R

I want to perform a test to see if the continuous variable is a predictor of the categorical variable. More information: I was given identifier IDs with corresponding dichotomous values (Call it CD, ...
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1answer
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Determining what the decision boundary will look like in various scenarios

I'm currently trying to work through this question, but not quite sure where to start. The cases I need to consider are (in turn): (a): $lamda_0$ = 0, $lamda_1$ = 0, $lamda_2$ = $\infty$ (b): $...
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Beta estimates of logistic regression

I am trying to predict the likelihood of violent incidents as a function of time in hour in r. Its a binomial classification problem. ...
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3answers
62 views

How do I model a dependent variable that is a proportion?

Problem I am trying to test gender differences in the risk propensity of investors related to specific kind of stocks. More specifically, I want to the test the hypothesis predicting that female ...
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Sensitivity of regression parameters to noise

How sensitive are the parameters obtained from OLS, logistic or other regression methods to noise ? By noise, I mean minor changes. For e.g. adding a small noise $-1<\Delta<1$ to $\beta_1$ in $...
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Very High McFadden's pseudo R2— adjusted?

I'm running several regressions, half multinomial logit (mlogit in R) and half ordinal logistic regressions (using polr)-- they are different because their dependent variables are of different types, ...
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Accuracy and ROC for Logistic and Decision Tree

So I run a logistic regression and decision tree model using same data. The accuracy shows that the decision tree outperforms logistic slightly. However, my ROC curve shows that logistic is much ...
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1answer
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Re-calibrating Intercept on logistic regression models for unbalance data

I have data-set that I’m modelling using logistic regression as land.cover~H1+H2+H3+H4+H6+H8+H14. My response and categorical variables are binary. However the number of 0 and 1 in my response ...
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Power analysis for ordinal logistic regression (multiple independent variables)

I have a question with regard to a simulation based power analysis for ordinal logistic regression suggested here: https://stats.stackexchange.com/a/22410/231675 (by Greg Snow) The suggested power ...
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1answer
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In generalized mixed-effects model, after fixed effects and variance covariance matrix are fitted, how are empirical random effects calculated?

For example, I would like to fit a logistic mixed-effects model. This article fitting glmm talks about how to fit fixed effects as well as variance covariance matrix of random effects. Theoretically ...
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1answer
32 views

Multinomial logistic regression reference category function

Bit of background info on the data and methods that I've used. I have a forest road data that consists of one dependent variable which has 4 classes (0-3). The Dependent variable reflects possibility ...
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1answer
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Marginal effect of logit is larger than 1

I guess this is more a math question than a statistics question. I do not understand how the value of a first derivative can be larger than the range of the original function. I must have a ...
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1answer
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Want to make sense of array dimensions in logistic regression algorithms

I am trying to implement a simple logistic regression algorithm from scratch in python (for learning purposes). Every article I've seen online so far presents the following expression for $z$ (...
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Goodness of Fit test for Logistic Regression Model

Data have been collected for 176 students. The response variable is 'Choice of Career' having 2 categories namely 'Academic' and 'Non-academic'. I have taken two explanatory variables namely 'Gender'(...