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

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Should I keep or eliminate an insignificant confounding variable?

Let's say that I am fitting a logistic regression model for a binary outcome and I have two covariates: $x_1$ and $x_2$ (both quantitative). I am confused as to what the correct course of action ...
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ROC and post estimation COX Harrell's C using your dataset

I have built a predictive model using a combination of logistic and cox regression models. I did it using a dataset of about 5000 records. I would like to calculate the AUC and the Harrell's post ...
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Diagnosing Unusually High Prediction Accuracy in Logistic Regression Model

I have constructed a logistic regression classifier in Matlab, using all self-written code. The data set I decided to use is the Breast-Cancer data set from UCI's machine learning repository. This ...
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Interpreting p-values of log regressions

The following output is for a log log model. ...
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How to interpret p-values on log log model?

If the p-value of log(independent variable) = 0.0023 for the hypothesis that the independent variable=0, how do I interpret the p-value? Does this interpretation change if this was a simple linear ...
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Testing if variables have a non-linear relationships with the dependent variable

How can I find evidence that a independent variable has a non-linear relationship with the dependent variable? Can I possibly achieve this by squaring all the independent variables and estimate a ...
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134 views

Binary logistic regression with only positive training examples - does that even make sense?

(I have learned about polynomial linear regression, logistic regression, and neural networks.) I have a binary logistic regression problem. I need to classify things to be true or false. What makes ...
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Logistic Regresion / SVM / Random Forest Implementation in Matlab

I would like to implement (L2-regularized) Logistic Regression, (L2 regularized) SVM and Random Forest for multiclass classification in Matlab (without using a toolbox or the corresponding functions ...
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Does it make sense to generate prediction intervals for the estimates of a logistic regression?

Say I have a binary outcome of 0 or 1 and suppose I were to use logistic regression model to estimate the probability a new sample will have an outcome of 1. I have read answers (for example here: ...
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Treatment for High Population Stability Index

What are the ways we can stabilize population if we have high population stability index greater than 0.2 in a predictive model? Or how to adjust if it is less than 0.2 but greater than 0.1?
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GEE Logistic Model with Subject Specific Predictions?

I have fit a marginal logistic model or GEE Logistic Regression model using SAS' proc genmod to obtain estimated parameters associated with mortality (death). Using SAS, I am able to obtain ...
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19 views

How to validate cluster formed in cluster analysis? [on hold]

Various methods such as One-way MANOVA, LDA and logistic regression have been mentioned in literature. Could you please suggest how to choose a method out of these? and Why? In R for cluster ...
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Do odds ratios have an effect on the variance components (level-2 variance) in multilevel models

I am estimating logistic multilevel models and have a question regarding the variance components (i.e. level-2 variance). I want to report my results as odds ratios and I am wondering if the ...
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Predicting individual treatment effects as the difference between predicted outcomes with and without treatment

To provide some context, I am trying to (a) identify the best ad to increase support for a particular issue among a large group of people, and (b) identify the people most likely to respond positively ...
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Why does the inclusion of an intercept in my logistic regression cause my $R^2$ to decrease dramatically

I am running a logistic regression in order to determine the error rate of an outcome given some covariates. Two of my covariates are indicator flags for the location. When I include an intercept, ...
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Type of predictors for logistic regression

Can predictors in logistic regression be categorical, numerical and ordinal? If categorical, can they be trichotomous?
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Switching “outcome” and “exposure” in multiple logistic regression

I read this question on whether switching the "outcome" and "exposure" changes the odds ratio in bivariate logistic regression. Which it does not. I'm wonder if this also holds for multiple logistic ...
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clogit in R: original variable or demeaned?

Conditional logistic regression is a fixed effects model. If you're modeling the dependent variable $y$, a glm fixed effect model doesn't actually model $y$. Instead, the glm fixed effect models ...
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Power Analysis for Logistic Regression with one nominal variable

How do I estimate sample size or do power analysis for logistic regression with one nominal independent variable? Is there a way to do it with Stata?
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36 views

How to separate categorical variables in modeling

Suppose I have a dataset as follows: ...
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Multinomial logistic regression and interaction [duplicate]

I am running a multinomial logistic regression and have my final model, but now want to check for interactions between my two exposure variables and my independent variables. When I run this, one of ...
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Why do logistic curve gives a very good fit to USA's population projection but it does not so for other? [on hold]

I'm studying population science and working with population projection right now. Can any one please describe the reason behind very good fitting of Logistic Growth Model in case of the projection of ...
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Is it possible to test for a linear trend when running a logistic regression?

I have a dichotomous DV and a single factor with three levels. Is it possible to test for a linear trend in the log-odds for each level of my factor?
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distribution of residuals in logistic regession

I am fitting binary outcome using generalized linear mixed model (glmm). I checked the Studentized and Pearson residual and they do not seem to be normal. Is it expected that residuals in logistic ...
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How to treat variable in logistic regression?

I have a variable I do not know how I should handle my logistic regression. The variable is the number of registered students each semester. If I plot it against my binary outcome, I get the following ...
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Ordinal Logistic Regression Predicted Probabilities

I'm looking for a way to produce a matrix of predicted probabilities on data that went through SPSS's logistic regression test. I only use two ordinal variables with a range of 1-4 and 1-10 ...
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Interpreting odds ratios as percentages?

## OR ## (Intercept) 0.0185 ## gre 1.0023 ## gpa 2.2345 ## rank2 0.5089 ## rank3 0.2618 ## rank4 0.2119 ...
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Modelling a binary outcome when census interval varies

For a current piece of work I’m trying to model the probability of tree death for beech trees in a woodland in the UK. I have records of whether trees were alive or dead for 3 different census periods ...
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What goodness of fit tests, for logistic regression models, are available in R?

I'm planning to work on some credit risk models using logistic regresson in R. Binary response. What all goodness of fit tests are to be known and WHAT PACKAGES are required for the same? Thank you. I ...
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Interpretation of the Odds-Ratio for percentage value in logistic regression [duplicate]

Running a logistic regression, I have a dependent variable Loyal Customer can be 0 or 1 and an independent variable ...
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Do we need to adjust sampling weight in logistic regression?

Suppose, I am using malnutrition data from Demographic and Health Survey. This survey used multistage cluster sampling. Here is a sampling weight (probability weight). If I want to say nationally ...
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Logistic model cross validation error in SAS [closed]

I am using sas and want to fit a logistic regression model to predict the prob of long male life and construct a 2*2 table for cross validation rate. But I don't know why my code does not work. ...
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Non-significant interaction effect

I currently have a regression where adding an interaction effect between two significant variables (a float and a boolean) makes them non-significant. Given that this interaction effect is not ...
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using both raw and weight of evidence values in logistic regression

I am building a logistic regressin model for probability of take-up for a lending product. I have a number of continuous variables. In the past, I have always used EITHER weight-of-evidence ...
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Modeling a proportion using longitudinal data

To illustrate my question I'll make a (very) fictional example. I have a set of 17 year old people that every year report how many cigarettes they smoked and how many miles they ran. Very few of ...
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How is the F-Stat in a regression in R calculated [duplicate]

I am running a regression and I'd like to be able to do the calculation to get to the F stat .3062. How is this .3062 calculated? Can you help? ...
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Rule of thumb for sample size for mixed-effects logistic regression analysis?

Is there a simple way of calculating the minimum number of participants (and/or items) needed for a mixed-effects logistic regression analysis? In particular, what should I do if I don't know what to ...
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How to fit an logistical autoregression in R?

I modeled the relationship of $X$ and $Y$ by the logistic function. The residual plot displays autocorrelation which I'd like to rid. I want to try adding trend component to $X$, thus the model ...
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26 views

Logistic regression (multinomial)

Having trouble getting my head around this. I should mention, I am not a staistician and all my 'knowledge' is self taught. I am trying to compare 4 hospital sites for patient outcome (either ...
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How to interpret my coeff/ORs when the main effect of my two predictors is significant but not the interaction between the two?

Apologies as this has been posted before in similar topics but still I am trying to stretch my brain to understand it. I have panel data, I am looking at both binary and count variables as outcomes ...
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Can you use a single set of weights for all classes in logistic regression?

For this question I want to specifically focus on the the method in this tutorial: CRF tutorial See equations 1.6 and 1.7 $p(y|\boldsymbol{x}) = \frac{\exp\left \{{\lambda_{y} + \sum\limits_{j=1}^K ...
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The intuition behind the different scoring rules

Consider the three scoring rules in the case of a binary prediction: Log: sum(log(ifelse(outcome, probability, 1-probability))) / n Brier: ...
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How can I calculate the AUC for softmax classifier (e.g., logistic regression)?

At the end of a convolutional neural network(CNN) , there are usually a softmax classifier attached to it. How can I calculate the AUC for the CNN (that is, for the softmax classifier)? Thanks!
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Why are results different from R mixed effect logistic regression models with nested random effects?

I have a dichotomous outcome on 2500 individuals. From 18 geographical areas, and many households nested within areas. I need to assess the association between various predictors and my outcome, ...
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Reporting a binary logistic regression

The binary logistic regression model was found to be non-significant for my results. What should I include in my results section?for example, would I still report the Nagelkerke Rsquared?
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Discrepancy between logistic regression and logistic regression results?

Suppose I have a data set of 200 controls (group A; has no memory problems) and 100 cases (group B; has memory problems). And I'm looking at the relationship between memory and cognitive test score ...
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How should I treat my covariate in R?

This is covariate age in my logistic regression. How should I treat it? Gets a little insecure. Have tried to read, but still insecure. Right now I treat it as if it were linear. A polynomial is not ...
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42 views

Large value of exp (B) in binary logistic regression SPSS what is wrong? [duplicate]

I had a very large value for Exp(B) in SPSS binary logistic regression. What is wrong and what should I do?
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A higher odds ratio but a broader confidence interval

I have derived two logistic models. one has just the predictor. Other has the predictor adjusted for few co-variates. The odds ratio of the predictor in the first model is 4.6 (1.39-16.70) and the ...