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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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How to avoid overfitting by entities in short text classifcation?

I am binary classifying headlines. This headlines are between 1 to 7 words, and sometimes include the name of the person who created them or locations. I am able to classify with 81% precision but ...
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Use of a predictor variable for a dependent variable that is directly related to the predictor variable

If I would like to predict a binary variable x, and x is true if y is true and ...
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Question regarding statistical methodology that involves logistic regression

I used following simulated data using R to demonstrate my problem. require(lmtest) require(splines) x=rnorm(20 ,0,1) y=rep(c(0,1),times=10) Although my simulated ...
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Diagnosing logistic regression [on hold]

I want to test logistic regression for accuracy using CHAID algorithm , so how can I do it in R?
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What is metrics.roc_curve and metrics.auc measuring when I'm comparing binary data with probability estimates?

I was working on a challenge, and I was excited because the metric.auc for my predicted values compared to my test values was very high. This was for a binary selection process. However, when I ...
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Replacing Variables by WoE [on hold]

How to Replace Variables by WoE (Weight of Evidence) and then use it in Logistic Regression in SAS. Sas enterprise miner gives a convenient way to do this, but how to do it in sas 9.4? Can you please ...
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spss GLM AIC and BIC

I have a dataset which contains categorical and numerical predictors, and a binary logistic response. I need to select a best binary logistic model, and to achieve this I use function "Generalised ...
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1answer
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Discrimination (pseudo) $R^2$ vs. C-index

In the context of binary logistic regression. Both pseudo $R^2$ and C-index measures the discrimination of the model. But why do you need both ? can you gain something from one but not from the other?
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Is it ok to have “fitted probabilities numerically 0 or 1 occurred” in the case of predicting disease? [duplicate]

I know this isn't directly related to statistics but I didn't know where else I should post this so please let me know and I will remove the question if this is the wrong forum. My question is very ...
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23 views

R: How can I calculate the F-statistic of a logistic model in R?

I am running a logistic regression in R and I noticed that the output does not include the F-statistic which shows the overall significance of the model. In another post, the formula for the F-...
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Finding outliers in binary data

Say you have data from 10 different sensors about the occurrence of some event - e.g. motion sensors. Each sensor records 1 if they detected the motion sensors and 0 if they didn't. If you have 10 ...
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A single logit model to estimate the churn of more than 1 product: how can i deal with a different % of 1's? [on hold]

I need to estimate 1 single logit model to predict the probability of churn for two different products (25% prod1-75% prod2). Each product have a different churn rate (7% vs 10%). I have not enough ...
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Standard Error of Maximum Likelihood Estimation [duplicate]

Can anybody tell me how to find numerical values for standar errors of the Maximum Likelihood Estimation of Binary Logistic Regression?
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How to know if there is a difference between two populations or if some feature has influence on a certain response variable Y?

I have a database about claims at auto insurance. I can do the math and get Frequency, Severity, Sinistrality. I need to know if cars with automatic transmission have a higher propensity to accidents ...
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22 views

“marginal standardisation” for comparing predicted probabilities between two groups? [on hold]

I have a doubt regarding to the R package "margins". I'm estimating a logistic model: ...
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3answers
35 views

How can we plot the predictions of logistic regression model in order to see whether it is good?

I am working on a basic problem that requires developing a logistic regression model (the output is True/False, whether a person gets cancer). I have used glm() in R and got the model with some ...
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Can Logistic regression be used as a Linear Regression model [closed]

In a question I'm given Construct a linear model and see how well the fat content can be estimated. That is, estimate the generalization error with a linear model. Optimize the number of ...
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How to determine which variables are statistically insignificant in multiple regression?

Currently, I am using R to analyze data. The data has 5 columns to it (glucose, glucose tolerance, insulin, insulin resistance, presence of diabetes(yes or no), presence of diabetes in numerical value(...
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The strength of negative regression coefficients

I realize that this is likely a dumb question, but does a logistic regression coefficient of -.222 demonstrate a stronger effect than a logistic regression coefficient of -.087? The negatives confuse ...
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Incorporate new information into admissions prediction model? [closed]

I am at a small college and we are doing basic admissions predictions for who will attend. We have built a basic logistic regression model that aims to predict the probability of students who we ...
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Outliers and influential observations in elastic net logistic regression

My dataset has many biomarkers and the boxplots of these variables show the presence of many outliers. However, these 'outliers' are real data and not misread observations. I want to use elastic net ...
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Calculate odds ratios and P-value for interaction across multiple separate subgroups

I'm trying to understand/replicate an adjusted logistic regression analysis where a treatment effect is estimated separately in a number (>2) of subgroups and estimating an overall P-value for ...
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Binary models with the regressor that has Bernouli distribution [on hold]

I have a binary dependent variable, but my regrerssor also has Bernouli distribution. Will logit still give a consistent estimator in this case? How can I estimate? Is that right? model: y=b1+b2*x b1=...
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2answers
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Logistic regression with missing data: which rows/columns to eliminate? What is the most simple method?

I have a large dataset (501 rows and 39 columns) with a lot of missing data. I have already deleted all the rows where the (binary) response variable is missing as well as three columns that were ...
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How to choose estimates after Bayesian regression?

In a Bayesian logistic regression with two predictor variables $x_{1}$ and $x_{2}$, I did MCMC (2000 samples) to estimate posterior distribution. Now it's done, how can I choose the final estimates ...
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Equivalent of ggforest for logistic regression in R? [closed]

My question concerns R! There is a perfect package (survminer) that allow beautiful forest plot of cox regression models, with the ggforest function. the output is as followed: My question is very ...
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Could grid search result in lower score

I worked on a data set for prediction a heart disease. I used Logistic regression and got score of 0.85. To improve it I used Grid Search cross validation on the hyper parameter C and got that the ...
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Predicting values in a data frame using appropriate log-logistic model

I am supposed to predict the concentration values (conc) for control and treat group in the column in the data frame, ref values should stay the same. ...
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1answer
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How should I model this data?

I need to use R studio to model the following problem: According to the Independent newspaper (London, March 8, 1994), the Metropolitan Police in London reported 30,475 people as missing in the year ...
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1answer
28 views

Should I get better accuracy predicting rare events or their reverse?

I remember whenever one needs to predict rare events (naive bayes classifier or logistic regression) that it is smart to simply predict the reverse of the rare event, which is far more common one can ...
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2answers
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Which regression should be used when both dependent and independent variables are nominal? All have multiple classes

The aim is to find which team will win the game based on head to head data. All the variables are nominal and have more than 2 classes. For now, I have coded them as dummy variables and have performed ...
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1answer
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why data normalization is important for models when parameters can manage the feature weight/importance

When we study about normalization, various facts are given to explain the necessity. The most important of all is that: Normalized column if in higher range than others can have more impact on ...
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conditional trees unexpected grouping — differences with chi squared test

I grouped some data with ctree (party package, conditional trees) (binary target) and the result is: ...
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1answer
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Why do we need regularized logistic regression?

We use regularized Linear Regression to prevent the model from overfitting (reduce model complexity). Does the same idea hold with regularized Logistic Regression? Is regularized Logistic ...
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Summarize regression results on different datasets

I have 20 datasets (extracted from 20 different software systems) and fit a logistic regression model on each of them. On every dataset, the model was the same (same dependent variable and the same ...
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Logistic regression with repeated mesures and unique outcome

I have one independent continuous and time-dependent variable X, repeatedly measured (from 1 to 4 times) in different patients during some period of time. My dependent variable Y is binary and is ...
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Logistic regression of both ordinal response and explanatory variables [duplicate]

Here the last column is my response variable. I have four predictors that are ordinal. How best to code them to preserve their "ordinal" properties?
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Preparing a GLM logistic regression: choosing the factors

I am researching the incidence of pain after an operation, according to anaesthetic type. Univariate analysis is inconclusive, but I would like to proceed to multivariate analysis. I have done some ...
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we say that logistic regression produces well calibrated models, is that still true for neural nets trained in batches?

This link has previous discussion about LR producing well calibrated models: Why does logistic regression produce well-calibrated models? Some people equate neural net based prediction models (even ...
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1answer
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Precision/recall curve and accuracy/threshold interpretation

I am running a logisitc regression and trying to interpret the predictive power it generates. How should I interpret the precision/recall curve and the accuracy as a function of threshold? My ...
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1answer
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Interpretation of the following logistic regression problem

I have a function that gives the probability of Y=1 given X i.e P(Y=1|X)=f(wX). This function is dependent on variables w and X and I have to give the range of w ...
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Logistic Regression Class Imbalance and the use of weighting and undersampling

I have been working on a machine learning model using Spark (binomial) LogisticRegression. The dataset has what I think is a high degree of imbalance - roughly 1% of rows are labelled as events. The ...
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1answer
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Logistic Regression bootstrapping gives 0 bias and standard error

I'm an R newbie and I'm trying to use logistic regression to predict Admission granted using 4 dependent variables - GPA, Gender, International student or not and SOP grade. Since I have only 113 data ...
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goodness-of-fit for logistic regression with a ratio dependent variable

My dependent variable is number of days in a week a certain activity occurs, so I figured I would express it as a percentage out of 7 (days) and model it using logistic regression. I would like to ...
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Statistics for a probability parameter

I am measuring how humans perform a perceptual test. The graph shows the proportion of correct responses as a function of an independent variable x for one person. The model includes a lapse ...
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30 views

Why Does Perfect Separation Make Logistic Regression Prediction Impossible? [duplicate]

I had this issue a while back where perfectly separated data prevented a logistic regression model from being created. But why does this fail, from a mathematical perspective? Sorry if someone asked ...
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1answer
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Understanding the logistic regression model from glmnet in R when the binary response is -1 or 1

I compared the results for the cases with y = {0,1} and y = {-1,1}. The estimated coefficients and probability from the method are different. How to understand these results? ...
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Multilevel logistic regression for 3 by 3 factorial design? Sparse matrix problem

For the sake of example, let’s say this is a costumer research study. I have a binary outcome (x) that is either making a purchase or not making it. I have three independent variables: store ...
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Logistic regression - binary to continuous - how to interpret?

Given data with a binary outcome, i.e.: $0$ = no event, $1$ = event which can be modeled with logistic regression, how then do we understand the following logic: ...
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Logistic regression: Understanding convergence towards coefficients from synthetic model

In preparation for working on real-world datasets, I am exploring classifiers on syntethically generated data. First I generate random variables $X_1 ... X_8$ that represent observables with physical ...