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

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Logistic Regression Odds Interpret

I was analyzing the different treatments applied to products. It started out with a a plethora of variables but I came to the conclusion that I can essentially only control the treatment applied so I ...
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what does it mean when out of sample AUC is greater than in sample AUC?

I am fitting a logistic regression model on a data set with about 200,000 observation and 100 features. According to SAS output, the model converged correctly with an in-sample AUC of 0.85. However, ...
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Use fitted value from regression on subset of features as independent variable

I am working with a relatively large data set with 2K columns and many variables can be grouped together (a logistic regression). So I am thinking can I use fitted value from regression on subset of ...
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Can multiple logistic regression be performed without a reference/baseline?

I was wondering of it's possible to perform a multiple logistic regression without a baseline reference. The analysis I'm dealing with doesn't have a "natural" baseline reference. Thanks in advance
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How to evaluate collinearity or correlation of predictors in logistic regression?

In linear regression it is possible to render predictors insignificant due to multicollinearity, as discussed in this question: How can a regression be significant yet all predictors be ...
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Partially sparse vectors for training classifiers

Is it a bad idea to use a partially sparse vector for training a logistic regression classifier? By "partially sparse", I mean that about half the vector is actually dense, with real valued numbers ...
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Interpreting interactions in logistic regression output [duplicate]

Using chi-square analysis, I find significant p-values for age (as a continuous predictor variable) and presence of a hip fracture (as a dichotomous categorical predictor variable) for the occurrence ...
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Category prediction in ordinal regression

This could be a naive question. I have three slabs for my dependent variable and when I run the ordinal regression the predicted responses are in first and third slab but no observation is predicted ...
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Is it possible to fit a logistic regression model to a dataset with categorical predictive variables with very high number of levels each?

I want to fit a model to a very large dataset, with a standard binary response variable and with 3 categorical predictor variables with 3000, 15 and 2 levels. Is there any inherent problem in this ...
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Missing value replacement in modeling and scoring

Here I have two questions I build a logistic regression model. While building model I have few observations have NA values, so I replace with mean value. Model is looking good and when we tried to ...
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Low Accuracy using online logistic regression in mahout

I am getting very low value of accuracy on running online logistic regression on standard iris data (150 records). public static void main(String args[]) throws IOException { ...
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Enumeration of covariate patterns in multiple logistic regression

Is there an easy way to identify and flag covariate patterns in SAS or Stata? Working in the context of multiple logistic regression so it would be very difficult to set up flags for each variable ...
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Graph with 2 interacted continuous predictor vatiable

When using glm(link=logit), I detected a significant interaction between two continuous predictor variables. How can I present the results visually using R?
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adjustment of covariates in linear model

I am trying to understand the adjustment of covariates in the linear model such as multiple logistic regression. How does adding a covariate adjusts the coefficients for that covariate (any intuitive ...
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logistic regression- validation dataset

I am working on getting propensity of Households to buy a certain product, I have completed the training dataset for running proc logistic in SAS, my question is 1) My training dataset is a biased ...
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How to answer a clients question on “How accurate your logistic regression model is?”

There are various methods to test the model accuracy, but when it comes to clients you may face people who don't know AIC, ks-statistic, c statistic, confusion matrix, etc. So, how one should answer a ...
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MLE vs MAP vs conditional MLE with regards to logistic regression

We have some set of iid RV's: $(X_i, Y_i), \; i=1,\ldots n$. We believe each to be distributed as $P(X_i, Y_i | \theta)$. So that $$ P(X,Y | \theta) = \prod_i P_i(X_i, Y_i | \theta) $$ Now using ...
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How to deal with unequal sample sizes while fully embrace a dataset?

Imagine the situation: Mythical Seafolk use holes in the seabed as their burrows. Each hole has two parameters - diameter and depth. Majority of holes are unoccupied due to their surplus (n = 235). ...
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Interpretation of multiple logistic regression with interactions in R

I am trying to look at whether 2 variables (one dichotomous categorical and one continuous) predict the occurrence of a dichotomous categorical dependent variable. ...
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Results with and without interaction

I'm working on an analysis with another person. First we did a logistic regression with study group and variable X. They were both significant. Then we added the interaction between study group and X ...
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Why am I getting different results for my logistic regression when performed by different software?

My data is simple, my independant variable is continous from 0-1000 and the response is either a 1 or a 0. I'm performing a logistic regression to determine the 50% inflection point. When I put this ...
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Logistic Regression, SVM or NN?

Just attended Andrew Ng’s online course on ML and although I’ve understood the methods I seem to be missing the intuition on where to apply them in terms of classification problems. What are the ...
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Logistic regression, Chi-square, and study design

I have a study in which I have developed a new predictor (binary) for a disease (also a binary variable). The study has two parts. In the first part, I want to test if my predictor is strongly ...
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Comparing two models

I am interested in comparing two logistic regression models. The two models are nested: model 1 contains all predictors, and model 2 contains all predictors except 1. My goal is to test if removing ...
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Why might UCINET and R return different results for a QAP Logistic Regression (on the same data)?

I'm trying to run a QAP logistic regression to predict the odds of a tie in a social network (represented as a binary adjacency matrix) given two independent variables (also binary matrices) but am ...
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How to simulate a binary response variable based on two non-interacting continuous variables [duplicate]

I want to simulate a binary response variable which depends on two normally distributed continuous variables, and I want to have more 1s than 0s in the response variable. I wonder how this can be done ...
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Should the average prediction for a given attribute value equal the rate for that value?

Let's say I'm predicting the likelihood that someone will buy a widget, using their age, eye color, and gender as input attributes. I split my data into a training set and a test set, and train up my ...
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How do I choose between a simple and a mixed effect logistic regression?

I have a list of predictor variables to put in to a logistic regression model. How I know that should I do a simple logistic regression (using glm function in R) or ...
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Can value of predicted probability from logistic model be greater than one?

I derived a multivariate logistic model from my data containing a single binary response and five predictors. I tried to calculate the predicted probabilities of one of the binary predictor ...
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Concern about collinearity when adding gender and gender-specific comorbidity for prediction of disease risk

I am build a model to predict the risk of having disease X, let say I have a series of variables and I select the variables to be included in multivariate logistic regression model by: i) clinical ...
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How to estimate model with both linear and exponential parameters?

I have a theoretical growth function that can be perturbed by events, and I'd like to estimate the growth parameters as well as the perturbation, and the rate of falloff after that perturbation. I'm ...
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Apply LASSO Model with nominal target in SAS?

I'm building a classification model with a pool of independent variables (hundreds of them). I'm in the step of variable selection/feature selection. Now I'm trying to figure out if there are any ...
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Is there a binomial regression model that captures data with fat tails?

Specifically, are there any binomial regression models that use a kernel with heavier tails and higher kurtosis than the standard kernels (logistic/probit/cloglog)? As a function of the linear ...
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In regard binary logistic regression, which method is better: enter or one of the forward or backward elimination methods?

I am analysing a set of data where I try to predict an outcome (Level of women’s nutrition knowledge; whether it is High or Low) by using certain covariates (demographic characteristics of the ...
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Strange GAM results, logistic regression

I am trying to fit a Generalized Partial Linear Model using the package gam in R. I have one continuous predictor EDUC and 3 ...
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What is the form of link function in this BUGS multinomial regression example?

The alligators example from openbugs examples repository is the same example that comes with winbugs. Basically this is a multinomial logistic regression example in which the outcome variable has 5 ...
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How to choose data for training a predictive model for attrition prediction

Trying to build a predictive model for attrition prediction at service desk/call center. Have daily data on the following parameters: 1.Call quality - QTM (0-100%), 2.No. of calls - Calls(Number) ...
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Logistic Regression : How to obtain a saturated model

I just read about the deviance measure for the logistic regression. However, the part that is called saturated model is not clear to me. I did an extensive Google search but none of the results ...
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estimate the log odds-ratio in R

I fit the logistic regression model for gender and drink for the data ihd using the following command ...
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Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the ...
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Identifying What Causes a Variable to Increase

Say I have a dataset with several continuous and categorical variables, and I want to identify what variables (values or properties of these variables) may cause one of the continuous variables to ...
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Does fixing coefficients in a regression make sense, and if so how to do it?

I have a generic question about whether it might sometimes make sense to fix specific regression coefficients to predetermined values. And if this makes sense in particular cases, how do you best go ...
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How to customize a link function to perform a logistic regression?

My data was collected using Randomized Response Technique. So I have additional variability into the data. I have a binary response variable. Should I customize a logit link function to incorporate ...
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how to interpret ordered probit model?

Below I attached the output I got from Stata for an ordered probit model. Can someone please help me to write a model using below result. My response variable has only 4 values (1,2,3,4) How to use ...
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Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression

I have a question about omitted variable bias in logistic and linear regression. Say I omit some variables from a linear regression model. Pretend that those omitted variables are uncorrelated with ...
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Propensity score matching: using alternative methods to create a distance measure

I would like to use a greedy nearest neighbour method to do propensity score matching. Though I've little experience here, it seems that the distance measure used is generally a propensity score ...
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Logistic Regression with different priors

I am using standard logistic regression for classification with reasonable results. As expected I get a probability of 0.5 for query points "far away" from the data. However I would like to assign ...
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Bootstrapping in SAS - PROC LOGISTIC - Next steps ? how to score / perform diagnostics?

My question is as follows. I am referencing the following paper by David Cassell - wherein David talks about bootstrapping techniques in SAS using PROC SURVEYSELECT (many thanks to David - truly a ...