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

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Functional logistic regression

I need some references related to functional logistic regression ? I am trying to solve a classification problem with many variables
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Functional data analysis [on hold]

I am new to functional data analysis. I have a data set that takes about 50 different features for a unit at different time instances. These units might fail . My initial goal is to find out which ...
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30 views

What is the prediction equation for penalized logistic regression?

I have used penalized logistic regression (R package logistf) to predict probability of a rare event. 0.12% is the event rate i.e., only 35 occurrence of event in ...
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how to combine coefficients of a logistic regression model with existing prior knowledge about covariates?

I am working on developing statistical models for fault-localization. on the one hand, i construct a logistic regression model with these considerations: 1-my dependent(response) variable is program ...
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when some of your coefficients in multivariate logistic regression model is negative

when some of your coefficients in multivariate logistic regression model is negative while i know these variable have positive sign in univariate model, What should I do؟
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How to deal with the “sure probabilty” (p=1) in logistic regression

The model of logistic regression is that: log(p/(1-p) = ...... The most interesting case (for me) is the case that we have p=1 and p=0. But in this case, the ...
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For a longitudinal observational study, what is the best method of comparing cost before and after intervention?

I have a sample of 200 patients, with data for 2 years. During year 1 they are on treatment A and accumulate overall healthcare costs of X. During year 2 they are on treatment B and accumulate ...
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Fitting logistic growth model. lme4 or nlme?

This is the first time I am using a nonlinear model. I am using a logistic growth model for analyzing tree growth pattern given by M. Bates and Pinheiro, 2000:. $$ y = ...
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Fitting a Logistic Regression Without an Intercept

Based on the answer here: Significance of categorical predictor in logistic regression I tried adding a "-1" to my model to fit it without an intercept, and see the correlations directly. It looks ...
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Monthly indicator variables, decreasing in weight

I have a logistic regression with a response variable that is a proportion and predictors that are dummy variables for the month of the year, along with a few key exogenous variables. My ...
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Multinomial Logistic Question

Using PROC Logistic with the glogit link, I am attempting to classify records according to 1 of 3 responses (0, 1-2, 3+). After cleaning and running multiple models, I landed on what I thought was a ...
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33 views

Relative Importance of categorical variables

My question concerns management issues more than statistical theory. I am searching for a way to measure the relative importance/influence of categorical variables in a logistic regression model with ...
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Censored logistic regression

I have the following problem: We have data with a 0-1 outcome which can occur precisely once. It can occur at any time within a certain time period (say 3 years). For this data set, for some ...
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Machine Learning Methods for Binary Classification

I was hoping to get a nice list of alternatives to logistic regression and decision trees for binary classification ("Yes vs. No" or "Cured vs. Not cured"). I am more interested in identifying the ...
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Backward selection (with fastbw) in penalized logistic regression

I have a dataset with more than 20 predictors and a single binary response variable. With only $n=181$ observations (64 deaths, 117 survivors), I decided to apply penalized logistic regression to ...
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29 views

Largest or smallest confidence interval at $\pi_{i}=0.5$ in logistic regression

A binomial GLM can be written as: $Y_{i}\thicksim B(1,\pi_{i})$ $\mathrm{E}(Y_{i})=\pi_{i}$ and $\mathrm{var}(Y_{i})=\pi_{i}\times(1-\pi_{i})$ ...
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logistic regression R and Stata [on hold]

I mostly use Stata for my regression analysis. I want to conduct a logistic regression on a proportion/number of success. Because I receive errors in Stata I did not expect nor understand (if there ...
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JAGS equivalent to R's I() (Inhibit Interpretation of Objects) function?

I'm wondering if anyone has come across the JAGS/BUGS equivalent to R's I() function. I am interested in using this in a polynomial logistic regression, i.e.: mod1 <- glm(Employment ~ Density + ...
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Translating a glmer(binomial) with more than two correlated random effects to BUGS/JAGS [migrated]

I am trying to translate a hierarchical logistic model fitted with glmer(family=binomial) to BUGS/JAGS. The model, however, has more than two correlated random effects. The call in glmer is: ...
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multinomial logistic regression, weighted logistic regression?

i have a binary predictor with many response variables. the binary predictor was originally continuous but was converted to binary.... if the response was >1000 then 1, else 0. I would like to have a ...
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Insignificant coefficients in Logistic Regression after LASSO variable selection

I am trying to use the LASSO technique to identify which variables to include in my model. I used cross validation to identify the value of lambda which minimizes the CV error. For this minimal value ...
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Can we perform logistic regression on cross section data?

Can we perform logistic regression on cross section data? My friend says that logistic regression only works for panel data.
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Variable Importance and Information Value for continuous variables

I am new to Logistic Regression. I have data which contains categorical and continuous variable. I want to calculate Information Value for all variables. For categorical variables it is easy to ...
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How to rescale “linear predictor” in drawing nomogram with “rms” package in R? [migrated]

I am trying to draw a nomogram from a logistic regression in R by using the rms package, but currently I have a problem: indeed, I can get the nomogram, but the "linear predictor" axis ranges from ...
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Categorical (5 categories) vs. continuous interaction logistic regression

Apologies if this is comes across as basic - I'm learning logistic regression using SAS while completing md/mph and don't have a lot of stats background. My response variable is dichotomous (1 = EMS ...
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How to model Zero-one Inflated Proportion Data?

I have a problem with my dependent variable, which is a proportion including ones and zeros. I am analyzing the use of a fungicide in apple farming. I have a sample of a survey of 1300 farmers and ...
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Can I combine 10 variables into one variable before performing logistic regression on 18 total variables?

Univariate analysis of 18 variables possibly associated with spine infection--can all the historical variables be combined into one variable, then logistic regression be performed?
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Best way to account for time lags in logistic regression (GLM or GLMM)

I am trying to determine the best, most conservative way to account of time lags in a logistic regression type analysis (a generalized linear model with or without mixed effects). I am working with ...
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Comparing logistic regression models with different predictors [duplicate]

What measure do I use to compare two logistic regression models with different predictors but the same response? y ~ x y ~ z I've used lrtest and anova before ...
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High asymmetric binary variables [closed]

I have a set of binary dependent variables where most values are concentrated in one category. Which methods are adequate to analyze such data and which restrictions or difficulties are usual in this ...
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1answer
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What statistical test would be appropiate for determining the preferences of lottery players?

I'm trying to conduct a simple study: to find out if there is a significant relationship between the time perspective of a person and his propensity to buy lottery. So to keep it simple, my ...
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Statistical Modeling with the combination of two models

I'm having a modeling problem now. Assume we have discrete random variable Y and continuous random variables X and Z. First, we assume a logistic regression between Y and Z.(Assumption One) Also, we ...
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Getting LS means of the response from logistic regression in SAS

So, I know this is more of a programming question than a stats question, but I thought I might try here anyway. I have a logistic regression model with a combination of categorical and continuous ...
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Logistic Regression Question (Adjustments)

I had a question revolving around logistic regression. I'm looking at a data-set for my work that yields somebody as approved or denied (think credit rating applying for a mortgage, similar but not ...
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Probability function for time series of logistic regressions

I'm testing out model described in this paper for time series of consumer loans Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Basically authors use ...
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Standardization for regularized, sparse hashed logistic regression

As the question states, I'm fitting large, sparse logistic regressions (with hashed interactions, a la vowpal wabbit) for a machine learning system. The features are on different scales, and I'm a ...
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Estimating AR process for Logistic Regression

I'm fitting a time-series model with independent $X$ variables coded as months of the year (so there are 12 of them) and the dependent $y$ variable is some proportion, bounded between 0 and 1. As a ...
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K-fold cross validation for a glmer model with nested data

I'm working on a data set that contains a hierarchical data structure (i.e., GPS locations nested within individual animals). I'm using a generalized linear mixed effects modeling procedure (lme4 ...
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“Multi-Task” Logistic regression with time series data [closed]

I'm trying to create model for consumer loan defaults that incorporates individuals payment behavior as time series. Typically this kind of problem is modeled using Cox/Allen model. Then, the other ...
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Odds vs probability in logistic regression

I am going through Trevor Hastie's Classification Techniques. Its says Odds are traditionally used instead of probabilities in horse-racing. I still don't understand how they relate more ...
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CTR Enhancement Model

I am looking to build a model to enhance CTR. Below is the business description. We have a coupon based website. For each retailer, we have a retailer page. Each retailer page will have up-to 100 ...
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Comparing logistic regression models with paired data, population-averaged estimates and robust standard errors

I would like to ask if any of you have suggestions for comparing logistic regression models with paired data (3 observations/ID code), population-averaged estimates and robust standard errors (I am ...
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ROC curve cut off and weights

I have a dependent variable distinguishing between patients that should go to treatment A or treatment B. I want to develop a questionnaire containing binary variables that should decide if the ...
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Need advice on statistical analysis

I'm designing a study with 3 different groups that are to receive 3 different interventions for a set period of time (n = 100, month long interventions). Would this be called a randomized crossover ...
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How can using Logistic Regression without regularization be better?

I'm using this Java machine learning library: https://sites.google.com/site/qianmingjie/home/toolkits/laml From the library I'm using Logistic Regression: ...
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Can we express logistic loss minimization as a maximum likelihood problem?

I have a simple question about the equivalence of loss minimization and likelihood maximization for logistic regression. Say are given some data $(x_i,y_i) \text{ for } ~i = 1,\ldots,N$ where $x_i ...
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glm using high dimensional data

glm using high dimensional data Hello all. I am new to all of this. I am trying to use GLM in R to do a logit regression. I have a high dimensional data set (each datapoint/vector has about 1000 ...
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ROC, variables, unbalanced data, where to start?

Can someone explain to me how you would know which variables to remove? And how do you know if something is accurate or not? Because when I plot an ROC curve the specificity/sensitivity curve, it ...
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Do logistic population growth models relate to binary logistic regressions?

I ask this because all resources regarding logistic regression in R involve binary outcomes, so they try to model questions like when will increase in temp cause a switch to fail (0, 1)—involving ...
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Is conditional logistic regression appropriate for comparing costs between treatments?

I am looking at previous reports done by a previous staff member comparing 2 treatments - lets call them treatment A and B for epilepsy and the total costs for a matched population. For instance the ...