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

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R: Find the second derivative of a log likelihood function

I'm interested in finding the values of the second derivatives of the log-likelihood function for logistic regression with respect to all of my m predictor variables. Essentially I want to make a ...
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16 views

Simple Logistic Regression Questions

Problem I want to estimate the probability of a user choosing a car or transit as a transport mode. Following is the output of the simple logistic regression: I want to estimate the probability of ...
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1answer
27 views

Logistic Regression - Adding interactions makes Independent variable statistically insignificant

My name is Abhi & I am trying to better understand logistic regression by solving a few practice problem. I am using R and RStudio as the development environment Problem Statement Given the age, ...
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40 views

Ordinal logistic regression with likert scales

I'm currently have a bit of difficulty determining how to analyze this data via logistic regression analysis. ...
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32 views

Regression analysis?

I have scored pig carcasses for injuries at 2 points; immediately after slaughter (slaughter stage 1 [SS1]) and again after the carcases have been washed, scalded and dehaired (slaughter stage 2 ...
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Using proper scoring rule to determine class membership from logistic regression

I am using logistic regression to predict likelihood of an event occurring. Ultimately, these probabilities are put into a production environment, where we focus as much as possible on hitting our ...
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11 views

Predicting whether a potential sale will be won or lost

I am currently working on a project using a sales system and trying to come up with a way to use the current pipeline of potential sales to predict the amount of product that will be sold in the ...
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12 views

CHAID Analysis of Logistic Regression Model

I created a logistic regression model to predict, for a people off work due to illness, the probability each of them would go back to work in the upcoming month. (Based on a number of factors like ...
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1answer
32 views

Logistic models as variables in a logistic model

I have a binomial logistic model I'm working on, with roughly 6 significant variables. I'd like one of those variables to be how likely something else is to happen. Lets say that I'm primarily ...
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22 views

Using empirical null distribution to adjust odds ratios

I am doing a case-control study analysis with 2500 cases and 2500 controls. I am interested in finding out if the cases have higher odds of having a particular disease than the controls, so I am ...
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Iinterpretation of coefficients of multiple comparisons in a logistic regression model

I'm confused about how to interpret coefficients in relation to the reference categories. I have two variables, A and M. A is a 3-level variable and M is a 4-level variable. The reference category for ...
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25 views

Research on debt recovery

My final year project is on debt recovery data for a debt collection firm. Data such as original/current balances,payments made,DOB, number of contacts made,whether or not a debtor has made insuarance ...
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Use of Deviance Residuals for Leave-One-Out Cross Validation

I am a newbie to stats and having some difficulties understanding how to use deviance residuals for leave-one-out cross validation for a logistic regression model. The problem that I am trying to ...
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How can I (Should I?) use logistic regression/the logit function to predict outcome of a tennis match in a simple simulator?

I am trying to create a tennis simulator. Specifically I am trying to make a 'random' simulator so that I can see how many times streaks of wins or losses occur, and then compare this to historical ...
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17 views

Conditional logistic regression model does not converge but logistic regression model does

I am running an analysis where I have 2500 cases and 2500 controls. The cases have disease A, and the controls do not. I am trying to see if having disease A increases the odds of various diseases. ...
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11 views

Using an odds ratio when data is sparse

Suppose I have around 20 exposures that potentially affect an outcome and I want to see which exposures have bigger impacts on the outcome. So I want to calculate each exposures' odds ratios by ...
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132 views
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How to evaluate fit of a logistic regression

I have a set of data points, which exhibit a solid linear correlation $r\approx 0.9$. I am basically plotting population in certain areas against the number of occurrences of a certain phenomenon (so ...
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1answer
39 views

VIsualizing the effect that only one predictor has on the outcome (R)

So I have performed a logistic regression on a data set with multiple predictors. I want to graphically represent the relationship between the outcome and only one of the predictors. What would be the ...
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14 views

How to calculate marginal effects for categorical covariates using mlogit in R

I am trying to use the mlogit package in R and have been following the vignette trying to figure out how to get the marginal effects for my data. The example ...
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binary logistic regression in spss version 22

I'm trying to run a Binary Logistic Regression. I have multiple IVs and 1 Binary DV. Can anyone show me how to do this with SPSS v.22? (or which one to select from this list) thank you
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Logistic regression with multi-class features in R

I'm working with a data set like the following: X = ...
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1answer
49 views

How to solve for the parameters in a logistic function? [duplicate]

I want to find the parameters of a logistic function. I read the guide here. It has a very clear explanation, but it did not have the final solution that I need. Now, we will consider a basis ...
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56 views

Great model prediction, but no significant variables

I am performing a binary logistic regression. I have developed a simple model which I am testing using the SPSS application. This first determines the predictive ability of a baseline model without ...
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1answer
12 views

Help with determining model for cross-sectional data where all variables are dummies

I am currently working on my dissertation project where my data are essentially all dummies. From my dependent to my independent variables, everything is a dummy variable (0,1), at least for the ...
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102 views

How to cope with missing data in logistic regression?

I'm investigating optimal bidding in auctions, and am using logistic regression to predict the probability of winning an auction given a set of explanatory variables (e.g. the price I bid, number of ...
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21 views

Constant RMSE margin between training and teseting set

I have a large number of independent datasets of varying size but same feature meaning. Features and outcome are both binary. I am trying to fit logistic regression to the data. I estimate ...
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20 views

Preconditioning the intercept for Logistic Regression

I am working on implementing a Logistic Regression model, using the newton-cg and lbfgs optimsers provided by scipy as the backend. I find the problems in which I fit the intercept, to be 50% slower ...
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Getting the rule from cross validation

I've got a question. Let's say I have a medical data representing 2 classes of patients (healthy and unhealthy) and some number of predictors which characterize these patients. Choosing different ...
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How to use regression analysis to set an optimal price

I am working on a side project with very small dataset where i am trying to figure out the optimal price i should set for a transaction fee (something like payPal). Currently i am using an arbitrary ...
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“Error in drop(y %*% rep(1, nc))” error for cv.glmnet in glmnet R package [migrated]

I have a function to return the auc value for a cv.glmnet model and it often, although not the majority of the time, returns the following error when executing the cv.glmnet function: Error in ...
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Weighing a variable based on distance to a known date

In a bankruptcy model, you want to assign a higher weight to a variable as a big event date approaches (such as a company's quarterly earnings announcement date) and you reverse this weighing as you ...
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41 views

Predicted probabilities from simulated betas and hypothetical data after conditional logit?

I'm working with conditional a conditional logit model to avoid bias that comes with FE logit models, when it comes to generating some hypothetical substantive effects, however, I run into trouble. ...
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Clarification on interpreting Wald's test and Likelihood ratio tests

I am running multinomial logistic regression analysis on my data. The response variable is the number of calves produced each year (0,1, or 2). I am trying to evaluate the influence of the X ...
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18 views

Softmax regression or $K$ binary logistic regression

For a multi-class classification problem, we can use $K$ binary logistic classifiers, or one softmax regression classifier, so how to make the choice between the two? IMHO, the $K$ binary logistic ...
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7 views

Use a different loss function for cross validation in liblinear

I am trying to learn a L2 regularized Logistic regression model in liblinear. I need a way to specify the C parameter which I do by cross validation. However, the loss/accuracy measure in cross ...
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Specifying the validation dataset for liblinear

I am trying to use the liblinear logistic regression model with L2 regularization. I don't want the training data to be splitted for the cross validation. I want to specify my own validation set for ...
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Cutoff Value in Logistic Regression [duplicate]

In R, the logistic regression output gives you predicted probabilities. Is there away of determining the threshold value $\alpha$, such that any $p > \alpha$ is classified as a $1$ and and $p \leq ...
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Is cross validation needed?

Suppose we have training data set and a test data set. The outcome variable is binary. Is it usually necessary to split the training data set so that there is a cross validation data set? Or can you ...
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What is the “Logistic Regression”? I cannot have a unified concept

I'm interested in logistic regression for modeling a classification problem. I tried to study logistic regression with two books, "Discrete Choice Methods with Simulation" (Train) and "Applied ...
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Logistic regression and error terms

In logistic regression, if we considered residuals, could they only take on the values $0$ or $1$? The data points themselves take on only $1$ or $0$. The logistic curve can take on any value between ...
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9 views

Liblinear logisitic regression with L2 regularization for classification

I am trying to use the liblinear library for logistic regression with L2 regularization. However, I am finding some issues with it. For eg when choosing the cost parameter, I chose the C parameter to ...
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10 views

How to improve results when using sampling in skewed binary classification?

I am using a data set with 18 features with True/False output (Related to mobile ad targeting). True values occurs only 0.4 % of the time. So, I have used sampling to keep the ratio of True and False ...
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21 views

What methods can be used to transform data?

I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired ...
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52 views

Binary or Multinomial Logistic Regression in SPSS: Interpretation and Reference Categories

I am trying to analyze my data using Multinomial Logistic Regression whereby my dependent variable is a clinical outcome (sick vs healthy) and 1 independent variables (Factors) are in several ...
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21 views

glmnetcr - error in R - regularized continuation ratio logistic regression

I'm trying to run regularized ordinal logistic regression with glmnet.cr() on 28 predictors, a mix of continuous and categorical. Here are the relevant lines of ...
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1answer
21 views

How to deal with a skewed class in binary classification having many features?

I am doing data analysis in the mobile ad targeting domain. I have around 18 features and for a combination of these features, the result is either True or False (1/0) depending on whether the ...
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2answers
52 views

High model fit but no significant impact of any of the predictors

I am applying binary logistic regression as my dependent variable is a dichotomous variable with 740 sample size. I have used enter method to input my variables and have designed two blocks. In block ...
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93 views

modelling data that contains only ones and zeros

I'm new to modelling this type of data, and I got punished for asking this on stackoverflow... I have a dataset where the predictive variables contain only ones and zeros, and the response variable ...
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26 views

Interaction in a repeated measures logistic regression

I want to test for the effect of interaction between two variables using a repeated measures logistic regression but I don't know how. I am using Stata and the following lines: ...
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multinomial logistic regression with alternative specific variables

I am working on a multinomial logistic regression problem which involves features from the dependent variable. It might be better to describe the problem by using the example in mlogit mlogit manual ...