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

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What does $P$ stand for in a logit regression

I was reading this paper: http://landdevelopability.org/ChiWebPublications/Chi%20and%20Voss%202005_JRAP_Migration%20Decision%20Making.pdf and on page 7, they say that ...
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14 views

Logistic regression problem, firth logit does not work [duplicate]

I have the following preconditions: Software: SPSS v21, possibly R Sample size: 5655 (will get around 8x times larger in 2 weeks, but I already want to find out statistical pitfalls) DV: binary; 1 = ...
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10 views

Softmax Regression GD Update Derivation

I'm implementing softmax regression and am deriving the max-log-likelihood update for gradient descent by hand first. Coming from the Stanford UFLDL site, they show the gradient of the cost function ...
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26 views

How to do one-vs-one classification for logistic regression?

I have a dataset with 4 clases and I want to apply logistic regression with one-vs-one classification. So, first I train for each pair of classes a logistic regression classifier (i.e. calculate the ...
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70 views

How do I interpret and report the output of a logistic regression in R on data that are all binary (0,1)?

My minimum adequate model is shown below. My independent variables (e.g. One, Five) represent habitat categories for which species have either been designated to (i.e. the assessment found that they ...
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51 views

What property of logistic regression is useful for modeling user behavior? [on hold]

I want to know that what property or attributes of logistic regression make it to useful for modeling user behavior.
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Multiple response question with copied observations

For an analysis with the answers of a multiple response question submitted by respondents as the dependent variable, I was thinking of using duplicate observations. So e.g. if a respondent submitted ...
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9 views

SPSS Binary logistic regression incoherent result after excluding outliers [duplicate]

I'm using binary logistic regression for my master thesis and after running the regression for a a few specific variables I get the following resutl: Then I created a filter to run the regression ...
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10 views

Alternative to multinomial logistic regression for model with multiple related outcomes

I have been using multinomial logistic regression to answer a question analagous to: "which candidate will a person vote for, given particular demographic characteristics?". I am now looking to move ...
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26 views

Want to predict the top 100 students out of 1000 students - what model to use? [on hold]

Currently, I'm looking at a model that uses logistic regression and then ranks the results based on probabilities from the logistic regression. Is there a better methodology?
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16 views

Bayesian random effects meta-analysis on the risk ratio with r2jags

Following the work of Warn 2002 I am trying to set up the model for a Bayesian meta-analysis on the risk ratio and the odds ratio. I am using R together with R2jags to fit a simple RE MA model. ...
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Could someone outline the pros and cons of the mlogit{mlogit} versus the multinom{nnet} functions for modelling multinomial logistic regressions in R?

I am trying to make an informed decision about which of the mlogit and multinom functions, in the mlogt and nnet packages respectively, I should use for a multinomial logistic regression model. I do ...
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78 views

logistic regression in r with many predictors

I have been running logistic regression in R, and have been having an issue where as I include more predictors the z-scores and respective p-values approach 0 and 1 respectively. For example if have ...
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52 views

Why do we need to log transform independent variable in logistic regression

I am curious that since we don't have normality assumption of the independent variable in logistic regression, why do I see people using log transformation for independent variables in logistic ...
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1answer
76 views

Handling categorical predictors in logistic regression, linear regression and SVM

I want to know how I can handle categorical variables in logistic regression, linear regression and SVM. The categorical variable has four categories 1,2,3 and 4. However, it doesn't mean 4 is like 4 ...
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2answers
53 views

Logistic/Probit Regression if the response variable is not a probability

I am working on a model which involves predicting a ratio between 0 and 1 using a number of variables. The ratio in question cannot be thought of as a probability. I am wondering if a logistic ...
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24 views

Normalize estimated probabilities from two logistic regression models

I have built two logistic regression models predicting the probability of purchase of two products. (Product A and Product B) For every customers, I want to choose the product that has the higher ...
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40 views

If removing another variable makes another variable insignificant should it be removed?

This is a logistic regression used for the goal of prediction. Originally a model had ten variables. Two variables were removed using a clustering procedure. Then one variable was removed due to ...
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1answer
22 views

Separated regression analysis vs control the covariate

I am conducting a data analysis on an Epidemiology cross-sectional study. Suppose the outcome variable is an binary variable for health status (1=health, 0= unhealth). And the exposue is infection at ...
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Logistic regression or T test?

A group of persons answers one question. The answer can be "yes" or "no". The researcher wants to know whether age is associated with the type of answer. The association was assessed by doing a ...
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Variance of the logit of the mean of the predicted values of a logistic model

I have a logistic model, and I am trying to calculate the standard error of the logit of the mean of the predicted values. Here an example with R: ...
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How to get average of squared residuals vs. Predicted frequency graph with variance function line in R for GLM model [on hold]

How to get average of squared residuals vs. Predicted frequency graph with variance function line in R for GLM model. fitted my data into GLM model using glm.nb function. Was looking to get the ...
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39 views

I am running a logistic regression model and get very low predicted probabilities

I am running a logistic model for catastrophic health expenditure (CHE) in Argentina. The sample size is 22500. I followed Xu et al. methodology to define CHE and adjusted for 8 socioeconomic ...
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24 views

Data centering for logistic regression estimation

I read in some articles that we can use data centering as per-process of logistic regression. Centering is $X-Mean(X)$ for every value of $X$ input? What is interpretation of coefficients in this ...
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Binomial Logistic regression to Compare results of multiple methods to a known value

I have a "known value" (recorded by a field observer) which I want to compare several methods of data collection with to test for significant differences between methods. Methods: 2 points along a ...
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I want to generate outliers in binary logistic model

I want to generate outliers in binary logistic model What I want is: to select 3 elements of 10 elements that are randomly generated, and: if the selected value is 1 convert it to zero & if ...
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What exactly are some fundamental differences between probit model and logistic regression [duplicate]

It seems that both these refer to cases where the regressed (dependent) variable can only take certain values, as opposed to a linear regression. So what is the difference between probit and logistic ...
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Can we use categorical independent variable in discriminant analysis?

In discriminant analysis, the dependent variable is categorical, but can I use a categorical variable (e.g residential status: rural, urban) along with some other continuous variable as independent ...
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Advantages of the softmax function in feedforward multi-class neural nets over logistic activation and one vs all approach

I am wondering if there is a benefit to the softmax function over an one-vs-all sigmoid activation function approach in feedforward neural networks for multi-class classification -- except for the ...
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107 views

How should strongly correlated covariates for logistic regression be treated?

I have to build a multiple logistic regression model with two strongly correlated covariates (predictor variables). How should they be treated? Am I to exclude one of them from the regression? There ...
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Why is Logistic Regression called a Machine Learning algorithm?

If I understood correctly, in a Machine Learning algorithm, the model has to learn from its experience, i.e when the model gives the wrong prediction for the new cases, it must adapt to the new ...
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32 views

Industry (and year) fixed effects with non-panel data?

For my thesis i'm trying to estimate the effect of several variables on the recommendation level of analysts artound Mergers and Acquisitions. My dependend variable is 'recommendation' which can be ...
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48 views

What is the correct way to determine which features most contributed to the prediction of a given input vector?

I am using logistic regression for binary classification. I have a big data set (happens to be highly unbalanced: 19 : 1). So I use scikit-learn's ...
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Stata automatically tests collinearity for logistic regression?

I'm using Stata for logistic regression. This software automatically checks for collinearity and remove (drop) some variables as we can see below: ...
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Can I Interpret the impact of variables like positive or negative on the model by Random Forest, as I can do by Logistic Regression

I have created a model for prediction of candidates presence or not . I have used Logistic Regression and Random Forest . By Logistic Regression, I got coefficients associated with 100 features and I ...
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Logistic Regression with Dummy dependent and Ordinal independent variables

I am working on model that involves a dummy dependent variable with probabilty of occurance of event (0,1) and ordinal independent variables (with the value increasing with the number of times another ...
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How to Test Collinearity Between GROUPS of Predictors?

I had a model (made with VW, log loss) based on a set of base (p=1000's) predictors. It did not predict well. I added set A of predictors (p=~5 predictors), and it improved immensely. I added set B ...
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Logistic regression: Absolute values for P

I am stuck with a problem (actually two problems). I have a dataset of about 150 cases and 30 or so dichotonous (yes/no) parameters. I selected 6 parameters (after literature study and crosstabs) for ...
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One of the independent variables in a logistic regression is the ratio of other two inputs. Is this okay?

Suppose that I have a logistic regression with continuous independent variables $a$,$b$, and $c$. In my logistic regression, $c = \frac{a}{b}$. Is it all right to include variable $c$ in the ...
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Why can't I reconstruct parameters of a synthetic data set?

The following Python function creates synthetic binary labeled data that is supposed to perfectly follow the logistic regression model: ...
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Logistic regression coefficients and exp(coefficients) meaning and relationship

Suppose that we have this output for Logistic Regression: Coef EXP(Coef) X1 2.45 11.67 X2 0.40 1.449 X3 -4.1401 0.0159 Here ...
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Dose-response curve in logistic regression: transformation of dose variable and what to do about non-monotonic relationships?

I'm attempting to develop a logistic regression model to predict a binary outcome (namely death) for an RCT cohort. One of my predictor variables is the empirical dose of the given treatment, which is ...
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Meaning of MATLAB logistic regression coeficients [duplicate]

Suppose that I'm using this function to implement logistic regression in MATLAB R2015a: ...
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39 views

Interpreting results output from Firth logistic regression in R

I am using Firth logistic regression to analyze data with a rare event. In my model I have 4 continuous variables and 1 dichotomous variable. This is my code: ...
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34 views

Observed / expected vs odds ratio

I am trying to understand the difference between the O/E ratio vs the odds ratio. I think the odds ratio can handle small samples better than the O/E ratio. I am wondering if anybody has more ...
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Manually scoring logistic regression model in SPSS? [closed]

First off, I'd like to apologize for my cluelessness, but I've come across a problem that I honestly have no clue how to circumvent. My programming skills are extremely limited, and my company uses ...
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Model change after switching reference level in R logistic regression model with interaction

I'm fairly new here so my apologies if I'm asking something obvious. My problem is the following: I have a dataset in which I want to examine the interaction of a risk-factor with an intervention to ...
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Correcting for heteroscedasticity in logistic regression

I am using a large health dataset as a part of a research project (N = ~18 000). My colleagues and I are investigating whether smoking predicts the presence or absence of a mental illness. We are ...
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Should I include a ratio as predictor if I don't have the complete numerator?

There is a ratio 'dti' calculated using: the borrower’s 'total monthly debt payments on the total debt obligations', excluding 'mortgage and the requested loan', divided by the 'borrower’s ...
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Get Logistic regressions odds from equation result

By using logistic regression in SPSS I get an equation that looks like this: 0.1535 * X1+ -0.0002973 * X2 + -114.9 * X3 + -114.0 * X4 How do I transform the result of the equation to the odds ...