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

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Practical exercise to solve SVM and logistic regression

I have a simple task regarding SVM and logistic regression, even though I know the theory behind SVM and logistic regression I still have a problem to solve them. Exercise. We want to learn a ...
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12 views

choosing between non-linear/logistic regression models

We are fitting regression models to dose-reponse data obtained from drug assays. Where a drug has an effect on cell viability, there is often a sigmoidal relationship between the proportion of viable ...
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Applying Mean to log2 Values, the right thing to do?

I have set of values (n=10), over two conditions. Condition_X - 5 Measurements Condition_Y - 5 Measurements These values are in log2 space. if I take the mean of each condition and subtract to ...
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25 views

Dummy variables-Regression

I am running a logistics regression where I have many covariates that I am controlling for. Should I be using dummy variables for those covariates? I am wondering since I am not really interested in ...
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17 views

How can I run ordered logistic regression on a large sparse matrix in R

I have a sparse matrix X, 970283x9511, with 970283 documents and 9511 features. I have a vector y of length 970283 corresponding to a rating 1-5. I know of the glmnet package, which has binomial and ...
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21 views

Nagelkerke $R^2$ interpretation

I used logistic regression and found that my model fits well: ...
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Expectation (covariance) between $X_j$ and $Y$ in logistic regression

Is there some way to explore the relationship of $E(X_jY)$ in a logistic regression? That is, $Y$ is a binary variable observed together with $p$-covariates $X$ (wlog centered). I am interested in ...
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21 views

Dummy and factor variables in logistic regressions

What are the differences in interpretations of betas for factor and dummy variables in a logistic regressions? In the same vein, what are the differences in the betas for interactions involving ...
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176 views

Why logistic regression cannot be solved by OLS

I know I mess up different things. However I want to get better understanding what is motivation behind logistic regression. $p(y_i=1|x_i)=(1+e^{-x_iw})^{-1}$ and according to OLS ...
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30 views

Logistic regression iterative process [duplicate]

I don't understand something very fundamental about Logistic Regression. For Linear Regression and for Naive Bayes there is a closed form for calculating parameters. For some reason, logit ...
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5 views

C-statistic for comparison of incidence rate poisson to cumulative incidence logistic models

I'm looking to compare the capacity of two models for predicting who does, and who does not, develop a certain infection during a hospital stay. Both models are based on the exact same patients, but ...
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17 views

Using logistic regression to predict American football outcomes

I am interested in calculating the probability that Team A defeats Team B in the NFL using past data. I have a pool of predictor variables such as passing yards, rushing TDs, etc, and I have the ...
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17 views

Estimating conditional effect of logistic regression

I'd like to examine the conditional effect of a new variable, say $\mathbf{Z}$, on a logistic regression previously fit to my response vector $\mathbf{Y}$ using the predictor(s) $\mathbf{X}$. Say ...
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39 views

Logistic regression before decision tree model

I am trying to run several decision tree models (CHAID, C&RT, QUEST), but I have learned that several researchers have applied logistic regression model first in order to select risk factors. So, ...
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Can the rectangular function be used to filter out magnitudes in logistic regression to add more flexibility?

I would like to use logistic regression rather than an artificial neural network to be able to more easily interpret the results. I would like though to be able remove the linearity by introducing a ...
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1answer
33 views

How to choose between poisson regression and zero inflated models

I have data on children visiting the A&E department. I want to see which variables are associated with the numbers of visits. For instance, if genetic factors are associated with more visits to ...
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24 views

Loss function from Posterior log likelihood

I am trying to implement Bayesian Logistic Regression and was looking at this paper http://www.stat.columbia.edu/~madigan/PAPERS/techno.pdf. The authors in this paper had assumed the $\beta$s to have ...
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1answer
78 views

How is the sigma^2 value (or MSE) for the link function computed in logistic regression in R?

For example, if you have a logistic regression on certain dataset: fit <- glm(y ~ x, data = test, family = "binomial") If you do ...
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8 views

How many number of items should i include in my test?

I have designed two post test ,a recognition test and a production test.I have inserted 18 items in each test. As I sifted through articles , I 've read that we should include at least 30 items to get ...
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45 views

Testing the statistical significance of regression coefficients in a logistic regression

Are only the p-values relevant when testing the regression coefficients of a logistic regression? Does the z-value of a coefficient give any further information about the significance of the ...
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Logistic Regression with significant variables and bad predictions

Can someone explain to me how my stepwise logistic regression model has variables with very low p-values but does not predict very well?
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25 views

How to do pooled logistic regression in Stata? [closed]

How do you go about doing pooled logistic regression in Stata? Can't find any info online!
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6 views

Effect of 4 options on one column of variables

Column 1 Column 2 1 56 3 18 3 27 2 31 4 22 1 32 2 65 4 39 How do I find out how much a 1,2,3 or 4 ...
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108 views

When do coefficients estimated by logistic and logit-linear regression differ?

When modelling continuous proportions (e.g. proportional vegetation cover at survey quadrats, or proportion of time engaged in an activity), logistic regression is considered inappropriate (e.g. ...
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57 views

Cross validation for logistic regression in R [closed]

I am doing a K-fold cross validation for a logistic regression. I first used the PCA to reduce the dimensionality. Then I used those principal components to build the regression model. My problem is ...
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Should outliers be remove first before identifying influential observations?

I have constructed a logistic regression model. I used half-normal probability plot and detected two outliers, which I removed. Then I want to identify influential observation, in order to improve the ...
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1answer
23 views

Calculating Standard Deviation when given sample size, mean difference & p value

I am trying to pool data in my meta-analysis and i need MEAN & SD. However the study has reported sample size (27), before (11.8) & after mean (11.9), and p value (0.540). I need the SD. ...
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34 views

compare muliptle probabilities

I have built three decision tree model to predict the response for three offers (One model per offer). I want to find the best offer for each customer based on the predicted probabilities. All offers ...
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1answer
40 views

Why does logistic regression not work in p > n setting?

I have started working on a wide (p > n) genetic data set. It is recommended to me to use a regularization technique (such as LASSO or Ridge regression) in order to reduce the number of genes to be ...
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Regression with both binary independent and dependent variables? [duplicate]

Is it okay to do a logistic regression where both independent and dependent variables are binary? They are both 0 or 1. Does it matter how many independent variables? If it is okay to do, how can I do ...
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51 views

main effect in logistic regression with the presence of interaction

I just have a question about how to get the main effect in the presence of interaction effect. I have two cohort: say cohort A and cohort B . For cohort A, I have this code as 1. Zero for cohort B. ...
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How should I handle measurement error in logistic/probit regression and what are its effects?

I am concerned with the problem where dependent discrete variable $R$ is to be modelled by continuous predictor $X^*$, which is subject to measurement error $u$ of the form $$X^*=X+u$$ ($X$ being the ...
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2answers
74 views

How to determine if the occurrence of two events is temporally connected?

I'm working on a dataset where I have dates as the main unit of analysis. I'm trying to see if two events are related; that is, if the first event happens, will the second event happen within a month ...
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17 views

Ordinal Logistic Regression using Fractional Polynomials in SAS

I am looking for Case Studies / Examples where Ordinal Logistic Regression Models ( using Fractional Polynomials for Continuous Predictor Variables ) are implemented in SAS ( SAS Macros )
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Values distribution issue in logistic regression [duplicate]

I have a problem with logistic regression. I have the following variables: Var1: 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0 NA 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 ...
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Discriminant, logistic approach to attrition analytics

I have a few variables for each employee like tenure, age, marital status, certification, working away from home, #of OThours in an year, last promotion, performance rating, etc... I have these ...
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logistic regression prediction: changing interpretation with changing prior

The data include 3 equally sized subsets A, B and C, belonging to two classes: A belongs to class 1. B and C belong to class 2. The prior probabilities of an observation coming from class 1 ...
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Gelman & Hill ARM - Question 5.11

Gelman Hill textbook has a question using election / voting data (http://goo.gl/ff8ryn); After fitting a logistic regression model for year 1964 using inncome, race, gender as a covariate, ...
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53 views

Larger p-values but less misclassification error in Logistic Regression

I was doing logistic regression in R on 'Smarket' data set available in the ISLR library. Since correlation between variables were less, I used all variables in my model and I was getting the ...
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1answer
39 views

Interpretation of Odds Ratio of Zero

In logistic regression, does an odds ratio of zero make sense, and if so, what's the interpretation. I've only been able to locate one reference that specifically refers to OR of 0: Making Sense of ...
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172 views

Visualizing variability from graph

It is written in the book Applied Logistic Regression, Second Edition. By David W. Hosmer and Stanley Lemeshow , p.2 that a problem with the above graph is that the ...
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Computing and plotting decision boundary in R

I need to plot a decision boundary in R,(logistic regression) with 2 variables. I tried to use glm but I get a straight line and also the decision boundary does not look correct. A small sample of the ...
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46 views

Does the position at which maximum distance occurs in a KS test make a difference?

From my understanding of the KS test, fromt the CDF of two datasets, it measures the distance between the two distributions at various points and and compares the 'maximum distance' to a predefined ...
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41 views

plot logistic regression line over heat plot

My data is binary with two linear independent variables. For both predictors, as they get bigger, there are more positive responses. I have plotted the data in a heatplot showing density of positive ...
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14 views

Characteristics Stability in Logistic Regression

Could someone please help me understand calculation of characteristics stability in logistic regression? What is a model point value in the formula?
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Logistic regression and dependent variables in R

I’m trying to do logistic regression, I utilize the following command: mylogit <- glm(Var0 ~Var1, data = mydata, family = "binomial") And I obtain a p-value ...
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Classification Algorithm For Small Sample Sizes

I am looking at a problem now where I need to train a classification algorithm. There are only 2 classes, lets call them A and B, and I want a value between zero and one indicating the probability ...
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1answer
88 views

From the Perceptron rule to Gradient Descent: How are Perceptrons with a sigmoid activation function different from Logistic Regression?

Essentially, my question is that in multilayer Perceptrons, perceptrons are used with a sigmoid activation function. So that in the update rule $\hat{y}$ is calculated as $$\hat{y} = ...
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How to fix dummy variables when I calculate predicted probability on logistic regression?

My question is about predicted probabilities in logistic regression. Let me make an example, analyze the relationship marriage (1: married, 0: single) as dependent variable and sex (1: male, 0: ...
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census data in predictive models

This may be a trivial question but I just wanted to confirm my thoughts on it. If I wanted to predict customer habits (just assume predicting a 0 or 1) and use various data at the customer level but ...