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

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Maximum likelihood deviance = lowest Bayesian deviance?

I've just run a logistic regression using the standard frequentist maximum likelihood approach and then again using Bayesian MCMC (weak priors, all ~ $n(0, 100)$). I calculated the deviance for each ...
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What is the difference between skewed logistic regression and rare event logistic regression

I was doing a traffic safety analysis. My understanding is that if I have a sample that the response distribution differs a lot. for example, I have 200 events, but only 20 of them are crashes. I ...
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What should be my initial values of coefficients while iterating over gradient descent for logistic regression?

I have understood how to get the cost function of logistic regression. Now I want to iterate and perform a gradient descent on the function. Should I choose {0} as my initial values for coefficients?
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Relative importance of multiple correlated independent variables in logistic regression

I have a multiple logistic regression with 11 independent variables (x1 to x11). I have another 4 continuous IVs that are highly correlated with each other and correlated with x11. The 4 IVs along ...
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Controlling for categorical variables when generating logistic regression elasticity curve

Assume we have a multiple logistic regression model with 3 continuous independent variables (x1,x2,x3). I understand that if we want to create an elasticity curve for a continuous variable of ...
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30 views

Difference Between Logit Models and Logistic Regression? [duplicate]

I know these two model has different equation, but I am not sure why people use logistic model instead of logit model and vice versa? What is the main reason behind it? If my response variable is a ...
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Simulate/Generate Data for Multinomial Logistic regression

How to simulate data for Multinomial Logistic regression? For Example i want to generate a high dimensional data set with 90 subjects and 500 independent predictors. The ratio of Classes should given ...
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How can we change gender preference into dichotomous variable when the categories are male, female or any in order to conduct logistics regression?

How can we change gender into dichotomous variable when the categories are male, female or any in order to conduct logistics regression? My research is about gender preferences at the time of birth, ...
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Logistic Regression: multicollinearity and Kappa statistics

I may be wrong but from my understanding logistic regression requires there to be little or no multicollinearity among the independent variables, and yet Kappa statistics as part of postResample() ...
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Is meta-analysis of odds ratios essentially hopeless?

In a recent paper Norton et al. (2018)$^{[1]}$ state that Different odds ratios from the same study cannot be compared when the statistical models that result in odds ratio estimates have ...
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Dropped 2 Categories in Dummy Variables (Logistic Regression)

I understand that when modeling, dummy variables should be k-1 and the dropped category should be the baseline. However, I do not know how to interpret if after feature selection 2 more categories of ...
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SPSS - Binary logistic regression: classification cutoff

Let's say I want to evaluate the predictive value of a continuous variable in the prediction of malignancy (event/status) of a tumour. Malignant = 1 Nonmalignant = 0 In SPSS, I can run a binary ...
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How to prove that the negative log-likelihood for logistic regression is convex?

How to prove that for logistic regression with individual observations, the negative log-likelihood is convex? I found this wonderful post here that explains why the log likelihood of logistic ...
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1answer
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CartPole: Using sigmoid and softmax cause program converge differently [on hold]

I am playing with the CartPole problem. It works but when I switch from Sigmoid to Softmax at the end of the network, as input for multinomial distribution, the program behaves quite differently. ...
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235 views

Does the function $e^x/(1+e^x)$ have a standard name?

Does a function in the form $e^x/(1+e^x)$ have a standard name? E.g. $y = a + bx$ is a linear function.
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Accounting for “surgeon preference” when modeling a binary surgical outcome

I'm trying to model the risk of a binary event following surgery (event=admitted for observation, y/n), and my main predictor is a three-level 'treatment' administered during the procedure (Tx_A, Tx_B,...
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Is it possible to compare continuous vs. dichotomous decisions in a meaningful way, that doesn't come down to different kinds of analyses?

I'm interested in finding out how the factors that contribute to a person's decision change when they are making a continuous vs. a dichotomous choice. However, I'm concerned that these two types of ...
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Can we compare odds ratio from one factor to another factor?

For example ${\rm logit}(\pi)=\beta_1 \cdot x_1+ \beta_2 \cdot x_2$, then I find that the odds ratio of $x_2$ is 27, and the odds ratio of $x_1$ is 1.5, can I say that $x_2$ has a more significant ...
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OLS weight bias with binary outcome

The typical approach when you have a binary outcome variable is to use logistic regression. If you use OLS regression then it becomes easy to violate various assumptions (normality of residuals, ...
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Inference using panel data regression model?

I have a balance panel dataset in the below mentioned format where Y is the dependent variable and {X1, X2, X3, X4, X5, X6} are independent variables. ...
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Learning similarity metric from data

I want to measure the similarity of fastText vectors. Typically, for vector similarity, the cosine similarity is used. I would like to learn a notion of similarity based on the labels of the tokens ...
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Heteroskedasticity underlying data generation process in logistic regression

We can develop the logistic regression model using the latent variable approach: \begin{equation} y = \begin{cases} 1, & \mathrm{if}\ X\beta + \epsilon > 0\\ 0, & \mathrm{otherwise} ...
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Name of logistic regression with two dependent variables?

my understanding is that Multinomial logistic regression is where your dependent variable could take values of 1,2 or 3 where 1-3 are classes. But what isit called if you have two dependent variables ...
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Can I calculate odds ratio for Firth's Bias-Reduced Logistic Regression?

I have data that I apply a test called Firth's Bias-Reduced Logistic Regression. I am using R with package called brglm. I got coefficient and I will try to find a way to calculate a confidence ...
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Approximation or closed form equation for summation of logistic function [closed]

The spread of epidemics follows a logistic growth, given in the equation below $I(t) = \frac{N}{1+(N-1)exp^{-rNt}}$ where, N is the population size, r is infection rate, t is time , I(t) is ...
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mixed logit estimation in R gmnl

I want to estimate the random parameters logit model with 2 transport mode alternatives taking into account only the total cost (log-normal) and total time (normal) of the trip in R gmnl, and then ...
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What are advantages of “meta-models”?

Suppose you want to create a credit score that differentiates risk between different applicants. What are the advantages of having multiple sub models that are combined to generate the final score ...
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Mathematically applying regularisation to logistic regression to reduce over fitting

I recently was introduced to the concept of regularisation to reduce the process of overfitting in logistic regression were the curve fits too perfectly to a point where our non linear line does a ...
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Accounting for Interaction between Independent variables

Is there some trustworthy resource on effect of interactions if not accounted for. Especially the situation is often, let's assume heart surgery, age, gender and logistic euroscore(derived from age ...
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Interpretation of P-value vs. Plot when performing Logistic Regression in R

I am new to R and Logistic Regression so I will try to be as clear as possible. I did the following really as a test case as proof of concept of what I've been trying to learn. I have performed ...
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21 views

can you trust the statistical control

I'm reading a paper that compares two vaccine types. THe people who got vaccine 1 are of lower SES and have more chronic conditions compared to those who got vaccine 2. Multivariate logistic reg was ...
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28 views

Logistic linear mixed effects model with categorical predictors

I've read a lot of threads on stack exchange but haven't exactly found what I'm looking for. Everyone seems to have a slightly different problem/issue. First, lets have a look at my data: 120 Users ...
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Multiple Comparisons, Risk Models

I am planning to do a survey which collects information on four predictors of crime using Likert scales along with demographics. Some of the demographics are categorical. The response variables are ...
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1answer
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Confidence intervals for emmeans estimates after multilevel binary logistic regression

I ran a multilevel binary logistic regression / generalized linear mixed-effects model in R, and then ran the following code to get post-hoc tests for a significant A x B interaction where A is a ...
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Using a logistic regression to predict binary outcome

Any input on my following query would be great. Currently I have a a number of monthly datasets of individual(s) who have purchased properties (2012-2017). These individuals can be split into two ...
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Comparing complex proportions between groups

I have a set of data where cells were collected from a number of subjects and phenotypically categorized (sample data below). I'm interested in asking how sets of antigen-specific cells change in ...
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Testing for particular association with logistic regression

I have conducted a study where I have two binary independent variables (let's call them A and B) and one dependent binary variable C (it is a judgment of a sentence by native speakers — yes or no). I ...
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logistic regression vs. linear regression

in the following table (association between cognitive score and Folate intake), the beta coefficient represents difference in slope between different groups with the standard group. the interpretation ...
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Binary logistic regression with and without Generalised Estimated Equations (GEE)

I have two questions which are related so will include them as one. So I have repeated measures data (pedestrians rejecting and accepting gaps on a road, the rejected gaps are repeated measures ...
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Issues fitting logistic GEE model in R [closed]

I am trying to predict the probability of having symptoms (binary) after unintentional impact (3 categories: 0, 1, 2+). However, I am having difficulty fitting the logistic GEE model in R. ...
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Calibration of penalized (LASSO or ELasticNet) logistic regression models

I would be very grateful for any help me with the following general query regarding calibration of penalized models with a binary outcome. I would like my prediction model to be calibrated (mean ...
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Logistic Regression - Distribution of predictor within outcome category

Assume a binary outcome variable with a single standard normal distributed predictor in a logistic regression. As sample size goes to infinity, what is the the within-outcome-category distribution of ...
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7-point Likert scale

I have about 19 participants in a training program. I am trying to study the attitude which it consists of three components: affective, cognitive, and behavioral and see how it can predict their ...
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Log response ratio of means not agreeing with linear mixed model output

I have a linear mixed model structured like so: Richness~Time*Treatment+(1|Site) Time has two levels (Pre and Post) as does Treatment (Treatment and Control). ...
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Alternative to logistic regression

With this synthetic data set (the relationship between survival/death and the factor x) (plotted in the below figure as blue points), I would like to know how the survival probability depends on the ...
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Difficulty understanding contingency table and logistic regression coefficient

I have the following contingency table D not-D E 980 122 not-E 2420 6439 where D may stand for disease, and E for exposure. This leads to the ...
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why is calibration of my logistic regression s shaped?

I am simulating data to compare real and predicted probabilities from logistic regression like this: ...
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Whenever I am building the first model in Logistic regression there is an error [closed]

Whenever I am building the first model in logistic regression, it is throwing the error shown below. My code is: ...
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1answer
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Where to start: own simulations on logistic regression. Hosmer-Lemeshow, Farrington, Pearson Chi-Square [closed]

I'd like to run my own simulations on the logistic regression model. I want to test on different grouping strategies, spare data and strategies for combined small groups. I read a lot of papers by ...
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What is the best way to display confidence intervals around a proportion?

I was reviewing an article about the effectiveness of a vaccine and it expressed these in % effectiveness = 1 - odds ratio. So far, so good. But they showed confidence intervals around the %'s, both ...