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Questions tagged [logistic]

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

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If the loglikelyhood function of $Y$ is $\log \binom {N}{Y}+Y \log p+(N-Y)\log (1-p)$ what is the meaning of “$Y$ is linear in $\log \frac {p}{1-p}$”

If the loglikelyhood function of $Y$ is $\log \binom {N}{Y}+Y \log p+(N-Y)\log (1-p)$ what is the meaning of "$Y$ is linear in $\log \frac {p}{1-p}$" The following taken from a book by W. Stroup on ...
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Treating Outliers [on hold]

Can the below methods be used for outlier treatment? 1. Capping the outliers with percentile values (1st-99th, 5-95th)? 2. Capping the outliers with Q1-1.5IQR, Q3+1.5IQR (will it lead to bias?) 3. ...
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Usig sigmoid function 's' in logistics regression, how this '1' calculated in s(1.805)? [on hold]

p(+|x) = P(Y = 1|x) = s(w·x+b) = s([2.5,-5.0,-1.2,0.5,2.0,0.7]·[3,2,1,3,0,4.15]+0.1) = s(1.805) = 0.86 Usig sigmoid function 's' in logistics regression, how this '1' calculated in s(1.805)? p(-|x)...
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is “multivariable linear regression” the same as logistic regression?

I am new to machine learning and I am simultaneously studying linear and logistic regression. Logistic regression is when there is one dependent variable and there may be more than one independent ...
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How to extract predicted probabilities from glmer results for a logistic mixed effects model [on hold]

I have two groups that I follow over 4 time points (Baseline, Three months, Six months, and Year). The outcome is some binary variable, lets say presence or absence of cancer. ...
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1answer
26 views

Regression analysis not showing the first level of treatments

I have my data that looks like this: data: or in R: ...
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Bootstrapping logistic fitting from bimodal samples

I'm trying to implement a bootstrap method to obtain confidences intervals of a fitted logistic curve. I've been learning a lot because I'm new to these things, so I ask forgiveness if the question ...
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Interpretation of linear, quadratic, cubic outputs of ordinal predictors in glm calculation

I have a binary dependent variable, and some numeric, binary, and ordinal independent variables. The whole idea is to create a predictive model based on all these data, which can be reported to others,...
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1answer
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How to interpret this logistic regression question?

This is from a DataCamp course. Try as I might, I can't seem to figure out how to break this down into explanations: The fitted coefficient β^1 from the medical school logistic regression model is 5....
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Regression analysis with multiple categories

I have this data below which I am analyzing using R. First, I am trying to find which predictors (chem1, chem2 and chem3) have effects on yield for this data. I did this model test below and found ...
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Estimating/translating odds ratio for different percentile intervals

I have a question regarding a translation in odds ratio curve OR(x). More specifically, let's say we have an OR(x) which we get using logistic regression. We look at the interval bounded with 25% and ...
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Do we use confusion matrix for Gradient descent [on hold]

I would like to use two algorithms, and then evaluate their result. Both of them are based on logistic regression, fitted with gradient descent. The second model contains additional features. I would ...
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Applying logistic regression with response and expected response

I hope my title is phrased correctly, otherwise feel free to rephrase it. This is my first time working with such a data set and i'm trying to understand if a method i'm using is correct. Here is a ...
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Reasoning behind dichotomization of (explanatory) categorical variable for binary logistic regression?

Generally speaking, what would be the idea behind dichotomizing ordinal, categorical variables for binary logistic regression. To be clear, I'm not talking about the dependent variable but only ...
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One versus One and One versus All multiclass classification using logistic regression in python [on hold]

This is my understanding of OvO versus OvA: One versus One is binary classification like Banana versus Orange. One versus All/Rest classification turns it into multiple different binary classification ...
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ROC for Logistic Regression in SPSS [on hold]

I have a data set with rare events and I want to set an optimal cutoff point for my binomial logistic regression. How can I generate an ROC curve of different cutoffs to determine the optimal one. ...
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Unusual DV Odds Ratio for multiple Binary Logistic Regression

I am attempting to diagnose issues with the DV odds ratio and resulting 95% CI for the final step of my logistic regression. As you can see in the below image, when the "Continuous F" variable is ...
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What kind of analysis would be appropriate for my data?

I'm working on a project looking at the geographic distribution of a type of physician in the United States. My data is as such: I would like to identify zip code level characteristics that predict ...
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how to make logit function from 2 nominal independent variable and 1 interval scaled independent variable? [closed]

im a college student, modificating a journal about financial knowledge and financial behavior. The variables are: "objective" financial knowledge (IV), "subjective" financial knowledge (IV), ...
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1answer
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Why cannot the model $\frac {y_{i,j }} {N_{i,j } } = \beta_0 + \beta_1 X_i + e_{i,j }, \ y_{i,j}\sim B(N_{i,j},\pi_i)$ have constant variance?

The following example is taken from a book by Walter Stroup on Generalized linear mixed models, and are supposed to show some limitations on trying to write models in equation form. Let $y_{i,j } \...
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Interpretation calibration plot RMS package

From what I understand from earlier postings the calibration plot in the RMS package can be useful : " Now with pure calibration accuracy you can sometimes judge a model to be inadequate no matter the ...
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Distribution to Examine Perceived Impact of Aircraft Flyovers [closed]

I am attempting to model the impact of aircraft flyover noise and had a two question survey filled by students 1) The frequency of disruption of their study sessions per day 2) The perceived noise ...
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Selecting landmark in SVM [closed]

I have difficulty to select landmarks l(i) in SVM. Suppose we have 3 features (m = 3), and we know that x(i) = l(i) (landmarks should be exactly the same as the training example), so which feature's ...
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Comparing GLM in R with custom SGD [on hold]

I am working on a project which requires a costum implementation of an SGD in Scala to solve logistic regression problems. As a baseline for correctness, I have among other things compared the results ...
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1answer
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Treating dummy variables that describe characteristics of another (also in the dataset)

In this question, we assume we have a health dataset with many triplets of dummy variables. The dataset looks like this: (existence_of_symptomA (1/0), symptomA_chronic (1/0), symptomA_persistent (1/...
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Fitting logistic regression: maximum likelihood vs maximizing likelihood times the prior

When deriving the coefficients of the logistic classifier, in the Elements of Statistical Learning(ESLI) book it is stated: Logistic modules are usually fit by maximum likelihood, using the ...
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3answers
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R Logistic regression and correlation

I'm having trouble understanding the output of this logistic regression in R. Data ...
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1answer
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Statistical significance testing and confidence intervals using RepeatedKFold

How do I get a confidence interval of a model measure significance between models when I do repeated KFold cross validation using ...
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1answer
25 views

interpreting intercept in logistic regression with binary variable in R

I am using R to run a logistic regression to analyze how a categorical variable ("population") correlates with a binary variable ("response") and am having some trouble interpreting the results (shown ...
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1answer
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Logit model for hundres of items - can and should I use the items as a category variable?

I am in the early phase of a new project about looking at multiple factors that potentially influence the probability that an item fails quality inspection. I am interested in seeing whether each ...
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How to compute the loss function using Mc Fadden pseudo r-squared

I have currently developed an optimised deep learning model (DNN) using cross-entropy loss function. To objectively compare my DNN model with conditional logistics I need to obtain McFadden pseudo r2 ...
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Estimating a logistic regression with OLS? [duplicate]

NB: This question is different from this one which assumes that we have computed the LHS of the regression equation with no issue. My question is about how to compute this LHS. Consider a simple ...
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Is it reasonable to include both independent variables and their interaction terms in a linear predictive model?

Suppose i have outcome variable O (effective/not effective), drug dosage A and variables B&C, and i want to train a linear model to predict O. B&C can only influence O by modifying metabolism ...
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1answer
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Generate odds ratios across deciles / quantiles of an indpendent variable

With reference to the following figure from Bellomo et al., 2011: How exactly are the odds ratios across the deciles 'referenced against the 4th decile' calculated? My initial impression is that a ...
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1answer
27 views

Choose one variable between 50 for logistic regression

So I have a data set which consists of roughly 100 independent variables, and then one dependent binary variable (the outcome). And each variable has roughly 1000 cases. Obviously I wish to do a ...
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Log-likehood : ommited value

I searched the second form of the log-likelihood equation which appear in A Probabilistic Perspective of Kevin Murphy because I've tried to understand why $y_{ic}$ disappear in the second part of the ...
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1answer
36 views

How to make my logistic model more flexible?

I'm working on a homework assignment, and I'm not sure if I did it right up until this point, and I'm not sure exactly where to go from here. Here is the circle.txt file I understand the first ...
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1answer
27 views

Sample Selection Bias in Logistic Regression [duplicate]

I'm working on a classification problem where I expect $True\ Positive\ Rate =0.999$ $True\ Negative\ Rate = 0.001$ To model this data, I have created a training set with an equal proportion of ...
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Performing and interpreting a logistic regression using ordered variables in R

I'm currently working on my first larger project with self-collected data and only few guidelines. My dataset contains 29 variables, all of which are categorical and most of which are ordered (with 2 ...
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Multinomial logistic regression on document-topic probabilities with user rating

I am analyzing a dataset of customer reviews with topic modeling and am trying to quantify which topics have a positive/negative influence on the given ratings. I have computed a topic model using LDA ...
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16 views

Adding an indicator variable to conditional logistic regression

Assuming I have a conditional logistic regression model as follows: $P(Y=1|X = x) = \frac {e^{\alpha+\beta x}} {1+e^{\alpha+\beta x}} $ If Z were to be an indicator variable that takes values of 1 ...
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Interpreting Logistic Regression Categorical Coefficients

So I have this question: If we fit a logistic regression with categorical predictor X with categories A, B and C, and have the estimated coefficients β0=−2.5 and βB=0.5 and βA=−0.2. (a) Interprete ...
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data analysis of categorial dicothomical variables

I have the following dataset I would like to calculate the frequency distribution of the response F or G in each participant, for each condition (MIO,TUO, NEUTRO) for a total of 15 images ...
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1answer
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Logistic regression using rms: calculate odds ratio and p-value for specific unit of change

I try to calculate the odds ratios and p-values for continuous and categorical predictors at a specific unit of change (e.g. odds ratio for a change of 10 years, not 1 year) of a multiple logistic ...
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Logistic Regression Simulation Apparent Bias

Hi Other Stats Humans! I am simulating some data and I am observing some unexpected results. After simulating the data, I found that when the true odds ratio is less than one, the estimated odds ...
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1answer
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Logistic Regression - Finding out information about a particular $X_i$ when all other Xs and parameters are known

Suppose someone made the following logistic regression: $Logit(p)$ = $\beta_0 + \beta_1X_1 + \beta_2X_2$ Now, someone else is trying to replicate the model creation, but by mistake the $X_2$ column ...
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3answers
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Multiple regression for a questionnaire study

Let's say I have a study in which I send a 100 people a questionnaire in which the dependent variable is binary, such as "does x, y and z correlate with whether a person is obese or not obese". Would ...
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Why is the intercept changing in a logistic regression when all predictors are standardized?

I'm conducting a logistic regression in R using glm. My outcome is race (White = 0, Black = 1). The data are below: ...
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Dummy*Continuous moderating variable creates too many missing values (only those with value 1 for dummy have values for continuous moderator)

So I am trying to explore the moderating effect of satisfaction of customer service on the main effect of experience of city customer service on public meeting attendance (IV: Experience, dummy / DV: ...
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1answer
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How to do CV in logistic regression when predicton doesn't work?

Having fit a logistic regression, I want to do cross-validation. How can I do this? The usual method of computing predictions and calculating the accuracy doesn't work ... because ... there's nothing ...