Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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How can I measure model performance with weighted logistic regression?
I am working with some survey data that uses probability weights. A number of sources explain that likelihood-based tests and fit statistics like likelihood-ratio, AIC, and BIC are not valid in the ...
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Logistic regression for classification: are there any analytical solutions for the out-of-sample accuracy?
I run a binary logistic regression, with a binary dependent variable and a continuous independent one.
Now I want to evaluate the out-of-sample performance of the classification algorithm so obtained. ...
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Interpreting regression coefficients based on Andrew Gelman's re-scaling method
I have two predictors in a binary logistic regression model: One binary and one continuous. My primary goal is to compare the coefficients of the two predictors within the same model.
I have come ...
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"Brute force" expected deviance for logistic regression?
A commonly used goodness of fit statistic for logistic regression is the deviance. This is also known as the likelihood ratio chi-square statistic. It is defined as:
$$D=\sum_{i=1}^{N}d_i^2$$
$$d_i^...
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Getting the bootstrap-validated AUC in R
In a paper by Faraklas et al, the researchers create a Necrotizing Soft-Tissue Infection Mortality Risk Calculator. They use logistic regression to create a model with mortality from necrotizing soft-...
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Fitting a Logistic Regression via Brier Score or Mean Squared Error
Is there a name for a logistic regression model that has been fit using the Brier score (or equivalently the mean-squared error) rather than the cross-entropy?
I realise this isn't maximum-likelihood, ...
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How to deal with underdispersion with binomial data
I'm working with a pretty large dataset (n = 4,500) where 10% of my points (pixels in a GIS landscape) are 1s and the rest are 0s. The full model for my data looks something like this:
...
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Frequency weights, rare events and logistic regression
I'm working on a model that requires me to look for predictors for a rare event (less than 0.5% of the total of my observations). My total sample is a significant part of the total population (50,000 ...
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Calculate goodness-of-fit (with deviance) to compare averaged models?
I need to compare the goodness of fit of several averaged logistic regression models by calculating the deviance explained. I'm using the MuMIn package in R to ...
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Numerical properties of the logistic growth model for non-linear regression
I am using the nls procedure in R to fit a logistic growth model. In their SSlogis function, José Pinheiro and Douglas Bates chose the formulation
...
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Train accuracy < Test accuracy with regularization
With a friend we were playing with the notMNIST data, logistic regression and regularization.
Without regularization, we could achieve a training accuracy (10k samples) of 78%, and test accuracy (15k ...
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How to subset alternatives in nested multinomial logistic regression?
I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
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Hidden vs Firth vs Shen-Gao logistic regression: dealing with the Hauck-Donner effect
In 1993 a version of penalized logistic regression was introduced by Firth in order to reduce the bias due to outliers and/or (quasi-)perfect prediction in logistic regression: Bias Reduction of ...
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Including feature-dependent priors on output class, in bayesian logistic regression
When doing logistic regression with data $D_N = \{(x_i, y_i)\}_i^N$ with $x_i \in \mathbf{X}^N$ (each data point has N features) and $y_i \in \mathbf{Y}$ being assigned output classes, in a Bayesian ...
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Is it possible to get a prediction interval for logistic regression via a latent variable?
carbocation asked how to compute prediction intervals for logistic regression. The answer was that prediction intervals don't make sense for logistic regression because the response variable only ...
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Geometric Interpretation of Softmax Regression
I'm writing a series of blog posts on the basics of machine learning, just for fun, mostly to validate my understanding of Andrew Ng's class. As I'm currently studying generalized linear models (GLMs),...
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Making new variable instead of correcting for temporal autocorrelation in a GLMM. Is it a valid alternative?
I am doing some forest disturbance research, in which the aim is to predict the probabilities of wind damage occurrence in forest stands of different site (altitude, slope steepness) and stand ...
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Maximum entropy classifier and sentiment analysis
I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
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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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What are the pros and cons of different metrics for evaluating a logistic regression model?
In the data science world, I have always evaluated the performance of logistic regression models simply using ROC/AUC. However recently, I've read from some traditional statistics source about some ...
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Mathematically Describing PCA chained with Logistic Regression
Python's scikit-learn package has a convenient pipe function that can combine machine learning techniques into one model with ...
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Is one-vs-all logit or multionomial logit regression more accurate?
What is advice of when to use one-vs-all logit or multinomial logit regressions? Most importantly, which one has a higher prediction power? Can one test hypothesis and estimate confidence intervals in ...
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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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How does the RMS package's nomogram calculate points for continuous variables?
I have been reading a number of papers where researchers have created risk scores based on logistic regression models. Often they refer to "Sullivan's method" but I have no access to this paper and ...
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Why beta regression?
According to http://r-statistics.co/Beta-Regression-With-R.html, the topline remark is:
Beta regression is used when you want to model Y that are probabilities themselves
Grammar aside, one may ...
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How should clustering be accounted for in logistic regression, when there are very few clusters?
I have survey data from 1000 patients. This is a convenience venue-based sample. In a specific city, at 9 hospitals that happen to have a psychosocial program, patients can opt into the program if ...
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Summing predicted probabilities from logistic regression using 'one vs. rest'
I have a multiclass classification problem that I have solved using a 'one vs. rest' approach via binary logistic regression classifiers from Python's scikit-learn package. In my problem, there are 3 ...
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Using Random Forests for modeling discrete choice problems
I am trying to model a discrete choice scenario in which (i) the explanatory variables are both individual- and alternative-specific, and (ii) the number of alternatives varies between individuals. ...
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Moran's test using the residuals of logistic regression
I have fitted a logistic regression and I would like to check for spatial autocorrelation in the residuals of the model. Is it statistically correct to implement the Moran's test using the residuals ...
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Which approach can be used to regress sleep time on brain mass, in this data set?
I was reading this blog post:
https://htmlpreview.github.io/?https://raw.githubusercontent.com/avehtari/BDA_R_demos/master/demos_rstan/sleep.html
the author describes a model to predict how many ...
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Does the Box Tidwell test for linearity of the logit require predictors to be in the range [0,1]?
Given a multinomial logistic regression model with 4 independent variables, 4 relevant interactions and a dependent variable with 3 categorical outcomes, I wanted to test for linearity of the logit.
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Censored logit transform for (ad hoc) exploratory data analysis
In my work I commonly have to analyze binary composition data, expressed as a fraction $f\in[0,1]$. The data $f[x]$ is spatially distributed ($x\in\mathbb{R}^n$, $n=1,2,3$), and typically comes in the ...
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How to know when to use Kernel SVM and not Linear SVM?
If I have more than 3 features in a dataset, then I can't visualize them to see if my classes are scattered in a non linear fashion. So how do I know when is the right way to use linear model with non-...
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Errors-in-Variables model for logistic regression
Simple question: I am familiar (though don't have tons of experience) with errors-in-variables regression. From what I have seen, this mostly is used with continuous outcomes in a linear model.
A) Is ...
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Put constraint on max (or min) predicted value (mgcv)
I want to fit my data using a logistic GAM model with cubic regression splines. I know for sure that in reality my estimated probability should not go above 0.5 (due to mislabeling). So I thought ...
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Aggregating Standard Errors for Predicted Probability Estimates
I obtain predicted values from a logistic regression for a certain outcome (e.g., mortality) at the hospital level – the data is at the patient level – and need to compute the average across hospitals....
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Diagnostics for multinomial and ordinal regression models
In the case of a binary outcome and a number of explanatory variables, logistic regression can be used and a number of diagnostic tools can be applied to assess the relative (e.g. AIC, if one wishes ...
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Mixed effect logistic regression in R: choosing random effects
I conducted an experiment which measured a binary response for each subject. The subjects were in 1 of 3 groups. There were two other fixed factors, each of which were continuums (cont1, cont2) ...
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Running regularized logistic regressions on very large datasets
I want to run a regularized logistic regression on a dataset with 25 million observations and about a 1000 mostly non-sparse columns with non-ignorable weights.
My first choice would be BayesGLM, ...
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Separation in logistic regression in a complex survey?
Firth's penalized maximum likelihood estimates, exact logistic regression and Bayesian logistic regression (e.g. bayesglm) can account for separation in logistic regression. But how to account for ...
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Singularity issues in multinomial logit model with differing choice sets
I am estimating a discrete choice model in which individuals choose which schools to attend.
I have a large amount of data on individuals and schools. However, each particular school only appears in ...
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Confidence interval for proportions
I have some data like this:
id pop var
1 593 51
2 592 31
3 346 20
4 1214 70
5 1063 66
6 1370 71
each ...
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Modeling delayed feedback using logistic regression
Suppose we are trying to model the probability of a user clicking on an ad using logistic regression. We will receive only the positive feedback so, we define $Y = 1$ when success was observed, $Y=0$ ...
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Different coefficient estimates from ncvreg and glmnet in logistic regression
I'm trying to compare the results from glmnet and ncvreg in logistic regression. The methods have similar coefficients estimates ...
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Can you use regression to predict values if you imputed data using MICE?
I used multiple imputation on a data set that had some missing values (I had to do this as the sample size was low so I couldn't just exclude the NAs). I know you can do
...
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Mixed-effects logistic regression
I'm new to data analysis and I'm trying to perform a mixed-effect logistic regression.
My data look like this:
...
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Do we have the actual theoretical study of L1/L2 regularization for Logistic regression?
It is very well known that L1 and L2 regularization can help in reducing the generalization error, and their effectiveness has been empirically demonstrated across a large set of machine learning ...
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