Unanswered Questions
4,154 questions with no upvoted or accepted answers
19
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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 ...
CommunityBot
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13
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0
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272
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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. ...
- 2,578
13
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0
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778
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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 ...
CommunityBot
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10
votes
0
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744
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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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10
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387
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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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9
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6k
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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-...
- 65.9k
8
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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, ...
- 84k
8
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0
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326
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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 ...
- 84k
8
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What is the intuition for testing seasonal difference with OCSB test and its correct application?
I have daily time series data of a shop's revenue. Now I would like to test for seasonal differencing with the OCSB test originally intrduced in (Osborn et al. (1988): Seasonality and the Order of ...
- 103
8
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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:
...
- 84k
8
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0
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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 ...
CommunityBot
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8
votes
0
answers
652
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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 ...
CommunityBot
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8
votes
0
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380
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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
...
- 1,332
8
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1
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832
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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 ...
CommunityBot
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7
votes
0
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207
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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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