# Tag Info

### Hierarchical logistic regression interaction

Besides the coefficients for x and z and their interaction, your HLM software presumably can return the corresponding variance-...
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1 vote

### Logistic regression for evaluating explanatory power of features - interpretation of score and confidence

The analysis scheme is far from sound, in more ways than are noted in comments. It's particularly bad with a binary outcome model. First, pre-selection of features based on single-variable models is ...
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### Confusion about the use of the MLE & the posterior in parameter estimation for logistic regression

What usually goes under the name "maximum likelihood estimation" is a frequentist approach, not Bayesian. So $\beta$ is a fixed constant. Then the likelihood the OP wrote is formally not ...
• 59.8k
1 vote

### Assessing model fit in logistic regression with multiple imputation

In addition to the answer by EdM, if you want to evaluate out-of-sample prediction error, it is not hard to combine multiple imputation with train-test splitting or cross-validation. Then, you should ...
• 96
Accepted

### Assessing model fit in logistic regression with multiple imputation

Section 3.9 of Frank Harrell's Regression Modeling Strategies discusses this matter in some detail, with code. A suggested approach, based on a paper by Chang and Meng (Statistica Sinica 32: 1489–1514,...
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### Odds ratio and p-value

Yes. "High" and "low" are not absolute terms but depend on the scale of the predictor. For example, if money affects the odds of the outcome, then having money measured in cents ...
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### Odds ratio and p-value

Yes. An odds ratio is a (somewhat unintuitive) measure of how "large" an effect is. A p value is a measure of how confident we can be that the effect size is significantly different from &...
• 5,077
Accepted

### Where is there is only set of odds ratio in ordinal logistic regression?

The reason that an ordered logit only gives you one set of odds ratios is that it assumes (maybe correctly, maybe incorrectly) that one set are all that you need. The model is governed by something ...
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### Where is there is only set of odds ratio in ordinal logistic regression?

This is explained in detail here (start with the first link to BBR). If you want to allow ratios of odds of $Y \geq y$ to vary with $y$ without restriction, use the polytomous (multinomial) logistic ...
• 95.9k
1 vote

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### Can we use a partial F test for logistic models?

The F-test can be used when the distributions of the error terms are not normal. However, one needs to make an adjustment to the degrees of freedom used. An example for the case with truncated normal ...
• 82.6k
1 vote

### Hypothesis testing categorical variables: Should we include them in logistic regression models?

Presumably you are interested in something like "Does the new UI element work for everyone (irrespective of device or race)?". I assume your model is something like ...
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### Is a logistic regression appropriate for my question and correctly interpreted?

Yes, you could use the logistic regression, but I find a simple contingency table much more intuitive. ...
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### Logistic Regression on Time Series Data

Your data looks as if someone tries until they are successful in each period (except for the last period, though maybe the data there is not mature). My approach below ignores that aspect of the ...
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### Standardized coefficients logistic Regression R

If you choose to go forward despite @Frank Harrell's cautions, you can use the formula explained in Menard, Scott (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage Publications. ...
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### Deriving predicted probabilities from gologit2 (proportional odds models) output

The Williams approach uses the Brant test for proportional odds. That this test is invalid has been pointed out since 1991 (Peterson & Harrell J Royal Stat Soc C). For related information see ...
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### Should I Use Regularization in Univariate Logistic Regression for Diagnostic Methods Comparison?

I assume your goal is to predict Method 2 with Method 1's value. Regularization shrinks the parameter to 0. As you have only one input, the shrinkage is less meaningful. I view the regularization ...
1 vote

### Logistic Regression on Time Series Data

For this kind of data, I would consider a State-Space model that allows a real-valued, latent $process$ to evolve over time while also modeling the observations as a Bernoulli process. The challenge ...
Accepted

### Need help understanding odds ratio over time example

First, LogisticRegression is a penalized variant of logistic regression. It applies a penalty to the coefficients, so there is a good chance you won't get the same ...
• 38.2k
1 vote

### Need help understanding odds ratio over time example

You should transform your data into long format and run a logit of visit type (coded 0 if one, 1 if more than one) on year (entered continuously) with the number of visits as a frequency weight. The ...
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