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Results tagged with binning
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user 28500
Binning means grouping a continuous variable into discrete categories. It is particularly used in reference to histograms, but could also be used more generally in the sense of coarsening.
1
vote
Plotting average shows (log) linear trend but fitting line has 0 p value
In this particular case, the binning has shown further information that you might want to explore. …
- 101k
2
votes
Accepted
Equivalent of Kaplan Meier for an unbounded number of sets
Your sense that Cox regression is a better solution is correct.
It's generally not a good idea to break up a continuous predictor variable. One useful approach is to use a flexible form like a spline …
- 101k
2
votes
Using the average as a cut off to group data and compare groups?
The extensive discussion of binning continuous variables goes into useful detail on the dangers of binning and better ways to proceed with non-linear relations among variables. …
- 101k
5
votes
Accepted
Methods to derive cut-offs for continuous variables
Don't do this, as it particularly doesn't make sense for a random forest model. In addition to the many reasons that categorizing a continuous predictor is a bad idea, it undercuts a potential strengt …
- 101k
4
votes
Accepted
Cox-Proportional Hazards Survival Curve has too many lines - can binning the continuous vari...
You need to think differently about the Cox PH analysis itself and the way that you display the data.
Keeping the variable as continuous in the Cox PH analysis itself is important, as you recognize. …
- 101k
2
votes
Using decision tree for unsupervised discretization?
Based on the example added to the original question, it seems that you already have data on the "popularity" of the articles. In that case I agree with @ttnphns that the best approach for discretizati …
- 101k
6
votes
Post-hoc power size calculation
First, as Russ Lenth has put it:
You've got the data, did the analysis, and did not achieve "significance." So you compute power retrospectively to see if the test was powerful enough or not. Th …
- 101k
1
vote
Logistic regression using a predictor that's part of the outcome
What you need to do is to evaluate the calibration of at-home versus in-office blood pressure (BP) monitoring. Forget the cutoff.
A systolic BP value > 130 mmHg has been used as a cutoff for defining …
- 101k
1
vote
Accepted
Probability as a function of age from observations over several years
This seems like a study that is best analyzed by standard methods of survival analysis. If you know the actual age at which tanks fail and have data on the ages of the tanks that haven't failed, survi …
- 101k
1
vote
Optimal multivariate binning where the cut-points must be the same for all observations
Binning continuous predictors in this way is probably not a good idea. Cut-points determined on a particular data sample are likely not to work as well on later data samples. … You could evaluate how well the post-model binning works by repeating your entire process on multiple bootstrap samples from your data. …
- 101k
5
votes
Should discretized continous varibles be treated as numeric or ordinal (in a GLM)?
Those are among the reasons that binning is not a good idea. … That, rather than prior binning, would be the "standard" approach to your problem. …
- 101k
1
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Combining quantile regression with binning
Thereafter, you can get outcome quantile estimates based on any desired quantile or binning of your predictor. …
- 101k