Join Monika Wahi for an in-depth discussion in this video Model metadata, part of Healthcare Analytics: Regression in R.
- [Narrator] This movie will introduce you…to the Model Metadata we will keep…about our logistic regression models.…First, we'll look at the structure I use…to keep metadata on logistic regression models.…Next, we'll re-run Model 1,…just so we can take a look at it,…and figure out what to put on the metadata table.…Then, I'll show you how I recorded the Model 1 metadata.…After that, we'll re-run Model 2,…and I'll show you how I documented Model 2…on the metadata table.…As you know from our linear regression models,…I always keep model metadata.…
That's how we can keep track of all the models we run…in round two and round three.…Just like with linear regression,…we need to keep track of some minimal information…about each of our models all in one place.…My style is to keep track of the Covariates…I included in each model…and to know which ones were significant.…I also keep comments as to why I added…or removed a variable.…But, the logistic regression model metadata…are slightly different…than the linear regression model metadata.…
Author
Monika Wahi
Released
1/19/2017- Dealing with scientific plausibility
- Selecting a hypothesis
- Interpreting diagnostic plots
- Working with indexes and model metadata
- Working with quartiles and ranking
- Making a working model
- Improving model fit
- Performing linear regression modeling
- Performing logistic regression modeling
- Performing forward stepwise regression
- Estimating parameters
- Interpreting an odds ratio
- Adding odds ratios to models
- Comparing nested models
- Presenting and interpreting the final model
Skill Level Advanced
Duration
Views
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Introduction
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Introduction to the course1m 40s
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1. Designing Your Research
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Scientific method review5m 13s
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2. Preparing for Linear Regression
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Indexes7m 20s
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Quartiles3m 41s
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Ranking4m 45s
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Regression review3m 53s
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Preparing to report results2m 39s
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3. Beginning Linear Regression Modeling
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Overview of modeling process4m 58s
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Linear regression output5m 29s
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Models 1 and 23m 21s
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Model metadata4m 20s
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4. Final Linear Regression Modeling
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Beginning Model 35m 7s
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Making a working Model 35m 23s
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Finalizing Model 33m 33s
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Looking at the final model5m 58s
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Fishing and interaction5m 5s
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Defending the final model4m 27s
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Presenting the final model6m 27s
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5. Preparing for Logistic Regression
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Odds ratio interpretation6m 26s
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Basic logistic code3m 53s
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6. Developing the Logistic Regression Model
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Running Model 15m 24s
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Adding odds ratios to models5m 35s
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Model metadata6m 36s
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Forward stepwise: Round 25m 53s
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Models 1 and 2 presentation6m 30s
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Model 3 presentation4m 14s
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Conclusion
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Review of metadata4m 41s
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Review of the process3m 21s
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Next steps2m 19s
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Video: Model metadata