Regressie-uitvoer analyseren

Je wilt weten hoe goed het model dat je zojuist hebt gebouwd bij de data past. Om dit te meten, wil je de mediaan van de absolute verschillen tussen de werkelijke en voorspelde inkomsten berekenen. Je voert de volgende regel code uit:

median(abs(biopics$earnings - model_1$fitted.values), na.rm = TRUE)

Wat denk je dat het resultaat zal zijn?

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Omgaan met missende data met imputaties in R

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Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

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In this chapter, you’ll find out why missing data can be a risk when analyzing a dataset. You’ll be introduced to the three missing data mechanisms and learn how to recognize them using statistical tests and visualization tools.

Exercise 1: Ontbrekende data: wat kan er misgaan Exercise 2: Lineaire regressie met onvolledige gegevens Exercise 3: Regressie-uitvoer analyseren

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Exercise 4: Modellen vergelijken Exercise 5: Mechanismen achter ontbrekende data Exercise 6: Herkennen van mechanismen voor ontbrekende data Exercise 7: t-toets voor MAR: datavoorbereiding Exercise 8: t-toets voor MAR: interpretatie Exercise 9: Ontbrekende datap patronen visualiseren Exercise 10: Aggregatieplot Exercise 11: Ruggengraatafbeelding Exercise 12: Mozaïekdiagram

Get to know the taxonomy of imputation methods and learn three donor-based techniques: mean, hot-deck, and k-Nearest-Neighbors imputation. You’ll look under the hood to see how these methods work, before learning how to apply them to a real-world tropical weather dataset. Along the way, you’ll also learn useful tricks that you can use to make them work even better for your problems.

Exercise 1: Mean imputation Exercise 2: Smelling the danger of mean imputation Exercise 3: Mean-imputing the temperature Exercise 4: Assessing imputation quality with margin plot Exercise 5: Hot-deck imputation Exercise 6: Vanilla hot-deck Exercise 7: Hot-deck tricks & tips I: imputing within domains Exercise 8: Hot-deck tricks & tips II: sorting by correlated variables Exercise 9: k-Nearest-Neighbors imputation Exercise 10: Choosing the number of neighbors Exercise 11: kNN tricks & tips I: weighting donors Exercise 12: kNN tricks & tips II: sorting variables

It’s time to learn how to use statistical and machine learning models, such as linear regression, logistic regression, and random forests, to impute missing data. In this chapter, you’ll look into how the models make their predictions and use this knowledge to draw the imputed values from conditional distributions. This is important as it ensures your imputations are more varied and plausible, making them more similar to the true data.

Exercise 1: Model-based imputation approach Exercise 2: Linear regression imputation Exercise 3: Initializing missing values & iterating over variables Exercise 4: Detecting convergence Exercise 5: Replicating data variability Exercise 6: Logistic regression imputation Exercise 7: Drawing from conditional distribution Exercise 8: Model-based imputation with multiple variable types Exercise 9: Tree-based imputation Exercise 10: Imputing with random forests Exercise 11: Variable-wise imputation errors Exercise 12: Speed-accuracy trade-off

Imputed values are not set in stone. They are just estimates and estimates come with some uncertainty. In this final chapter, you’ll discover how bootstrapping and chained equation using the mice package can be used to incorporate imputation uncertainty into your models and analyses to make them more reliable and robust.

Exercise 1: Multiple imputation by bootstrapping Exercise 2: Wrapping imputation & modeling in a function Exercise 3: Running the bootstrap Exercise 4: Bootstrapping confidence intervals Exercise 5: Multiple imputation by chained equations Exercise 6: The mice flow: mice - with - pool Exercise 7: Choosing default models Exercise 8: Using predictor matrix Exercise 9: Putting it all together Exercise 10: Analyzing missing data patterns Exercise 11: Imputing and inspecting outcomes Exercise 12: Inference with imputed data Exercise 13: Final remarks