broom gebruiken

Veel programmeertaken zijn makkelijker als je alle gegevens in data frames houdt. Dat geldt extra als je met de tidyverse werkt, waar dplyr en ggplot2 data frames verwachten. Het pakket broom bevat functies die modellen opsplitsen in drie data frames: één voor de element op coëfficiëntniveau (de coëfficiënten zelf en de p-waarden per coëfficiënt), de elementen op observatieniveau (zoals voorspelde waarden en residuen), en de elementen op modelniveau (voornamelijk prestatie-indicatoren).

De functies in broom zijn generiek. Ze werken dus met veel typen modellen, niet alleen met lineaire regressiemodellen. Ze werken ook met logistische regressiemodellen (zoals je in Hoofdstuk 4 zult zien) en met veel andere modeltypen.

mdl_price_vs_conv is beschikbaar en broom is geladen.

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Introductie tot regressie in R

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# Get the coefficient-level elements of the model
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Deze oefening maakt deel uit van de cursus

Introductie tot regressie in R

SkillTag.level.intermediateSkillTag.label

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You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Exercise 1: A tale of two variables Exercise 2: Which one is the response variable?Exercise 3: Visualizing two variables Exercise 4: Fitting a linear regression Exercise 5: Estimate the intercept Exercise 6: Estimate the slope Exercise 7: Linear regression with lm()Exercise 8: Categorical explanatory variables Exercise 9: Visualizing numeric vs. categorical Exercise 10: Calculating means by category Exercise 11: lm() with a categorical explanatory variable

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Exercise 1: Voorspellingen maken Exercise 2: Huisprijzen voorspellen Exercise 3: Voorspellingen visualiseren Exercise 4: De grenzen van voorspellen Exercise 5: Werken met modelobjecten Exercise 6: Modelonderdelen uitlezen Exercise 7: Handmatig huizenprijzen voorspellen Exercise 8: broom gebruiken

Huidige oefening

Exercise 9: Regressie naar het gemiddelde Exercise 10: Homerun!Exercise 11: Opeenvolgende portefeuillerendementen plotten Exercise 12: Opeenvolgende rendementen modelleren Exercise 13: Variabelen transformeren Exercise 14: De verklarende variabele transformeren Exercise 15: Ook de responsvariabele transformeren

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Exercise 1: Quantifying model fit Exercise 2: Coefficient of determination Exercise 3: Residual standard error Exercise 4: Visualizing model fit Exercise 5: Residuals vs. fitted values Exercise 6: Q-Q plot of residuals Exercise 7: Scale-location Exercise 8: Drawing diagnostic plots Exercise 9: Outliers, leverage, and influence Exercise 10: Leverage Exercise 11: Influence Exercise 12: Extracting leverage and influence

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Exercise 1: Why you need logistic regression Exercise 2: Exploring the explanatory variables Exercise 3: Visualizing linear and logistic models Exercise 4: Logistic regression with glm()Exercise 5: Predictions and odds ratios Exercise 6: Probabilities Exercise 7: Most likely outcome Exercise 8: Odds ratio Exercise 9: Log odds ratio Exercise 10: Quantifying logistic regression fit Exercise 11: Calculating the confusion matrix Exercise 12: Measuring logistic model performance Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Congratulations