Motorrijongeluk-data: lineaire aanpak

In deze eerste oefening fit je een lineair model op een gegevensset en visualiseer je de resultaten om te zien hoe goed het de relaties in de data vastlegt. De gegevensset, opgeslagen in een data frame met de naam mcycle, bevat metingen van de versnelling van het hoofd van een crash-testdummy tijdens een motorrijongeluk. De kolom accel bevat metingen van de versnelling (g) en de kolom times bevat de tijd (milliseconden).

Deze oefening maakt deel uit van de cursus

Niet-lineaire modellering met Generalized Additive Models (GAM's) in R

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Oefeninstructies

Gebruik de functies head() en plot() om naar het data frame mcycle te kijken.
Gebruik de functie lm() om een model te fitten op de mcycle-data waarbij de variabele accel een lineaire functie is van times.
Visualiseer de modelfit met de meegeleverde aanroep van termplot().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Examine the mcycle data frame
head(___)
plot(___)

# Fit a linear model
lm_mod <- lm(___, data = mcycle)

# Visualize the model
termplot(lm_mod, partial.resid = TRUE, se = TRUE)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Niet-lineaire modellering met Generalized Additive Models (GAM's) in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

In this chapter, you will learn how Generalized additive models work and how to use flexible, nonlinear functions to model data without over-fitting. You will learn to use the gam() function in the mgcv package, and how to build multivariate models that mix nonlinear, linear, and categorical effects to data.

Exercise 1: Introductie Exercise 2: Motorrijongeluk-data: lineaire aanpak

Huidige oefening

Exercise 3: Motorongelukgegevens: niet-lineaire aanpak Exercise 4: Onderdelen van een niet-lineaire functie Exercise 5: Basisfuncties en smoothing Exercise 6: Complexiteit van het motorfietsmodel instellen Exercise 7: Smoothingparameters gebruiken om overfitting te voorkomen Exercise 8: Complexiteit en smoothing samen Exercise 9: Multivariate GAM's Exercise 10: Multivariate GAM's voor autoperformance Exercise 11: Categorische variabelen toevoegen aan het model voor autoprestaties Exercise 12: Categoriegebonden smooths voor verschillende autotypes

In this chapter, you will take a closer look at the models you fit in chapter 1 and learn how to interpret and explain them. You will learn how to make plots that show how different variables affect model outcomes. Then you will diagnose problems in models arising from under-fitting the data or hidden relationships between variables, and how to iteratively fix those problems and get better results.

Exercise 1: Interpreting GAM outputs Exercise 2: Significance and linearity (I)Exercise 3: Significance and linearity (II)Exercise 4: Visualizing GAMs Exercise 5: Plotting the motorcycle crash model and data Exercise 6: Plotting multiple auto performance variables Exercise 7: Visualizing auto performance uncertainty Exercise 8: Model checking with gam.check()Exercise 9: Reading model diagnostics Exercise 10: Fixing problems with model diagnostics Exercise 11: Checking concurvity Exercise 12: Examining overall concurvity in auto data Exercise 13: Examining concurvity between auto variables

In this chapter, you will extend the types of models you can fit to those with interactions of multiple variables. You will fit models of geospatial data by using these interactions to model complex surfaces, and visualize those surfaces in 3D. Then you will learn about interactions between smooth and categorical variables, and how to model interactions between very different variables like space and time.

Exercise 1: Two-dimensional smooths and spatial data Exercise 2: Modeling soil pollution in the Netherlands Exercise 3: Adding more variables to predict soil pollution Exercise 4: Plotting and interpreting GAM interactions Exercise 5: Plotting the model surface Exercise 6: Customizing 3D plots Exercise 7: Extrapolation in surface plots Exercise 8: Visualizing categorical-continuous interactions Exercise 9: Soil pollution in different land uses Exercise 10: Plotting pollution in different land uses Exercise 11: Interactions with different scales: Tensors Exercise 12: Pollution models with multi-scale interactions Exercise 13: Teasing apart multi-scale interactions

In the first three chapters, you used GAMs for regression of continuous outcomes. In this chapter, you will use GAMs for classification. You will build logistic GAMs to predict binary outcomes like customer purchasing behavior, learn to visualize this new type of model, make predictions, and learn how to explain the variables that influence each prediction.

Exercise 1: Types of model outcomes Exercise 2: Classifying purchasing behavior Exercise 3: Purchase behavior with multiple smooths Exercise 4: Visualizing logistic GAMs Exercise 5: Visualizing influences on purchase probability Exercise 6: Interpreting purchase effect plots (I)Exercise 7: Interpreting purchase effect plots (II)Exercise 8: Interpreting purchase effect plots (III)Exercise 9: Making predictions Exercise 10: Predicting purchase behavior and uncertainty Exercise 11: Explaining individual behaviors Exercise 12: Doing more with GAMs