Meerdere variabelen voor autoprestaties plotten

Bij het plotten van GAMs wil je soms alleen naar delen van een model kijken, of juist naar alle termen in het model. Hier ga je oefenen met het selecteren van welke termen je visualiseert.

Deze oefening maakt deel uit van de cursus

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

Cursus bekijken

Oefeninstructies

Plot het meegeleverde model (mod) dat de mpg-gegevens gebruikt, en gebruik select om alleen het partiële effect van price te bekijken.
Maak nog een plot, dit keer met alle termen op één pagina, inclusief de lineaire term comp.ratio.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

library(mgcv)
# Fit the model
mod <- gam(hw.mpg ~ s(weight) + s(rpm) + s(price) + comp.ratio, 
           data = mpg, method = "REML")

# Plot the price effect
___

# Plot all effects
___

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: Introduction Exercise 2: Motorcycle crash data: linear approach Exercise 3: Motorcycle crash data: non-linear approach Exercise 4: Parts of non-linear function Exercise 5: Basis functions and smoothing Exercise 6: Setting complexity of the motorcycle model Exercise 7: Using smoothing parameters to avoid overfitting Exercise 8: Complexity and smoothing together Exercise 9: Multivariate GAMs Exercise 10: Multivariate GAMs of auto performance Exercise 11: Adding categorical to the auto performance model Exercise 12: Category-level smooths for different auto types

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: GAM-resultaten interpreteren Exercise 2: Significantie en lineariteit (I)Exercise 3: Significantie en lineariteit (II)Exercise 4: GAM's visualiseren Exercise 5: Het motorongelukmodel en de gegevens plotten Exercise 6: Meerdere variabelen voor autoprestaties plotten

Huidige oefening

Exercise 7: Onzekerheid in auto-prestaties visualiseren Exercise 8: Modelcontrole met gam.check()Exercise 9: Diagnoses van modellen lezen Exercise 10: Problemen oplossen met modeldiagnostiek Exercise 11: Concurviteit controleren Exercise 12: De algemene concurviteit in autogegevens onderzoeken Exercise 13: Concurviteit tussen auto-variabelen onderzoeken

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