Toprak kirliliğini tahmin etmek için daha fazla değişken ekleme

Şimdi mekânsal etkileşimleri olan modele ek öngörücüler ekleyelim.

Bu egzersiz

R ile Genelleştirilmiş Katkılı Modeller (GAM'ler) ile Doğrusal Olmayan Modelle

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Topraktaki kadmiyumu tahmin etmek için, bu kez x, y yüzeyine ek olarak yükseltinin (elev) ve nehre uzaklığın (dist) etkileri için smooth terimleri içeren başka bir model kur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Fit the model
mod2da <- gam(cadmium ~ s(x, y) ___, 
              data = meuse, method = "REML")

# Inspect the model
summary(mod2da)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Genelleştirilmiş Katkılı Modeller (GAM'ler) ile Doğrusal Olmayan Modelle

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: 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: İki boyutlu düzgünleştirmeler ve mekânsal veriler Exercise 2: Hollanda'da toprak kirliliğinin modellenmesi Exercise 3: Toprak kirliliğini tahmin etmek için daha fazla değişken ekleme

Geçerli Egzersiz

Exercise 4: GAM etkileşimlerini görselleştirme ve yorumlama Exercise 5: Model yüzeyini görselleştirme Exercise 6: 3B grafikleri özelleştirme Exercise 7: Yüzey grafiklerinde ekstrapolasyon Exercise 8: Kategorik-sürekli etkileşimleri görselleştirme Exercise 9: Farklı arazi kullanımlarında toprak kirliliği Exercise 10: Farklı arazi kullanımlarında kirliliği görselleştirme Exercise 11: Farklı ölçeklerle etkileşimler: Tensörler Exercise 12: Çok ölçekli etkileşimlerle kirlilik modelleri Exercise 13: Çok ölçekli etkileşimleri ayırma

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