Philippe J. S. De Brouwer - The Big R-Book

16 Chapter 23Figure 23.1: An example of the decision tree on fake data a represented in t ...Figure 23.2: Three alternatives for the impurity measure in the case of clas ...Figure 23.3: The plot of the complexity parameter (cp) via the function plot ...Figure 23.4: The decision tree, fitted by rpart. This figure helps to visual ...Figure 23.5: The same tree as in Figure 23.4 but now pruned with a complexit ...Figure 23.6: The decision tree represented by the function prp() from the pa ...Figure 23.7: The plot of the complexity parameter (cp) via the function plot ...Figure 23.8: rpart tree on mpg for the dataset mtcars. Figure 23.9: The same tree as in Figure 23.8 but now pruned with a complexit ...Figure 23.10: ROC curve of the decision tree. Figure 23.11: The accuracy for the decision tree on the Titanic data. Figure 23.12: The plot of a randomForest object shows how the model improves ...Figure 23.13: The importance of each variable in the random-forest model. Figure 23.14: Partial dependence on the variables (1 of 3). Figure 23.15: Partial dependence on the variables (2 of 3). Figure 23.16: Partial dependence on the variables (3 of 3). Figure 23.17: A logistic regression is actually a neural network with one ne ...Figure 23.18: A simple neural net fitted to the dataset of mtcars, predictin ...Figure 23.19: A visualisation of the ANN. Note that we left out the weights, ...Figure 23.20: A visualisation of the performance of the ANN (left) compared ...Figure 23.21: Avisualisation of the performance of theANNcompared to the lin ...Figure 23.22: A boxplot for the MSE of the cross validation for the ANN. Figure 23.23: The cars in the dataset mtcars with fuel consumption plotted i ...Figure 23.24: The result of k-means clustering with three clusters on the we ...Figure 23.25: The plot() function applied on a prcomp object visualises the ...Figure 23.26: The custom function biplot() project all data in the plane tha ...Figure 23.27: A projection in the plane of the two major principal component ...Figure 23.28: The projection of mtcars in the surface formed by the two firs ...Figure 23.29: Two dimensional projections of the dependency structure of the ...Figure 23.30: A three dimensional plot of the cars with on the z-axis the fi ...Figure 23.31: plotly will produce a graph that is not only 3D but is interac ...Figure 23.32: A plot with autoplot(), enhanced with ggrepel of the fuzzy clu ...Figure 23.33: A hierarchical cluster for the dataset mtcars.

17 Chapter 24Figure 24.1: The results of the bootstrap exercise: a set of estimates for ...

18 Chapter 25Figure 25.1: A spacing grid for the predictions of t mpg . Figure 25.2: Bootstrapping the returns of the S&P500 index . Figure 25.3: The histograms of the different coefficients of the linear reg ...Figure 25.4: The histogram of the RMSE for a Monte Carlo cross validation o ...Figure 25.5: Histogramof the RMSE based on a 5-fold cross validation. The h ...Figure 25.6: The life cycle of a model: a model is an integrated part of bu ...

19 Chapter 26Figure 26.1: Demonstration of the barChart() function of the package quantm ...Figure 26.2: Demonstration of the lineChart() function of the package quand ...Figure 26.3: Demonstration of the candleChart() function of the package qua ...Figure 26.4: Bollinger bands with the package quandmod . Figure 26.5: The evolution of the HSBC share for the last ten years ....Figure 26.6: The Q-Q plot of our naive model to forecast the next opening p ...

20 Chapter 27Figure 27.1: A visualization of the dominance relationship .Figure 27.2: The scores of different cities according to the WSM .Figure 27.3: The preference structure as found by the ELECTRE I method give ...Figure 27.4: Another representation of Figure 27.3 . It is clear that Krakow ...Figure 27.5: The results of ELECTRE I with comparability index C 2 and param ...Figure 27.6: The results for ELECTRE Iwith comparability indexC 2. The A → B ...Figure 27.7: The preference structure as found by the ELECTRE II method giv ...Figure 27.8: The results for ELECTRE I with comparability index C 2.Figure 27.9: Examples of smooth transition schemes for preference functions ...Figure 27.10: Examples of practically applicable preferences functions P ( d )...Figure 27.11: The hierarchy between alternatives as found by PROMethEE I .Figure 27.12: The preference relations resulting from PROMethEE I. For exam ...Figure 27.13: The result for PROMethEE I with different preference function ...Figure 27.14: The results for PROMethEE I method with the custom preference ...Figure 27.15: Promethee II can also be seen as using a richer preference st ...Figure 27.16: The hierarchy between alternatives as found by PROMethEE II. ...Figure 27.17: PROMethEE II provides a full ranking. Here we show how much e ...Figure 27.18: The variance explained by each principal component .Figure 27.19: A projection of the space of alternatives in the 2D‐plane for ...Figure 27.20: A standard plot with autoplot() with labels coloured Figure 27.21: Autoplot with visualization of two clusters Figure 27.22: Clustering with elliptoid borders, labels of alternative, pro ...

21 Chapter 28Figure 28.1: The elements of wealth creation in a company. The company acqu ...Figure 28.2: KPIs of the Value Chain that can be used by a manager who want ...

22 Chapter 30Figure 30.1: The Epachenikov kernel (left), for h = 1; and the Gaussian ker ...Figure 30.2: As illustration on how the Epachenikov Kernel Estimation works ...Figure 30.3: Some concepts illustrated on the example of a call option with ...Figure 30.4: The intrinsic value of a long call illustratedwith its payoff ...Figure 30.5: The intrinsic value of a short call illustrated with its payof ...Figure 30.6: The payoff and profit for a long put (left) and a short put (r ...Figure 30.7: The price of a long call compared to its intrinsic value. The ...Figure 30.8: The price of a long put compared to its intrinsic value. Note ...Figure 30.9: Step 1 in the binomial model . Figure 30.10: The first 2 steps of the binomial model . Figure 30.11: The Cox–Ross–Rubinsteinmodel for the binomialmodel applied to ...Figure 30.12: he Cox–Ross–Rubinsteinmodel for the binomialmodel applied to ...Figure 30.13: The value of a call option depends on many variables. Some ar ...Figure 30.14: The value of a put option depends on many variables. Some are ...Figure 30.15: An illustration of how the delta of a call and put compare in ...Figure 30.16: Linear option strategies illustrated. The red line is the int ...Figure 30.17: Linear option strategies illustrated. Part 2 (basic composite ...Figure 30.18: Linear option strategies illustrated. Part 3 (some more compl ...Figure 30.19: A covered call is a short call where the losses are protected ...Figure 30.20: A married put is a put option combined with the underlying as ...Figure 30.21: A collar is a structure that protects us from strong downward ...

23 Chapter 31Figure 31.1: A basic and simple scatter-plot generated with ggplot2. Figure 31.2: The same plot as in previous figure, but now enhanced with Loe ...Figure 31.3: The same plot as in previous Figure, but now enhanced with dif ...Figure 31.4: A facet plot will create sub-plots per discrete value of one o ...Figure 31.5: The standard functionality for scatterplots is not optimal for ...Figure 31.6: The contour plot is able to show where the density of points i ...Figure 31.7: Adding a Loess estimate is a good idea to visualize the genera ...Figure 31.8: This plot shows a facet plot of a contour plot with customised ...