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"page": "ADABOOST", "title": "Classification using AdaBoost", "topics": [ "ADABOOST" ] }, { "page": "alcohol", "title": "Alcohol dataset", "topics": [ "alcohol" ] }, { "page": "APRIORI", "title": "Classification using APRIORI", "topics": [ "APRIORI" ] }, { "page": "apriori-class", "title": "APRIORI classification model", "topics": [ "apriori-class" ] }, { "page": "augmentation", "title": "Duplicate and add noise to a dataset", "topics": [ "augmentation" ] }, { "page": "autompg", "title": "Auto MPG dataset", "topics": [ "autompg" ] }, { "page": "BAGGING", "title": "Classification using Bagging", "topics": [ "BAGGING" ] }, { "page": "beetles", "title": "Flea beetles dataset", "topics": [ "beetles" ] }, { "page": "birth", "title": "Birth dataset", "topics": [ "birth" ] }, { "page": "boosting-class", "title": "Boosting methods model", "topics": [ "boosting-class" ] }, { "page": "boxclus", "title": "Clustering Box Plots", "topics": [ "boxclus" ] }, { "page": "britpop", "title": "Population and location of 18 major british cities.", "topics": [ "britpop" ] }, { "page": "CA", "title": "Correspondence Analysis (CA)", "topics": [ "CA" ] }, { "page": "capitals", "title": "Capitals dataset", "topics": [ "capitals" ] }, { "page": "CART", "title": "Classification using CART", "topics": [ "CART" ] }, { "page": "cartdepth", "title": "Depth", "topics": [ "cartdepth" ] }, { "page": "cartinfo", "title": "CART information", "topics": [ "cartinfo" ] }, { "page": "cartleafs", "title": "Number of Leafs", "topics": [ "cartleafs" ] }, { "page": "cartnodes", "title": "Number of Nodes", "topics": [ "cartnodes" ] }, { "page": "cartplot", "title": "CART Plot", "topics": [ "cartplot" ] }, { "page": "CDA", "title": "Classification using Canonical Discriminant Analysis", "topics": [ "CDA" ] }, { "page": "cda-class", "title": "Canonical Disciminant Analysis model", "topics": [ "cda-class" ] }, { "page": "closegraphics", "title": "Close a graphics device", "topics": [ "closegraphics" ] }, { "page": "compare", "title": "Comparison of two sets of clusters", "topics": [ "compare" ] }, { "page": "compare.accuracy", "title": "Comparison of two sets of clusters, using accuracy", "topics": [ "compare.accuracy" ] }, { "page": "compare.jaccard", "title": "Comparison of two sets of clusters, using Jaccard index", "topics": [ "compare.jaccard" ] }, { "page": "compare.kappa", "title": "Comparison of two sets of clusters, using kappa", "topics": [ "compare.kappa" ] }, { "page": "confusion", "title": "Confusion matrix", "topics": [ "confusion" ] }, { "page": "cookies", "title": "Cookies dataset", "topics": [ "cookies", "cookies.desc.test", "cookies.desc.train", "cookies.y.test", "cookies.y.train" ] }, { "page": "cookplot", "title": "Plot the Cook's distance of a linear regression model", "topics": [ "cookplot" ] }, { "page": "correlated", "title": "Correlated variables", "topics": [ "correlated" ] }, { "page": "cost.curves", "title": "Plot Cost Curves", "topics": [ "cost.curves" ] }, { "page": "credit", "title": "Credit dataset", "topics": [ "credit" ] }, { "page": "data.diag", "title": "Square dataset", "topics": [ "data.diag" ] }, { "page": "data.gauss", "title": "Gaussian mixture dataset", "topics": [ "data.gauss" ] }, { "page": "data.parabol", "title": "Parabol dataset", "topics": [ "data.parabol" ] }, { "page": "data.target1", "title": "Target1 dataset", "topics": [ "data.target1" ] }, { "page": "data.target2", "title": "Target2 dataset", "topics": [ "data.target2" ] }, { "page": "data.twomoons", "title": "Two moons dataset", "topics": [ "data.twomoons" ] }, { "page": "data.xor", "title": "XOR dataset", "topics": [ "data.xor" ] }, { "page": "data1", "title": "\"data1\" dataset", "topics": [ "data1" ] }, { "page": "data2", "title": "\"data2\" dataset", "topics": [ "data2" ] }, { "page": "data3", "title": "\"data3\" dataset", "topics": [ "data3" ] }, { "page": "dataset-class", "title": "Training set and test set", "topics": [ "dataset-class" ] }, { "page": "dbs-class", "title": "DBSCAN model", "topics": [ "dbs-class" ] }, { "page": "DBSCAN", "title": "DBSCAN clustering method", "topics": [ "DBSCAN" ] }, { "page": "decathlon", "title": "Decathlon dataset", "topics": [ "decathlon" ] }, { "page": "distplot", "title": "Plot a k-distance graphic", "topics": [ "distplot" ] }, { "page": "EM", "title": "Expectation-Maximization clustering method", "topics": [ "EM" ] }, { "page": "em-class", "title": "Expectation-Maximization model", "topics": [ "em-class" ] }, { "page": "eucalyptus", "title": "Eucalyptus dataset", "topics": [ "eucalyptus" ] }, { "page": "evaluation", "title": "Evaluation of classification or regression predictions", "topics": [ "evaluation" ] }, { "page": "evaluation.accuracy", "title": "Accuracy of classification predictions", "topics": [ "evaluation.accuracy" ] }, { "page": "evaluation.adjr2", "title": "Adjusted R2 evaluation of regression predictions", "topics": [ "evaluation.adjr2" ] }, { "page": "evaluation.fmeasure", "title": "F-measure", "topics": [ "evaluation.fmeasure" ] }, { "page": "evaluation.fowlkesmallows", "title": "Fowlkes–Mallows index", "topics": [ "evaluation.fowlkesmallows" ] }, { "page": "evaluation.goodness", "title": "Goodness", "topics": [ "evaluation.goodness" ] }, { "page": "evaluation.jaccard", "title": "Jaccard index", "topics": [ "evaluation.jaccard" ] }, { "page": "evaluation.kappa", "title": "Kappa evaluation of classification predictions", "topics": [ "evaluation.kappa" ] }, { "page": "evaluation.msep", "title": "MSEP evaluation of regression predictions", "topics": [ "evaluation.msep" ] }, { "page": "evaluation.precision", "title": "Precision of classification predictions", "topics": [ "evaluation.precision" ] }, { "page": "evaluation.r2", "title": "R2 evaluation of regression predictions", "topics": [ "evaluation.r2" ] }, { "page": "evaluation.recall", "title": "Recall of classification predictions", "topics": [ "evaluation.recall" ] }, { "page": "exportgraphics", "title": "Open a graphics device", "topics": [ "exportgraphics" ] }, { "page": "toggleexport", "title": "Toggle graphic exports", "topics": [ "exportgraphics.off", "exportgraphics.on", "toggleexport", "toggleexport.off", "toggleexport.on" ] }, { "page": "factorial-class", "title": "Factorial analysis results", "topics": [ "factorial-class" ] }, { "page": "FEATURESELECTION", "title": "Classification with Feature selection", "topics": [ "FEATURESELECTION" ] }, { "page": "filter.rules", "title": "Filtering a set of rules", "topics": [ "filter.rules" ] }, { "page": "frequentwords", "title": "Frequent words", "topics": [ "frequentwords" ] }, { "page": "GBREG", "title": "Regression using Gradient Boosting", "topics": [ "GBREG" ] }, { "page": "general.rules", "title": "Remove redundancy in a set of rules", "topics": [ "general.rules" ] }, { "page": "getvocab", "title": "Extract words and phrases from a corpus", "topics": [ "getvocab" ] }, { "page": "GRADIENTBOOSTING", "title": "Classification using Gradient Boosting", "topics": [ "GRADIENTBOOSTING" ] }, { "page": "HCA", "title": "Hierarchical Cluster Analysis method", "topics": [ "HCA" ] }, { "page": "intern", "title": "Clustering evaluation through internal criteria", "topics": [ "intern" ] }, { "page": "intern.dunn", "title": "Clustering evaluation through Dunn's index", "topics": [ "intern.dunn" ] }, { "page": "intern.interclass", "title": "Clustering evaluation through interclass inertia", "topics": [ "intern.interclass" ] }, { "page": "intern.intraclass", "title": "Clustering evaluation through intraclass inertia", "topics": [ "intern.intraclass" ] }, { "page": "ionosphere", "title": "Ionosphere dataset", "topics": [ "ionosphere" ] }, { "page": "kaiser", "title": "Kaiser rule", "topics": [ "kaiser" ] }, { "page": "KERREG", "title": "Kernel Regression", "topics": [ "KERREG" ] }, { "page": "KMEANS", "title": "K-means method", "topics": [ "KMEANS" ] }, { "page": "kmeans.getk", "title": "Estimation of the number of clusters for _K_-means", "topics": [ "kmeans.getk" ] }, { "page": "KNN", "title": "Classification using k-NN", "topics": [ "KNN" ] }, { "page": "knn-class", "title": "K Nearest Neighbours model", "topics": [ "knn-class" ] }, { "page": "LDA", "title": "Classification using Linear Discriminant Analysis", "topics": [ "LDA" ] }, { "page": "leverageplot", "title": "Plot the leverage points of a linear regression model", "topics": [ "leverageplot" ] }, { "page": "LINREG", "title": "Linear Regression", "topics": [ "LINREG" ] }, { "page": "linsep", "title": "Linsep dataset", "topics": [ "linsep" ] }, { "page": "loadtext", "title": "load a text file", "topics": [ "loadtext" ] }, { "page": "LR", "title": "Classification using Logistic Regression", "topics": [ "LR" ] }, { "page": "MCA", "title": "Multiple Correspondence Analysis (MCA)", "topics": [ "MCA" ] }, { "page": "MEANSHIFT", "title": "MeanShift method", "topics": [ "MEANSHIFT" ] }, { "page": "meanshift-class", "title": "MeanShift model", "topics": [ "meanshift-class" ] }, { "page": "MLP", "title": "Classification using Multilayer Perceptron", "topics": [ "MLP" ] }, { "page": "MLPREG", "title": "Multi-Layer Perceptron Regression", "topics": [ "MLPREG" ] }, { "page": "model-class", "title": "Generic classification or regression model", "topics": [ "model-class" ] }, { "page": "model-methods", "title": "Residuals and fitted values of a model", "topics": [ "fitted.model", "model-methods", "residuals.model", "rstudent.model" ] }, { "page": "movies", "title": "Movies dataset", "topics": [ "movies" ] }, { "page": "NB", "title": "Classification using Naive Bayes", "topics": [ "NB" ] }, { "page": "NMF", "title": "Non-negative Matrix Factorization", "topics": [ "NMF" ] }, { "page": "ozone", "title": "Ozone dataset", "topics": [ "ozone" ] }, { "page": "PAM", "title": "Clustering using _K_-medoids (PAM)", "topics": [ "PAM" ] }, { "page": "params-class", "title": "Learning Parameters", "topics": [ "params-class" ] }, { "page": "PCA", "title": "Principal Component Analysis (PCA)", "topics": [ "PCA" ] }, { "page": "performance", "title": "Performance estimation", "topics": [ "performance" ] }, { "page": "plot.apriori", "title": "Plot function for apriori-class", "topics": [ "plot.apriori" ] }, { "page": "plot.cda", "title": "Plot function for cda-class", "topics": [ "plot.cda" ] }, { "page": "plot.factorial", "title": "Plot function for factorial-class", "topics": [ "plot.factorial" ] }, { "page": "plot.hca", "title": "Plot function for hca-class", "topics": [ "plot.hca" ] }, { "page": "plot.selection", "title": "Plot a feature selection", "topics": [ "plot.selection" ] }, { "page": "plot.som", "title": "Plot function for som-class", "topics": [ "plot.som" ] }, { "page": "plotavsp", "title": "Plot actual vs. predictions", "topics": [ "plotavsp" ] }, { "page": "plotcloud", "title": "Plot word cloud", "topics": [ "plotcloud" ] }, { "page": "plotclus", "title": "Generic Plot Method for Clustering", "topics": [ "plotclus" ] }, { "page": "plotdata", "title": "Advanced plot function", "topics": [ "plotdata" ] }, { "page": "plotzipf", "title": "Plot rank versus frequency", "topics": [ "plotzipf" ] }, { "page": "POLYREG", "title": "Polynomial Regression", "topics": [ "POLYREG" ] }, { "page": "predict.apriori", "title": "Model predictions", "topics": [ "predict.apriori" ] }, { "page": "predict.boosting", "title": "Model predictions", "topics": [ "predict.boosting" ] }, { "page": "predict.cda", "title": "Model predictions", "topics": [ "predict.cda" ] }, { "page": "predict.dbs", "title": "Predict function for DBSCAN", "topics": [ "predict.dbs" ] }, { "page": "predict.em", "title": "Predict function for EM", "topics": [ "predict.em" ] }, { "page": "predict.factorial", "title": "Projection of new observations into a factorial space", "topics": [ "predict.factorial" ] }, { "page": "predict.hca", "title": "Predict function for hierarchical clustering", "topics": [ "predict.hca" ] }, { "page": "predict.kmeans", "title": "Predict function for K-means", "topics": [ "predict.kmeans" ] }, { "page": "predict.knn", "title": "Model predictions", "topics": [ "predict.knn" ] }, { "page": "predict.meanshift", "title": "Predict function for MeanShift", "topics": [ "predict.meanshift" ] }, { "page": "predict.model", "title": "Model predictions", "topics": [ "predict.model" ] }, { "page": "predict.pam", "title": "Predict function for PAM", "topics": [ "predict.pam" ] }, { "page": "predict.selection", "title": "Model predictions", "topics": [ "predict.selection" ] }, { "page": "predict.som", "title": "Predict function for a self-organising map", "topics": [ "predict.som" ] }, { "page": "predict.spectral", "title": "Predict function for Spectral clustering", "topics": [ "predict.spectral" ] }, { "page": "predict.textmining", "title": "Model predictions", "topics": [ "predict.textmining" ] }, { "page": "print.apriori", "title": "Print a classification model obtained by APRIORI", "topics": [ "print.apriori" ] }, { "page": "print.boosting", "title": "Print an ensemble model", "topics": [ "print.boosting" ] }, { "page": "print.cda", "title": "Print a canonical discriminant analysis", "topics": [ "print.cda" ] }, { "page": "print.dataset", "title": "Print a training/test split", "topics": [ "print.dataset" ] }, { "page": "print.dbs", "title": "Print a DBSCAN clustering", "topics": [ "print.dbs" ] }, { "page": "print.em", "title": "Print an EM clustering", "topics": [ "print.em" ] }, { "page": "print.factorial", "title": "Plot function for factorial-class", "topics": [ "print.factorial" ] }, { "page": "print.knn", "title": "Print a _K_-nearest-neighbours model", "topics": [ "print.knn" ] }, { "page": "print.meanshift", "title": "Print a mean shift clustering", "topics": [ "print.meanshift" ] }, { "page": "print.model", "title": "Print a classification or regression model", "topics": [ "print.model" ] }, { "page": "print.params", "title": "Print tuned method parameters", "topics": [ "print.params" ] }, { "page": "print.selection", "title": "Print a feature selection result", "topics": [ "print.selection" ] }, { "page": "print.som", "title": "Print a self-organising map", "topics": [ "print.som" ] }, { "page": "print.spectral", "title": "Print a spectral clustering", "topics": [ "print.spectral" ] }, { "page": "pseudoF", "title": "Pseudo-F", "topics": [ "pseudoF" ] }, { "page": "QDA", "title": "Classification using Quadratic Discriminant Analysis", "topics": [ "QDA" ] }, { "page": "query.docs", "title": "Document query", "topics": [ "query.docs" ] }, { "page": "query.words", "title": "Word query", "topics": [ "query.words" ] }, { "page": "RANDOMFOREST", "title": "Classification using Random Forest", "topics": [ "RANDOMFOREST" ] }, { "page": "reg1", "title": "reg1 dataset", "topics": [ "reg1", "reg1.test", "reg1.train" ] }, { "page": "reg2", "title": "reg2 dataset", "topics": [ "reg2", "reg2.test", "reg2.train" ] }, { "page": "regplot", "title": "Plot function for a regression model", "topics": [ "regplot" ] }, { "page": "resplot", "title": "Plot the studentized residuals of a linear regression model", "topics": [ "resplot" ] }, { "page": "roc.curves", "title": "Plot ROC Curves", "topics": [ "roc.curves" ] }, { "page": "rotation", "title": "Rotation", "topics": [ "rotation" ] }, { "page": "runningtime", "title": "Running time", "topics": [ "runningtime" ] }, { "page": "scatterplot", "title": "Clustering Scatter Plots", "topics": [ "scatterplot" ] }, { "page": "selectfeatures", "title": "Feature selection for classification", "topics": [ "selectfeatures" ] }, { "page": "selection-class", "title": "Feature selection", "topics": [ "selection-class" ] }, { "page": "snore", "title": "Snore dataset", "topics": [ "snore" ] }, { "page": "SOM", "title": "Self-Organizing Maps clustering method", "topics": [ "SOM" ] }, { "page": "som-class", "title": "Self-Organizing Maps model", "topics": [ "som-class" ] }, { "page": "SPECTRAL", "title": "Spectral clustering method", "topics": [ "SPECTRAL" ] }, { "page": "spectral-class", "title": "Spectral clustering model", "topics": [ "spectral-class" ] }, { "page": "spine", "title": "Spine dataset", "topics": [ "spine", "spine.test", "spine.train" ] }, { "page": "splitdata", "title": "Splits a dataset into training set and test set", "topics": [ "splitdata" ] }, { "page": "stability", "title": "Clustering evaluation through stability", "topics": [ "stability" ] }, { "page": "STUMP", "title": "Classification using one-level decision tree", "topics": [ "STUMP" ] }, { "page": "summary.apriori", "title": "Print summary of a classification model obtained by APRIORI", "topics": [ "summary.apriori" ] }, { "page": "summary.model", "title": "Summary of a classification or regression model", "topics": [ "summary.model" ] }, { "page": "SVD", "title": "Singular Value Decomposition", "topics": [ "SVD" ] }, { "page": "SVM", "title": "Classification using Support Vector Machine", "topics": [ "SVM" ] }, { "page": "SVMl", "title": "Classification using Support Vector Machine with a linear kernel", "topics": [ "SVMl" ] }, { "page": "SVMr", "title": "Classification using Support Vector Machine with a radial kernel", "topics": [ "SVMr" ] }, { "page": "SVR", "title": "Regression using Support Vector Machine", "topics": [ "SVR" ] }, { "page": "SVRl", "title": "Regression using Support Vector Machine with a linear kernel", "topics": [ "SVRl" ] }, { "page": "SVRr", "title": "Regression using Support Vector Machine with a radial kernel", "topics": [ "SVRr" ] }, { "page": "temperature", "title": "Temperature dataset", "topics": [ "temperature" ] }, { "page": "TEXTMINING", "title": "Text mining", "topics": [ "TEXTMINING" ] }, { "page": "textmining-class", "title": "Text mining object", "topics": [ "textmining-class" ] }, { "page": "titanic", "title": "Titanic dataset", "topics": [ "titanic" ] }, { "page": "treeplot", "title": "Dendrogram Plots", "topics": [ "treeplot" ] }, { "page": "TSNE", "title": "t-distributed Stochastic Neighbor Embedding", "topics": [ "TSNE" ] }, { "page": "universite", "title": "University dataset", "topics": [ "universite" ] }, { "page": "vectorize.docs", "title": "Document vectorization", "topics": [ "vectorize.docs" ] }, { "page": "vectorize.words", "title": "Word vectorization", "topics": [ "vectorize.words" ] }, { "page": "vectorizer-class", "title": "Document vectorization object", "topics": [ "vectorizer-class" ] }, { "page": "vowels", "title": "Vowels dataset", "topics": [ "vowels", "vowels.test", "vowels.train" ] }, { "page": "wheat", "title": "Wheat dataset", "topics": [ "wheat" ] }, { "page": "wine", "title": "Wine dataset", "topics": [ "wine" ] }, { "page": "zoo", "title": "Zoo dataset", "topics": [ "zoo" ] } ], "_rundeps": [ "abind", "arules", "arulesViz", "askpass", "backports", "base64enc", "boot", "broom", "bslib", "ca", "cachem", "car", "carData", "cli", "cluster", "codetools", "colorspace", "cowplot", "cpp11", "crosstalk", "curl", "data.table", "Deriv", "digest", "doBy", "dplyr", "DT", "ellipse", "emmeans", "estimability", "evaluate", "FactoMineR", "farver", "fastmap", "flashClust", "fontawesome", "foreach", "forecast", "Formula", "fracdiff", "fs", "gclus", "generics", "ggforce", "ggplot2", "ggraph", "ggrepel", "glue", "graphlayouts", "gridExtra", "gtable", "highr", "htmltools", "htmlwidgets", "httr", "igraph", "irlba", "isoband", "iterators", "jquerylib", "jsonlite", "knitr", "labeling", "later", "lattice", "lazyeval", "leaps", "lifecycle", "lme4", "lmtest", "magrittr", "MASS", "Matrix", "MatrixModels", "mclust", "memoise", "mgcv", "mime", "minqa", "modelr", "multcompView", "mvtnorm", "nlme", "nloptr", "nnet", "numDeriv", "openssl", "otel", "pbkrtest", "pillar", "pkgconfig", "plotly", "pls", "polyclip", "promises", "purrr", "qap", "quantreg", "R6", "rappdirs", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "Rdpack", "reformulas", "registry", "rlang", "rmarkdown", "S7", "sass", "scales", "scatterplot3d", "seriation", "showtext", "showtextdb", "SparseM", "stringi", "stringr", "survival", "sys", "sysfonts", "systemfonts", "tibble", "tidygraph", "tidyr", "tidyselect", "timeDate", "tinytex", "TSP", "tweenr", "urca", "utf8", "vcd", "vctrs", "viridis", "viridisLite", "visNetwork", "withr", "xfun", "yaml", "zoo" ], "_vignettes": [ { "source": "v07-clustering-earthquakes.Rmd", "filename": "v07-clustering-earthquakes.html", "title": "Clustering: earthquakes in Fiji", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Which method produces the most stable clusters?", "Question 2. How do the clusters read in depth and magnitude?", "Question 3. Where are the shallowest earthquakes? The deepest?", "Question 4. How many clusters are there really?", "Question 5. Which methods give the most relevant clusters?" ], "created": "2026-08-28 18:53:53", "modified": "2026-10-05 22:46:47", "commits": 3 }, { "source": "v04-clustering-wheat-varieties.Rmd", "filename": "v04-clustering-wheat-varieties.html", "title": "Clustering: wheat varieties", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Raw data, or centred and scaled?", "Question 2. How many clusters do the methods see?", "Question 3. With three clusters, which method is the most compact? The most stable?", "Question 4. Which method comes closest to the three varieties?" ], "created": "2026-08-28 18:53:53", "modified": "2026-09-04 18:29:47", "commits": 2 }, { "source": "v02-discriminant-analysis-spine.Rmd", "filename": "v02-discriminant-analysis-spine.html", "title": "Discriminant analysis: the lumbar spine", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Which method predicts the two-class problem best?", "Question 2. And the three-class problem?", "Question 3. Is the three-class problem harder than the two-class one?", "Question 4. Which classes are hardest to separate?", "Going further: are non-linear methods worth it?" ], "created": "2026-08-28 18:53:53", "modified": "2026-09-04 18:29:47", "commits": 2 }, { "source": "v06-ensemble-methods-ionosphere.Rmd", "filename": "v06-ensemble-methods-ionosphere.html", "title": "Ensemble methods: the ionosphere", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Which method predicts best?", "Question 2. How do the two best differ, in false positives and false negatives?" ], "created": "2026-08-28 18:53:53", "modified": "2026-10-05 22:46:47", "commits": 3 }, { "source": "v08-feature-selection-crabs.Rmd", "filename": "v08-feature-selection-crabs.html", "title": "Feature selection: crabs", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Selecting for the species", "Question 2. Selecting for the sex", "Question 3. Which variables, in the end?" ], "created": "2026-08-28 18:53:53", "modified": "2026-10-05 22:46:47", "commits": 3 }, { "source": "v03-linear-regression-hill-races.Rmd", "filename": "v03-linear-regression-hill-races.html", "title": "Linear regression: Scottish hill races", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Which single variable predicts best? Was it foreseeable?", "Question 2. One variable or two?", "Question 3. What do the residuals say?", "Question 4. Can the predictions be improved?" ], "created": "2026-08-28 18:53:53", "modified": "2026-10-05 22:46:47", "commits": 3 }, { "source": "v01-pca-breast-tumours.Rmd", "filename": "v01-pca-breast-tumours.html", "title": "Principal component analysis: breast tumours", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. How many factorial axes are needed?", "Question 2. How should the first two axes be read?", "Question 3. What can be said about the two groups of tumours?" ], "created": "2026-08-28 18:53:53", "modified": "2026-09-04 18:29:47", "commits": 2 }, { "source": "v05-visualisation-cars.Rmd", "filename": "v05-visualisation-cars.html", "title": "Visualisation: cars from three continents", "engine": "knitr::rmarkdown", "headings": [ "The data", "Question 1. Should the data be centred and scaled?", "Question 2. 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