DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Residuals Plot

A plot of an lm (linear model) object. This provides a clear picture of any strange behaviour from the residuals.

[width=0.9]rplot-lm-residuals

pdf("graphics/rplot-lm-residuals.pdf")
  x <- runif(100, -3, 3)
  y <- 1 + 2*x + rnorm(100)
  m <- lm(y~x)
  par(mfrow=c(2,2))
  plot(m)
dev.off()

http://rattle.togaware.com/code/rplot-lm-residuals.R

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