Okay perhaps I should've been more clear about the data. Im actually working on spectroscopic measurements from food authenticity testing. I have five different types of meat: 55 of chicken, 55 of turkey, 55 of pork, 34 of beef and 32 of lamb - 231 in total. On each of these 231 meats, 1024 spectroscopic measurements were taken. Matrix of 231 by 1024. But the questions I want answered are which of the 1024 measurements are important for predicting meat type and which of the different types of meat are incorrectly classified - i.e can we tell the difference between chicken and turkey. So to carry out a multivariate analysis on the data Ive split it into two. A training data set and a test data set - half and half although I think the larger half (55 goes into 27 and 28) went into the test data set which explains the inequalities in the row numbers. By the way 1024 is standard - can't change that. Can't change the 231 either.
So I created a new row with the meat types for each row. End up with the following R code: library(tree) meat.tree <- tree(meat.type~., data=train) using tree.cv (or cv.tree) lowest missclassification rate is 5 so cut the number of nodes down to 5 using prune.tree prunedtree <- prune.tree(meat.tree, best = 5, method = "misclass") Then I want to use predict.tree and the test data set. predicttree <- predict.tree(prunedtree, data = test) I already said what it produces. Again, how would I display the misclassification rate at each node on the diagram? I know about misclass.tree(prunedtree, detail = TRUE) but that doesn't actually display them on the classification tree - it just gives a bunch of numbers of the worksheet and it just wouldn't look very neat if I had to add them later. -- View this message in context: http://www.nabble.com/Couple-of-Questions-about-Classification-trees-tp22461673p22464302.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
