similar to: Reading data for discriminant analysis

Hi !! I am trying to analyze some of my data using linear discriminant analysis. I worked out the following example code in Venables and Ripley It does not seem to be happy with it. ============================ library(MASS) library(stats) data(iris3) ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3]) ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

> data(iris) # iris3 is first 3 rows of iris > iris3 <- iris[1:3,] # z compares row 1 to each row of iris3 and is correctly computed > z <- c(F,F,F) > for(i in seq(z)) z[i] <- identical(iris3[1,],iris3[i,]) > z [1] TRUE FALSE FALSE # this should do the same but is incorrect > apply(iris3,1,function(x)identical(x,iris3[1,])) 1 2 3 FALSE FALSE FALSE

Can anyone explain the root of my problem? When I type the following code into R, I receive 42 support vectors insted of the 21 stated in the book 'Modern Applied Statistics with S': library(MASS); library(e1071); library(class); lcrabs <- log(crabs[,4:8]); (svm(crabs$sp ~ ., data = lcrabs, cost = 100, gamma = 1)); By changing the value of gamma I can obtain only 21 support vectors,

I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X <- read.table("data", header=T) lda_analysis <- lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x,

I've been experimenting with the lattice graphics in chapter 3 of MASS. The code to produce figure 3.14 is: data(crabs) library(mva) lcrabs.pc <- predict(princomp(log(crabs[,4:8]))) crabs.grp <- c("B", "b", "O", "o")[rep(1:4, rep(50,4))] splom( ~lcrabs.pc[, 1:3], groups = crabs.grp, panel = panel.superpose, key = list(text =

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

I'm trying to learn how to create Trellis multi-panel plots, but I'm having some trouble reproducing the graphs shown in Venables & Ripley (2002) (e.g., Figs 4.14 & 4.15). Actually, everything looks fine except for the fact that I can't see any text above the individual panels. I'm using R 1.9.0 for OS-X running on Mac OSX 10.3.3, and I'm drawing the graphs into

Dear All, After followed the help of nnet, I could get the networks trained and, excitedly, get the prediction for other samples. It is a two classes data set, I used "N" and "P" to label the two. My question is, how do I get the predicted numerical value for each sample? Not just give me the label(either "N" or "P")? Thanks! FYI: The nnet example I

Dear r-help, I am trying to build a new function (to process rpart objects) that will output matrix that has a row for each node and a column for each feature. With each entry in the table is a numerical property at that node for that feature (e.g. surrogate split agreement, improvement). My current trouble is that the only clue to the identity of the feature is stored as the *name* of the

Dear R users, I am a novice user working through V & R. My first question stems from p20 of the second edition.: I perform the following actions: > mydata <- c(2.9,3.4,3.4,3.7,3.7,2.8,2.8,2.5,2.4,2.4) > names(mydata) <- c('c','j','b','e','i','h','g','d','f','a') > mydata c j b e i h

Hi all, I have a question about the SOM routine. You can either supply the initial representatives for the lattice yourself or else they are chosen randomly from the dataset. Is it possible to pass the random-seed as an argument somehow, when choosing the random initialisation of the lattice? As it is now, each time I run a SOM on a dataset with the same settings the resulting SOM will still

> > library(MASS) > Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + Sp = rep(c("s","c","v"), rep(50,3))) > train <- sample(1:150, 75) > table(Iris$Sp[train]) c s v 22 23 30 > z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Error in if (targetlist[i] == stringname) { : argument is of length

Dear All, I was trying to follow some of the examples in Venables and Ripley "Modern applied ... with S-plus" I have downloaded a copy of the iris data set and loaded into R. : however I cannot use the apply command (from p47): > apply (iris, 2 ,mean) Error in sum(..., na.rm = na.rm) : invalid "mode" of argument > apply (iris, c(2) ,mean) Error in sum(..., na.rm =

I hope this time I'm using the "iris" dataset correctly: ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) lir <- data.frame(log(ir)) names(lir) <- c("a","b","c","d") I'm trying to understand the meaning of expressions like "~ a+b+c+d", used with princomp, e.g. princomp(~ a+b+c+d, data=lir, cor=T) By inspection, it

Using the data set fgl in MASS the following code layout(matrix(1:9,3,3)) for(i in 1:9){ boxplot(fgl[,i] ~ type, data = fgl,main=dimnames(fgl)[[2]][i])} produces a 3 by 3 array of plots, each one of which consists of six boxplots. Is it possible to do this in lattice? Steve "R version 2.7.2 (2008-08-25)" on Ubuntu 6.06

Dear sir or ma'am, I have a question about the dataset "fgl." The dataset seems to be in the "VR" package, so I tried to download it from CRAN. However, after downloading, when I tried to load the package, it was not in my package list. I am wondering what is wrong. Any advice on how to access the fgl dataset would be appreciated. Thanks. Seungho Huh

Hello all, If I run this script, and observe the output, the results are not there at all (try to do the same in the browser). Any suggestions? require ''rubygems'' require ''mechanize'' agent = WWW::Mechanize.new agent.user_agent_alias = ''Mac Safari'' page =

Hello all, I have tried to post this yesterday, but noticed I was actually not subscribed yet... Well, here we go again: If I run this script, and observe the output, the results are not there at all (try to do the same in the browser). Any suggestions? require ''rubygems'' require ''mechanize'' agent = WWW::Mechanize.new agent.user_agent_alias =

hi, all, I am trying to build 1.1.12 for symbian platform like this: 1, bldmake bldfiles 2, abld build wins udeb (to build for emulator) but building failed, the error message is : nmake -nologo -x - -s -f "\Install\Symbian\8.0a\S60_2nd_FP2\EPOC32\BUILD\HOME\MYOWN\NOKIA\SPEEX\SPEEX-1.1.12\SYMBIAN\SPEEX\WINS\SPEEX.WINS" LIBRARY Mdf.c