similar to: Error running lda example: Session Info

Hello all, I'm trying to run lda() from the MASS library but the Help example generates the following error: #Code from example in lda Help file # Resulting Error >Error in if (targetlist[i] == stringname) { : argument is of length zero My Current R Installation: MacOSX: 10.5.8 R: 2.10.0 -- Gregory Riddick, PhD. CRTA Research Fellow National Institutes of Health National

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

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

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)))

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

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

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

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 =

Maybe you should show your colleague how to access help pages in R? Right in ?knn, it says: prob: If this is true, the proportion of the votes for the winning class are returned as attribute 'prob'. so 1.0 mean all three NNs are of the `winning'; i.e., predicted, class, and 0.66667 means 2 out of the 3 NNs are of the winning class, etc. Andy > From: Ko-Kang

Please add me to mailing list. regards Astha ************************************** See what's free at http://www.aol.com. [[alternative HTML version deleted]]

It's in the Venables & Ripley MASS (ed 3) book in the section on principal components. The context is as follows > ir <- rbind(iris[,,1], iris[,,2], iris[,,3]) > ir.species <- factor(c(rep("s",50),rep("c",50),rep("v",50))) (then they use brush(ir) which I guess is not an R function) and then > princomp(log(ir[1:4]),cor=T) (there is no [1:4]

Hi. The there is an example in nnet help which is pasted in below. I am not sure how they are generating 'targets'. What is the 'class.ind() function doing? In the help docs for it they say "Generates a class indicator function from a given factor." I tried putting a simple vector of the "classes" into test.cl (below) but I get an error of "(list) object

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

Hi, there: Can anyone here kindly point some good reference or links on this topic? Esp. some solutions from BioConductor or R, when dealing with microarray-like, "fat" data? thanks, -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[alternative HTML version deleted]]

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,

Dear Madame or Sir, in my current project, I have so far used a lot of very different plots. I am now trying to gain informatin with the help of parallel coordinate plots. Therefore, I use the function "parcoord" of the MASS package. What I would like to do, is to color let's say the points according to the first half of rows of a specific matrix/dataframe in a color different from

Hello everyone, I am working on a linear discriminant analysis and am having issues finding the full output of my lda. Specifically, there is no reporting of the Proportion of Trace that is a normal output of the procedure. I'm using a csv file and everything is reading in correctly. I've looked and looked and can't figure out why my output is not complete. Is it something simple that

Hello All, I can run the "lower level" functions OK, but many of the higher level (eg. parSApply) functions are generating errors. When running the example (from the snow help docs) for parApply on MacOSX 10.5.5, I get the following error: cl <- makeSOCKcluster(c("localhost","localhost")) sum(parApply(cl, matrix(1:100,10), 1, sum)) Error in