similar to: when dimensionality is larger than the number of observations?

Dear R People: I have an AOV model that I get confidence intervals from via > confint(chick1.aov1) 2.5 % 97.5 % trtA 1.472085 1.607915 trtB 1.512085 1.647915 trtC 1.328751 1.464582 > I am using R2HTML to produce HTML output. However, the HTML code itself just has rounded values, i.e., 1.5 and 1.6. Has anyone run across this, please? Any suggestions would be much appreciated.

Hi, I am trying use lm() on some data, the code works fine but I would like to use a more efficient way to do this. The data looks like this (the data is very sparse with a few 1s, -1s and the rest 0s): > head(adj0708) MARGIN Poss P235 P247 P703 P218 P430 P489 P83 P307 P337.... 1 64.28571 29 0 0 0 0 0 0 0 0 0 0 0 0 0 2 -100.00000 6 0 0

Hi all, My question is not really urgent. I can write a loop and solve the problem. But I know that I'll be in a similar situation many more times so it would be useful to find out the answer Is there a fast way to perform linear fit to all the columns of a matrix? (or in the one dimension of a multi-dimensional array.) I'm talking about many single linear fits, not about a multiple fit.

> 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 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],

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)))

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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]

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

> > 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

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'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

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

Hi list, I want to write a general function so that it would take an lm object, extract its data element, then use the data at another R function (eg, glm). I searched R-help list, and found this would do the trick of the first part: a.lm$call$data this would return a name object but could not be recognized as a data.frameby glm. I also tried call(as.character(a.lm$call$data)) or

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 =