search for: iris3

> 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,f...

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], iris3[-tr,,2], iris3[-tr,,3]); cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))); z <- lda(train, cl); predict(z, test)$class; data.fra...

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 <- predict(a,log(ir),dimen=2)$x > eqscplot (a.x,type="n",xlab=&q...

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))) ir.lda<-lda(log(ir),ir.species) ir.ld<-predict(ir.lda,dimen=2)$x eqscplot(ir.ld, type="n", xlab = "First linear discriminant", ylab =...

...(). 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 experiencing. Any ideas on how to get around it or will it simply not work with nnet()? > library(MASS) > library(nnet) > library(ipred) > data(iris3) > set.seed(191) > > samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) > ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + species = factor(c(rep("s",50), rep("c", 50), rep("v", 50)))) > errorest(species ~., d...

...the winning class, etc. Andy > From: Ko-Kang Kevin Wang > > Hi, > > [I'm posting this on behalf of a colleague -- as I don't know > knn myself...] > > How to interpret the knn() results? > > Tried the example codes in the documentation: > data(iris3) > train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3]) > test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3]) > cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) > knn(train, test, cl, k = 3, prob=TRU...

...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 followed from help document is, ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), species = c(rep("s",50), rep("c", 50), rep("v", 50))) ir.nn2 <- nnet(species ~ ., data = ird, subset = samp, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) table(ird$species[-samp], predict(...

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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]

> > 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] == st...

...est.cl(targets[-samp], predict(crsNNET1, crsNNET1[-samp])) I get: Error in as.vector(data) : (list) object cannot be coerced to type 'double' By the way, my samp vector is: samp <- c(sample(1:250,125), sample(251:500,125), sample(500:920,300)) Here is the example from nnet: data(iris3) # use half the iris data ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3]) targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) ) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) ir1 <- nnet(ir[samp,], targets[samp,], size = 2, ra...

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]]

...mbers 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 three digits, 0, 0.4677313, and 0.5111955. The script is as below. Please help to understand this behavior. library('AMORE') ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)), 150, 3, byrow=TRUE) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) net <- newff(n.neurons=c(4, 2, 3), # number of units per layer learning.rate.global=1e...

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

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,...

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

...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 = agent.get("http://www.sbstransit.com.sg/iris3/bus_serviceopt.aspx") search_form = page.forms.with.name("Form1").first search_form.txtsvcno = "014" search_form.txtbusstop = ''92129'' search_results = agent.submit(search_form) open(''output.html'',''w'') {|f|...

...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 = agent.get("http://www.sbstransit.com.sg/iris3/bus_serviceopt.aspx") search_form = page.forms.with.name("Form1").first search_form.txtsvcno = "014" search_form.txtbusstop = ''92129'' search_results = agent.submit(search_form) open(''output.html'',''w'') {|f|...