similar to: help in output file

HI, Dear R community, I have a large data set names dd.txt, the columns are: there are 2402 variables. a1, b1, ..z1, a11, b11, ...z11, a111, b111, ..z111.. IF I dont know the relative position of the columns, but I know I need the following variables: var<-c(a1, c1,a11,b11,f111) Can I use read.columns to read the data into R? I have tried the following codes, but it does not work

HI, Dear R community, I am writing the following function to create one data set(*tree.pred*) and one vector(*valid.out*) from loops. Later, I want to use the data set from this loop to plot curves. I have tried return, list, but I can not use the *tree.pred* data and *valid.out* vector. auc.tree<- function(msplit,mbucket) { * tree.pred<-data.frame()

HI, Dear R community, Last friday, I used the codes, it works, but today, it does not run? > fit.dimer <- rpart(outcome ~., method="class", data=p.df) Error in `[.data.frame`(frame, predictors) : undefined columns selected DOEs anyone have comments or suggestions? Thanks in advance! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

cv.fold<-function(i, size=3, rang=0.3){ cat('Fold ', i, '\n') out.fold.c <-((i-1)*c.each.part +1):(i*c.each.part) out.fold.n <-((i-1)*n.each.part +1):(i*n.each.part) train.cv <- n.cc[-out.fold.c, c(2:2401, 2417)] train.nv <- n.nn[-out.fold.n, c(2:2401, 2417)] train.v<-rbind(train.cv, train.nv) #training data for feature

> fit.dimer <- rpart(as.factor(out) ~ ., method="class", data=p_df) > > fit.dimer$frame[, "var"] [1] NE WC <leaf> TA <leaf> <leaf> WG WD WW WC [11] <leaf> <leaf> <leaf> CT <leaf> FC <leaf> YG QT <leaf> [21] <leaf> <leaf> NW DP DY <leaf> SK

Dear R community, I have one function, it works for small data set, but does not work on large data set, can anyone help me with this? > #creat new variable by dividing each aa dimer by total_length. > imper<-function(x, file) { + round(x/file$length, 5) + } > dim(test) [1] 999 2402 > test[varname[2:2401]]<-

Dear Dr. Sarkar, When I try to run the codes, I found the following problem: > h<- sample(1:14, 319, rep=T) > c<- sample(1:14, 608, rep=T) > n<- sample(1:14, 1140, rep=T) > vt<-c(h, c, n) > ta<-rep(c("h", "c", "n"), c(319, 608, 1140)) > > to<-data.frame(vt,ta) > library(lattice) Attaching package: 'lattice'

HI, Dear R community, How to extract the variables actually used in tree construction? I want to extract these variables and combine other variable as my features in next step model building. > printcp(fit.dimer) Classification tree: rpart(formula = outcome ~ ., data = p_df, method = "class") Variables actually used in tree construction: [1] CT DP DY FC NE NW QT SK TA WC WD WG WW

HI, Dear R community, My original file has 1932 lines, but when I read into R, it changed to 1068 lines, how comes? cdu@nuuk:~/operon$ wc -l id_name_gh5.txt 1932 id_name_gh5.txt > gene_name<-read.table("/home/cdu/operon/id_name_gh5.txt", sep="\t", skip=0, header=F, fill=T) > dim(gene_name) [1] 1068 3 -- Sincerely, Changbin -- Changbin Du DOE Joint Genome

somfunc<- function (file) { aa_som<-scale(file) final.som<-som(data=aa_som, rlen=10000, grid=somgrid(5,4, "hexagonal")) pdf(file="/home/cdu/changbin/file.pdf") #output graphic file. plot(final.som, main="Unsupervised SOM") dev.off() } I have many different files, if I want output pdf file with the same name as for each dataset I feed to the function

I ran this code (several times) from the Quick-R web page ( http://www.statmethods.net/advstats/cart.html) but my cross-validation errors increase instead of decrease (same thing happens with an unrelated data set). Why does this happen? Am I doing something wrong? # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start,

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

Dear community, I have one file named ca_boost_feature.txt, Feature selection (Boosting:0.0025,5)! H.2.C C.1.D C.3.R E.0.N C.2.S C.0.G H.3.G log file: ep If I want to use the second line of this file, how to read it into R? varr<-read.table("/home/cdu/operon/carbonic/ca_boost_feature.txt", sep=" ", skip=1, header=F, strip.white=TRUE, nrows=1) Warning message: In

HI, Dear R community, I am using the following codes to do the som. I tried to label the notes by the majority vote. either through mapping or prediction. I attached my output, the left one dont have any labels in the note, the right one has more than one label in each note. I need to have only one label for each note either by majority vote or prediction. Can anyone give some suggestions or

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

I've run into another postscript/rpart problem unrelated to the issues I've mentioned in a previous query. I'm using 1.3.1 on a Win2K box. >plotcp(some.rpart.object) draws a very nice plot on the windows graphic device. If I save this as a postscript file, either by opening a postscript device before calling plotcp, or by saving the graphics window as a postscript file, the

Full_Name: Gregory R. Warnes Version: R 1.3.1 OS: Solaris 2.8 Submission from: (NULL) (192.77.198.200) rpart library version 3.1-2 Error message: > plotcp(fit.thirds.1,ylim=c(0.7,1.5)); Error in plot.default(ns, xerror, axes = FALSE, xlab = "cp", ylab = "X-val Relative Error", : formal argument "ylim" matched by multiple actual arguments > This can be

hi, I have a question regarding cp values in rpart(). When I use plotcp() I get a figure with cp values on the x-axsis, but then I use printcp() the cp values in that list are different from the values in the figure by plotcp(). Does someone know why? Silje [[alternative HTML version deleted]]

Hi, I am using the packages tree and rpart to build a classification tree to predict a 0/1 outcome. The package rpart has the advantage that the function plotcp gives a visual representation of the cross-validation results with a horizontal line indicating the 1 standard error rule, i.e. the recommendation to select the most parsimonious model (the smallest tree) whose error is not more than one

Hello, I am working with rpart function but geting some error in prediction. the same code works fine with iris dataset. but applying other dataset it doesn't work. sample code is given for reference. > acc_model<-rpart(V1~V2+V3+V4+V5+V6+V7+V8, data=accEx.train) > plotcp(acc_model) >