similar to: Can somebody suggest how to achieve following data manipulation?

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

I'm trying to generate some plots for print publication and I'm having trouble getting them the way I want. I get a plot with a legend that overlaps the bars and looks bad. Also, I'm saving this as PDF file (whatever happened to the "png" function to save as PNG format, in r 1.4.1?) I've tried a number of things, which produce a number of questions. 1) it seems that the

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

In an earlier post, a person wanted to divide each of the rows of rawdata by the row vector sens so he did below but didn't like it and asked if there was a better solution. rawdata <- data.frame(rbind(c(1,2,2), c(4,5,6))) sens <- c(2,4,6) temp <- t(rawdata)/sens temp <- t(temp) print(temp) Gabor sent three other solutions and I understood 2 of them but not the

I understand that in R, for loops are not used as often as other languages, and am trying to learn how to avoid them. I am wondering if there is a more efficient way to write a certain piece of code, which right now I can only envision as a for loop. I have a data file that basically looks like: 1,55 1,23 2,12 ... that defines a matrix. Each row of the data file corresponds to a row of the

Dear R, I import data from spss into a R data.frame. On this rawdata I do some data processing (selection of observations, normalization, recoding of variables etc..). The result is stored in a new data.frame, however, in this new data.frame the value labels are lost. Example of what I do in code: # read raw data from spss rawdata <- read.spss("./data/T50937.SAV",

Hi R -users, i've a table as describe below. I'm reading the numeric value presented in this table to populate a list. #table #============ #X    A    B    C #x1    2    3    4 #x2    5    7    10 #x4    2    3    5 #============ rawData <- read.table("raw_data.txt",header=T, sep="\t") myList=list() counter=0 for (i in c(1:length(rawData$X))) {     print (i)    

Hello there, Much thanks in advance for any help. I have a few questions: 1) Why do I keep getting the following error: File1 <- read.csv("../RawData/File1.csv",as.is=TRUE,row.names=1) Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file '../RawData/File1.csv': No such file or

Hi All, I have txt file like : $ cat data.txt US 10 UK 12 Ind 4 Germany 14 France 8 > rawdata <- read.table(file='data.txt',sep='\t' , header=FALSE) > rawdata V1 V2 1 US 10 2 UK 12 3 Ind 4 4 Germany 14 5 France 8 I want to draw pie chart for the above data. How to split rawdata into : con <-

Hi, More questions in my ongoing quest to convert from RapidMiner to R. One thing has become VERY CLEAR: None of the issues I'm asking about here are addressed in RapidMiner. How it handles misisng values, scaling, etc. is hidden within the "black box". Using R is forcing me to take a much deeper look at my data and how my experiments are constructed. (That's a very

I am having a problem understanding the lda package. I have a dataset here: [,1] [,2] [,3] [1,] 2.95 6.63 0 [2,] 2.53 7.79 0 [3,] 3.57 5.65 0 [4,] 3.16 5.47 0 [5,] 2.58 4.46 1 [6,] 2.16 6.22 1 [7,] 3.27 3.52 1 If I do the following; "names(d)<-c("y","x1","x2") d$x1 = d$x1 * 100 d$x2 = d$x2 * 100 g<-lda( y ~ x1 + x2, data=d) v2

Hello, I'm wondering how I can merge two featuresets into one. My dataset is two sets of microarray data and it looks like followings: > rawData $v1 TilingFeatureSet (storageMode: lockedEnvironment) assayData: 2197815 features, 59 samples element names: channel1, channel2 protocolData rowNames: LT290677RU_D1_2011-02-16 LT286300LU_D1_2010-07-24 ... LT003990RU_D1_2010-11-04 (59

Hi everyone, I experience some problems with adressing of data.frames when I retrieve some information for geographical position (ypos, xpos) ot of a MySQL Database and want to perform some simple statistics. The problem is adressing the dataframes with a construct like rawdata[c(type)] vs. rawdata$TEMPMIN to retrieve the numerical information and not a string (I want to store the numerical

Hello, I am importing some raw voltage multichannel measurements into an R data frame. I need to scale each column with the respective sensitivity for that channel. I figured how to do it, but I am curious if there isn't a more elegant way. Now I start with something like this: rawdata <- data.frame(rbind(c(1,2,3), c(4,5,6))) sens <- c(2,4,6) and I do this: data <-

Hello, I call a function via .Call passing to it a raw vector(D) and an integer(I) The vector is a series K1,KData1, V1,VData1, K2, KData2, ... where the integer K1 is the length of Data1 and similarly for Ki (wrt Datai)(similarly for V*) There 2*I such pairs( (Ki,KDatai), (Vi,VDatai)) The numbers Ki(and Vi) are written in network order. I am returning a list of I elements each element a

Thanks David, Yes, I am talking about the MASS package.Thank you for pointing out that these scale the same. My question is, how do I get from the V1 data: V1 1 164.4283 2 166.2492 3 170.5232 4 156.5622 5 127.7540 6 136.7704 7 136.3436 to the other set of data: + 1 -2.3769280 + 2 -2.7049437 + 3 -3.4748309 + 4 -0.9599825 + 5 4.2293774 + 6 2.6052193 + 7 2.6820884 On Mon, Sep 28, 2009

Here's the small scale version of the R script: http://pastebin.com/sEYKv2Vv Here's the file that I'm reading in: http://r.789695.n4.nabble.com/file/n4487682/weatherData.txt weatherData.txt I apologize for the length of the data. I tried to cut it down to 12 lines, however, it wasn't reproducing the bad output that I wanted to show. The problem is that my whole data set

Hi, This should be an easy one, but I have some trouble formatting the data right I'm trying to replace the column of a subset of a dataframe with the scaled data for that column of the subset subset(rawdata, code== "foo", select = a) <- scale( subset(rawdata, code== "foo", select = a) ) It returns: could not find function "subset<-" The scale

R-users, I want to concatenate columns from different files in a single object. I'm doing bad. My peace of code is as follow: rawdata <- list.files("./data") for (i in rawdata) { mat[ ] <- read.table(paste(i ,sep="")) } At the end of the loop I have just one column. What I'm doing wrong? Thanks, Fred -- View this message in context: