similar to: Executing the same function on consecutive files

I have a list of dataframes i.e. each list element is a dataframe with three columns and differing number of rows. The third column takes on only two values. I wish to split the list into two sublists based on the value of the third column of the list element.  Second issue with lists as well. I would like to reduce each of the sublist based on the range of the second column, i.e. if the range of

Hi all, I try to exceute a function "myfun" that should use as input "input1.csv" and "input2.csv" . Then I try to save the output dat33 on a csv file (on per each time I execute input1..input 2 and so on). So my problem is how to finally obtain several csv file with "ggt1.csv", "ggt2.csv". The program creates ggt1.csv but BUT when runs

First PRoblem solved > read.csv("KT80.csv") X x 1 1 0.01331361 > I ommitt "" in calling the file name... 2011/12/14 Trying To learn again <tryingtolearnagain@gmail.com> > Hi all, > > I have 100 csv files always with this information (I Attach two example > excels) > > KT80.csv contains: > ,"x" >

Hi group, I imported 16 data frames using the function "list.files" temp <- list.files(path="...........") myfiles = lapply(temp, read.table,sep = "") Now I have 16 data set imported in R window. I want to combine them by row and tried some thing like (Here I am considering only 20 columns) for(i in 1:16){ data<- cbind(myfiles[[i]][,1:20]) } but it

Folks, I am trying to get a loop to run which increments the object name as part of the loop. Here "fit1" "fit2" "fit3" and "fit4" are linear regression models that I have created. > for (ii in c(1:4)){ + SSE[ii]=rbind(anova(fit[ii])$"Sum Sq") + dfe[ii]=rbind(summary(fit[ii])$df) + } Error in anova(fit[ii]) : object 'fit' not found

Dear all, I have 9 data frames, and I'm simply trying to sum the values of column 3 (on a row-by-row basis). However, there are a slightly different number of rows in each data frame, so I'm receiving the following error: "Error in Ops.data.frame(mrunoff_207101[3], mrunoff_207102[3]) : ? + only defined for equally-sized data frames". Here is what I'm attempting to do:

Hi I would like to shorten mod1 <- nls(ColName2 ~ ColName1, data = table, ...) mod2 <- nls(ColName3 ~ ColName1, data = table, ...) mod3 <- nls(ColName4 ~ ColName1, data = table, ...) ... is there something like cols = c(ColName2,ColName3,ColName4,...) for i in ... mod[i-1] <- nls(ColName[i] ~ ColName1, data = table, ...) I am looking forward to help Christof

Hi there, I ran the following code: vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption vols.csv" , header=TRUE, sep=",") X<-ts(vols[,2]) #X dcOU<-function(x,t,x0,theta,log=FALSE){ Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) }

Hi vorbis-devs, I use the Ogg Vorbis libraries built from the win32sdk in winlame. Since the rc2 DLL's are not functioning, I compiled them by myself. The apparent reason is that some data arrays like _residue_P were not shared among DLLs. I looked at the code, and found that IMHO dirty hack in vorbis/win32/src/shmmap*.h that maps the data arrays. Why not placing the data arrays in

Hi, I have two datasets, x and y. Simplified x and y denote: X Y A B C A B C . . . . . . . . . . . . . . . . . . I want to implement all possible models such as lm(X$A~Y$A), lm(X$B~Y$B), lm(X$C~Y$C)... I have tried the following: fun<- function(x,y){ for(i in 1:length(colnames(x))){ for(j in 1:length(colnames(y))){

Hello, I have an R script that I use as a template to perform a task for multiple files (in this case, multiple chromosomes). What I would like to do is to utilize a simple loop to parse through each chromosome number so that I don't have to type the same code over and over again in the R console. I've tried using: for(i in 1:22){ etc.. } and replacing each chromosome number with

Hello. I create FLAC file decoding, processing and playing program and have the following question : how to convert FLAC 16 bit file data to 32 bit float buffer for CPU processing? I've already inplemented sound playing and tested it with sine wave - it works without problems; I even made writing into decoding buffer values of sine wave, instead of decoded FLAC file data, and it also works

I realize this should be simple, but even after reading over the several help pages several times, I still cannot decide between the myriad "apply" functions to address it. I simply want to apply a function to all the rows (or columns) of the same index from two (or more) identically sized arrays (or data frames). For example: > a=matrix(1:50,nrow=10) >

I'm wondering if anyone can give some basic advice about how to approach a specific task in R. I'm new to R but have used SAS for many years, and while I can muscle through a lot of the code details, I'm unsure of a few things. Specific questions: If I have to perform a set of actions on a group of files, should I use a loop (I feel like I've heard people say to avoid looping

Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for each distinct value of ID. Specifically, for the set of data corresponding to each of the 4 values of ID (76,111,121,168) in the below data, I should invoke

Hi all, I have a function written by me that read a matrix (data frame) from a txt with 4 million of rows and 13 columns. The think is my function works with an input matrix of 100x13 and now I tried to execute my function with the big "input file" and it is running form the moment two hours... There is a way to know (how much time could it cost?) The second question is... I want

The replay gain code has dos line endings in CVS, which causes problems for the Sun compiler, among others. Attached is a patch for the lazy, but it's probably easier to fix locally and commit. -r

I've examining a number of linear regression models on a large dataset following the basic ideas presented here http://www.r-bloggers.com/r-calculating-all-possible-linear-regression-models-for-a-given-set-of-predictors/ Calculating all possible linear regressions . I run into a problem with ldply when I have a formula that includes no intercept. Here's a simple test to show what happens.

Dear list, I have tried to setup my Xemacs for use with Sweave, which I indend to learn. I have followed the instructions in the Sweave FAQ, that is to say, I put (defun Rnw-mode () (require 'ess-noweb) (noweb-mode) (if (fboundp 'R-mode) (setq noweb-default-code-mode 'R-mode))) (add-to-list 'auto-mode-alist '("\\.Rnw\\'" . Rnw-mode)) (add-to-list

hey, folks, I have two very simple questions. I am not quite sure if I can do this using R. so, I am analyzing a large data frame with thousands of variables. For example: Dependent variables: d1, d2, d3 (i.e., there are three dependent variables) Independent variables: s1, s2, s3, ......s1000 (i.e., there are 1000 independent variables) now I want to run simple linear regression analyses of