similar to: to raise in a loop more than 1

R helpers I am getting to grips with R but came across a small problem today that I could not fix by myself. I have 3 text files, each with a single column of data. I read them in using: myData1<-scan("C:/Program Files/R/myData1.txt") myData2<-scan("C:/Program Files/R/myData2.txt") myData3<-scan("C:/Program Files/R/myData3.txt") I wanted to produce a

Hi, I would obtain a random value between two (for example between 40.15 and 56.58 I would have only one value). I'm looking for a package/a function which could do this. Could anybody help me please? Cordialement Damien Landais

Hi, I have a problem. I have a data frame looking like: ID val A? .3 B? 1.2 C? 3.4 D? 2.2 E? 2.0 I need to CREATE the following TABLE: CASE?? DIFF A-A??? 0 A-B??? -0.9 A-C??? -3.1 A-D??? -1.9 A-E??? -1.7 B-A??? ... B-B??? ... B-C B-D B-E C-A C-B C-C C-D C-E D-A D-B D-C D-D D-E E-A E-B E-C E-D E-E WHERE CASE IS THE COUPLE OF ELEMENTS CONSIDEREDM AND DIFF IS THE computed DIFFERENCE between

Hello all, I am working with a 1000x1000 matrix, and I would like to return a 1000x1000 matrix that tells me which value in the matrix is greater than a theshold value (1 or 0 indicator). i have tried mat2<-as.matrix(as.numeric(mat1>0.25)) but that returns a 1:100000 matrix. I have also tried for loops, but they are grossly inefficient. THanks for all your help in advance. Lanre

Hi I have a panel dataset with large number of groups and differing number of observations for each group. I want to randomly select say, 20% of the groups or 200 groups, but along with all observations from the selcted groups (with the corresponding data). I guess it is possible to generate a random sample from the groups ids and then match that with the entire dataset to have the intended

Hi, I would know how to plot two variograms on a same graph. I can plot one by one but I would draw both on the same one. Is it possible? Do i need any special package? Thanks! Cordialement Damien Landais

Dear users, I have a double for loop that does exactly what I want, but is quite slow. It is not so much with this simplified example, but IRL it is slow. Can anyone help me improve it? The data and code for foo_reg() are available at the end of the email; I preferred going directly into the problematic part. Here is the code (I tried to simplify it but I cannot do it too much or else it

you could try mapply mydata2<-mapply("[", mydata, lapply(mydata, function(x) !x %in% A)) mydata2[[1]]<-A #to replace the obviously deleted elements of "A" mydata2 mydata2[[1]] mydata2[[2]] mydata2[[3]] mydata2[[4]] Stefano -----Messaggio originale----- Da: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]Per conto di jastar

Hi, I have a problem. Please, look at example and try to help me!! > A<-c("aaa","bbb","ccc","ddd","eee") > B<-c("vvv","ooo","aaa","eee","zzz","bbb") > C<-c("sss","jjj","ppp","ddd") > D<-c("bbb","ccc")

Hi, I have a dataset including monthly date from 1971-01-01 to 2009-01-01. The dates are character variables. I want to select the subsample from my original data, so I use mydata3<-window(mydata,start="1972-02-01",end="2005-02-01"), or mydata3<-window(mydata, start=as.date("1972-02-01"),end=as.date("2005-02-01")) or mydata3<-window(mydata,

Hello, does anyone know of a quick way to create a data frame "like" another, but with more rows? What I'd like to do is this: if mydata is a data.frame like a b c 1 TRUE yes 2 FALSE no 3 TRUE yes I'd like to get mydata2 with the same column names and column types, but without the values and with more rows. All I could think of was to manually do

I'm trying to convert data from character to numeric. I've imported data as a csv file, I'm assuming that the import is a database - are all the columns in a database considered "vectors" and that they can be operated on individually Therefore I've tried the following mydata <- as.numeric(mydata$apples) when i then look at mydata again the named column is still

Hi: I want to give an index with all the dates between Sept. to Nov. as 1, and anything else is 0. It doesn't matter which year it is, as long as it is between Sept. to Nov, then set up to 1, otherwise is 0. My data frame looks like below: ID Date 201 1/1/05 6:07 AM 201 3/27/09 9:45 AM 201 9/29/09 8:44 AM 203 10/16/08 10:01 AM 203 10/28/08 9:45 AM 203 10/31/08 11:12 AM 203 11/7/08 11:32 AM

Hello, I have some code that currently works fine and I am endeavoring to convert the major pieces of it into functions. This involves taking "hard coded" names of variables that are used in various places and figuring out how to abstract them out into functions where the arguments (i.e. a list of variables)?can be passed to the parent function and used within that function for various

Hi All, I'm a little stumped by the following problem. I've got a dataset with the following structure: idxy ix iy country (other variables) 1 1 1 c1 x1 2 1 2 c1 x2 3 1 3 c1 x3 . . . . . 3739 55 67 c7 x3739 3740 55 68 c7 x3740 where ix and

Dear All, I have a dissimilarity matrix which I happily convert to a distance object by running: X <- as.dist(Y) and I can happily now run either hclust(X) or agnes(X). So that the various bits of output are labelled correctly I would dearly like to be able to give names to the columns and rows of X, as would happen if I ran: mydata<-read.table("clipboard",header=T)

I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent). I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and

I'm trying to calculate the percent change for a time-series variable. Basically the first several observations often look like this, x <- c(100, 0, 0, 150, 130, 0, 0, 200, 0) and then later in the life of the variable they're are generally no more 0's. So when I try to calculate the percent change from one observation to the next, I end up with a lot of NA/Nan/INF, and

Hi I'm using R(2.9.0) and gstat package under Windows to plot sample variograms. When I want to fit them, I don't have the possibility to choose Cauchy functions. Under Mathematica for example, we have it and it seems to be the best one to fit the variograms I have I tested the others functions, they can fit but I would,if possible, use the Cauchy one. If anybody knows something about