similar to: can not read a table

Hi all, I had a dataset A like: TIME DV 0 0 1 10 5 20 24 30 36 80 48 60 72 15 I would like to add 24 to those values higher than 24 in the TIME column. I did the following: If (A$TIME>=24) { A$TIME <- A$TIME+24} It did not work. How should I do it? Thanks, -- View this message in context: http://r.789695.n4.nabble.com/help-with-if-statement-tp4650315.html Sent from the

Hi, all, I have a column like the following: ID TIME sub_001 0 sub_001 24 sub_002 0 sub_002 24 sub_003 0 sub_003 24 sub_004 0 sub_005 24 .. .. is there a way for me to just

I have a dataframe such like the following: ID TIME DV 1 0 0.880146038 1 1 0.88669051 1 3 0.610784702 1 5 0.756046666 2 0 0.456263368 2 1 0.369991537 2 3 0.508798346 2 5 0.441037014 3 0 0.854905349 3 1 0.960457553 3 3 0.609434409 3 5 0.655006334 . . . . . . I would like to generate another column with the normalized values of DV. for each ID, normalize to the value at TIME 0. I was able to

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine >

Dear guRus, I am trying to replace "~" by "$\sim$" for TeX. However, I can't get the backslash to work. I would like to turn "DV~IV" into "DV$\sim$IV". sub("~","$\sim$","DV~IV") => "DV$sim$IV" sub("~","$\\sim$","DV~IV") => "DV$sim$IV"

Hi, I have a large data frame and within this there is one column which contains individual codes (eg. 1.1234.2a.2). I am splitting these codes into their 4 components using strsplit (eg. "1", "1234", "2a", "2"). However there are some individual codes which do not have a last component (eg. 2.4356.3b. ), I want to give these codes a "1" as their

Suppose I have a plot plot(1:10, pch = "") And I want some text to indicate a Normal distrubition. I could do this: text(5, 6, substitute(X~~~~N(mu, sigma^2)), adj = 0) text(5.35, 6, "~", adj = 0) But that's clumsy, and depending on your plotting device, might not even look sensible. I'd prefer to be able to do it more directly and simply the way these do: text(5,

Dear List, I am relatively new to R and am trying to create more attractive plots than excel can manage! I have looked through the various programmes ggplot, lattice, hmisc etc but my case seems to be not metnioned, maybe it is but i have not noticed - if this is the case i apologise.

Hi, i have a strange situation: A domain is still running where domblklist points to a snapshot file and also dumpxml says the current drive is that snapshot file. But the file has been deleted hours ago. And the domain is still running. I can login via ssh, the database and the webserver are still running, domain is performant. How can that be ? Also lsof shows that the file is deleted:

Hi all Is there any reason why the parameter i in a "for" loop ignores a value of zero? For example sim=c() p=.2 for(i in 0:5) {sim[i]=dbinom(i,5,p) } sim [1] 0.40960 0.20480 0.05120 0.00640 0.00032 In this example the quantile i= 0 was ignored since dbinom(0,5,p) [1] 0.32768 The same behaviour occurs if I use a while loop to perform the same calculation: sim=c() p=.2 i=0

Dear all, I have a data frame with one factor and four numeric variables and wish to obtain the var-cor matrix separately by factor. I tried by() and sapply() but getting nowhere. I understand this can be done by subsetting the dataframe, but there should have some sleek ways of doing it. Here is a simulated dataframe; s <- rep(c("A","B","C"), c(25,22,18)) d

I want to load many files in the R. The names of the files are "Sim1.txt", " Sim2.txt", "Sim3.txt", "Sim4.txt", "Sim5.txt" and so on. Can I read them at one time? What should I do? I can give the same names in R. Thanks. For example: > tst=paste("Sim",1:20,".txt",sep="") # the file names > tst [1]

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

Hi, Can anyone help sort out the problem with the following script - I am a R newbie and I am self taught. obs.all = c() for(i in 1:386){ if (n.sim[i]>0){ obs = (1:133429)[event.details[,2] == i] obs.all = c(obs.all, sample(obs[obs < n.sim[i]], size = n.sim[i], replace=T)) } Basically, in the sample bit, I only want to get obs.all if the value of

In some time series simulations I'm doing, I occasionally want the model to be ``white noise'', i.e. no model at all. I thought it would be nice if I could fit this into the arima.sim() context, without making an exceptional case. I.e. one ***could*** do something to the effect if(length(model)==0) x <- rnorm(n) else x <- arima.sim(model,n) but it would be more suave if one

Hello all. I am trying to run PAN, multilevel multiple imputation program, in R to impute missing data in a longitudinal dataset. I could successfully run the multiple imputation when I only imputed one variable. However, when I tried to impute a time-varying covariate as well as a response variable, I received an error message, “Error: subscript out of bounds.” Can anyone tell if my commands

Hey All I am trying to read in a small text file using read.table. dput(sim) structure(list(ï...X. = 1:7, Y1 = c(2.5501, 4.105, 5.4687, 7.0009, 8.5066, 9.785, 11.5167), Y2 = c(2.5501, 4.105, 5.4687, 11.0009, 8.5066, 9.785, 11.5167), Y3 = c(2.5501, 4.105, 5.4687, 7.0009, 8.5066, 9.785, 15.5167)), .Names = c("ï...X.", "Y1", "Y2", "Y3" ), class =

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the