similar to: Question about making array dataset inside a package

I am writing a function where the arguments are names of objects or variable names in a data frame. To convert the strings to the objects I am using eval(parse(text=name)): f.graph.two.vbs<-function(dataname,v1){ val<-paste(dataname,v1,sep="$") val<-eval(parse(text=val)) val } However running this returns an error:

I have an R script with the following applicable lines: xshort <- window(s, start=st, end=ed) . . . xshort <- ts(xshort, frequency=1, start=1) . . . m1 <- m2 <- m3 <- m4 <- m5 <- m6 <- NULL m1 <- tslm(xshort ~ trend) I get an error: Error in get(dataname) : object 'xshort' not found When I do traceback() I get: 3: get(dataname) 2: tslm(xshort ~

Hey everybody, i am using the rugarch-package and its great! I have a pretty easy problem, but i just dont get it, so thanks if you can help me. Normally i use: / data(DATANAME) spec = ugarchspec() fit = ugarchfit(data = x[,1], spec = spec) fit slotNames(fit) names(fit at fit) coef(fit) infocriteria(fit) likelihood(fit) nyblom(fit) signbias(fit) head(as.data.frame(fit)) head(sigma(fit))

At 10:37 PM 5/1/00 -0400, E. S. Venkatraman wrote: >I have the following problem. I have survival data (time, status) along >with several covariates (X1, X2,..., Xn). I want to fit a Cox model for >each of the covariate (univariately) and obtain the fitted probability of >survival at a fixed time point t0 and covariate value Xi0. I tried to do >this in a for loop where the index

I have been using R the past couple of years to run models on data we are collecting. I recently got a new computer and updated to a new version of R (2.60 -> 2.90). Since the update, I cannot get my syntax to run. I have tried copying the file it is looking for into many different directories to try and run it. In the last version, I found that it was easiest if the file was copied into the R

The generic rule is that R is not a macro language, so looping of names of things gets awkward. It is usually easier to use compound objects like lists and iterate over them. E.g. datanames <- paste0("aa_", 2000:2007) datalist <- lapply(datanames, get) names(datalist) <- datanames col1 <- lapply(datalist, "[[", 1) colnum <- lapply(col1, as.numeric) (The 2nd

Hi R-users! Being new to R, and a fairly advanced Stata-user, I guess part of my problem is that my mindset (and probably my language as well) is wrong. Anyway, I have what I guess is a rather simple problem, that I now without success spent days trying to solve. I have a bunch of datasets imported from Stata that is labelled aa_2000 aa_2001 aa_2002, etc. Each dataset is imported as a matrix, and

Hi! Thanks for the replies! I understand people more accustomed to R doesn't like looping much, and that thinking about loops is something I do since I worked with Stata a lot. The syntax from Peter Dalgaard was really clever, and I learned a lot from it, even though it didn't solve my problem (I guess it wasn't very well explained). My problem was basically that I have a data matrix

Um, if you insist on doing it that way, at least use assign(varname, as.vector(get(varname))) -pd > On 4 Dec 2017, at 22:46 , Love Bohman <love.bohman at sociology.su.se> wrote: > > Hi! > Thanks for the replies! > I understand people more accustomed to R doesn't like looping much, and that thinking about loops is something I do since I worked with Stata a lot. The

Dear R-devel, I've made some potential extensions to writeForeignSAS in 'foreign' that I wanted to pass along if anyone is interested. I've attached the diff -u output against the version found in foreign_0.8-15 and an .R file with my changes. (In this .R file, the function is named writeForeignSAS7 to simplify testing/comparisons.) I've tried to alter the current

I have a dataframe with names in the first column and wait times between decisions in the second column. Since individuals make multiple decisions, I want the average for each individual. For example, the data might look like this name wtime jo 1 jo 2 jo 1 jo 3 tim 3 tim 2 tim 2 ro 1 ro 2 etc. I'm hoping there is something like mean(dataname$wtime[name]) which will just

The function addmargins() adds margins to a table, but returns a matrix. But even after converted to a table the print.zero="." option of print.table() does not work: > x <- sample( 1:7, 20, replace=T ) > y <- sample( 1:7, 20, replace=T ) > tt <- table( x, y ) > tx <- as.table( addmargins( table( x, y ) ) ) > print( tt, zero.print="." ) y x 1 2

The function addmargins() adds margins to a table, but returns a matrix. But even after converted to a table the print.zero="." option of print.table() does not work: > x <- sample( 1:7, 20, replace=T ) > y <- sample( 1:7, 20, replace=T ) > tt <- table( x, y ) > tx <- as.table( addmargins( table( x, y ) ) ) > print( tt, zero.print="." ) y x 1 2

:-) I don't insist on anything, I'm just struggling to learn a new language and partly a new way of thinking, and I really appreciate the corrections. I hope I someday will be able to handle lists in R as easy as I handle loops in Stata... Thanks again! Love -----Ursprungligt meddelande----- Fr?n: peter dalgaard [mailto:pdalgd at gmail.com] Skickat: den 4 december 2017 23:09 Till:

Dear all, I''m reading codes in sch_sqf.c in kernel 2.4 of Linux. I find I messed up with the concept of slot, bucket and hash tables. Could anyone explain the data stucture of the hash tables a little to me. Thanks very much. Best, Franklin BTW: I can not understand why the number of entries of dep[SFQ_DEPTH*] should be twice:( _______________________________________________ LARTC

Commit 90805dc (c/s 26387:4056e5a3d815) ("EPT: Make ept data stucture or operations neutral") makes nested p2m tables be unlinked from the host p2m table before their destruction (in p2m_teardown_nestedp2m). However, by this time the host p2m table has already been torn down, leading to a possible race condition where another allocation between the two kinds of table being torn down can

library(survival) library(boot) data=NULL lambda=NULL result=NULL pat=rep(1:102,each=1) trt=rep(c(1,0),51) status=rep(1,102) site=rep(1:51, each=2) nr.datasets=100 seed=2006 beta=log(1/2) for (i in 1:51) { lambda[i]=1+((3-1)/50)*(i-1)} lambda1=rep(lambda, each=2) dummy=rep(c(exp(beta),1),51) elf=lambda1*dummy r=70 #the number of bootstrap replicates

Loops are not evil, and no-one in this thread said they are. But I believe your failure to provide a reproducible example is creating confusion, since you may be using words that mean one thing to you and something else to the readers here. ################################ # A reproducible example includes a tiny set of sample data # Since we cannot reproducibly refer to filenames (your

Full_Name: John P Cavanaugh Version: .99 OS: linux Submission from: (NULL) (24.116.10.99) dataset$ema12 <- EMA (dataset$Close,12) dataset$ema26 <- EMA (dataset$Close,26) dataset$MACD_fast <- dataset$ema26 - dataset$ema12 dataset$MACD_slow <- EMA(dataset$MACD_fast,9) dataset$MACD_hist <- dataset$MACD_fast - dataset$MACD_slow # This line doesnt work!!! But... if I does work if I

Hello, I'm using library(spatstat) and trying to use ppp on my dataset. I get a segmentation error when I try to run it. Any suggestions? Code below ... Thanks, Suzanne --------------------------------------- dat <- read.delim(file="trees_R2.csv", sep=",", header=1) P16 <- (dat$Plot == "P1") | (dat$Plot == "P2") | (dat$Plot == "P3")