similar to: Testing for Inequality à la "select case"

I have a question concerning memory. I understood that R takes a fixed amount of memory at startup (which I can influence with --vsize --nsize) and that gc() shows the memory still free of the total memory reserved for R. However, if I create a long vector of character data, gc() only seem to reflect the space needed for a vector of pointers to char, the space used for the character data itself

Could of requests: 1) Is there any example or writeup on the difficulties of extending reference classes across packages? Just so I can fully understand the issues. 2) In what sorts of situations does the performance of reference classes cause problems? Sure, it's an order of magnitude slower than constructing a simple environment, but those timings are in microseconds, so one would need a

> 1) Is there any example or writeup on the difficulties of extending > reference classes across packages? Just so I can fully understand the > issues. Here's a simple example: library(scales) library(methods) MyRange <- setRefClass("MyRange", contains = "DiscreteRange") a_range <- MyRange() a_range$train(1:10) # Error in a_range$train(1:10) : could not

Hello I created a radar chart using the function of ?radarchart? from the ?fmsb? package in R software. The matrix I am using is as follows: x<-c(c(rep(4.5,7),c(rep(0,7)), 3.34, 3.28, 1.37, 1.12, 3.52, 4.07, 3.66)); a<-matrix(x,nrow=3, ncol=7,byrow=T) I would like to show the range of c(0,5) on the axis instead of c(0,100) or c(0,1). I really appreciate it if any body can guide me.

Hi, My english isn't brilliant and my problem is very difficult to describe but I try ;) My first question is: May I write loop "for" like this or similar - for (i in sth : sth[length(sth)], k in sth_else : length(sth_else) ) - I'd like to have two independent conditions in the same loop "for". My secound question depend on program below. I'd like to write every

Dear R users we're having a problem reading a largish data file using read.table(). The file consists of 175000 lines of 4 floating pt numbers. Here's what happens: > dat_read.table('sst.dat') Error: memory exhausted (This is line 358 of src/main/memory.c). Cutting down the file to around 15000 lines allows read.table() to work OK. I edited the memory limits in Platform.h

I have configured samba like member of AD, if i type in console 'wbinfo -u' y get all user of my AD, if type in console 'wbinfo -g' y get all groups too. It's correct but if i type 'getent passwd' or 'getent group' don't get any user or group of my AD... why??? * in nsswitch.conf appears: passws: files winbind group: files winbind shadow: files winbind

Tonight I''ve been moving some of my personal data around on my desktop system and have hit some on-disk corruption. As you may know, I''m cursed, and so this had a high probability of ending badly. I have two SCSI disks and use live upgrade, and I have a partition, /aux0, where I tend to keep personal stuff. This is on an SB2500 running snv_46. The upshot is that I have a slice

Does anyone on the list have definitive information on whether ZFS works with CLARiiON devices? bash-3.00# uname -a SunOS XXXXXXX 5.10 Generic_118833-33 sun4u sparc SUNW,Sun-Fire-V245 bash-3.00# powermt display dev=all Pseudo name=emcpower0a CLARiiON ID=APM00033500540 [XXXXXXX] Logical device ID=600601607C550E00F25F4629AFBEDB11 [LUN 61] state=alive; policy=BasicFailover; priority=0; queued-IOs=0

Hello, I have several questions around matrices size limitation: can i create a matrix with 700000 rows? does it depends on the number of columns? if so can Is there a way to ask to R, given the number of columns, the max number of rows I can have? I need in fact to do regression prediction on a huge matrix (700000x1000)ca. ... I will probably have to decompose it on several

Dear R helpers, I am trying to calculate the Annualized Percent Rate using following R - Code. # ____________________________________________________________________ ## R Code library(animation) # INPUT C = 250000    # Loan Amount E = 2500      # Other Cost R = 6         # Interest rate r = R/1200    # Monthly interest rate q = 12        # No of Compoundings        n = 20        # No of

How do I convert a list to a matrix? --8<---------------cut here---------------start------------->8--- list(c(50000, 101), c(1e+05, 46), c(150000, 31), c(2e+05, 17), c(250000, 19), c(3e+05, 11), c(350000, 12), c(4e+05, 25), c(450000, 19), c(5e+05, 16)) as.matrix(a) [,1] [1,] Numeric,2 [2,] Numeric,2 [3,] Numeric,2 [4,] Numeric,2 [5,] Numeric,2 [6,] Numeric,2 [7,]

Hi everyone, My data consists of a system of nearly 75000 roads, available as a shapefile. When I plot the road system, by adding the individual roads with 'lines' and store it as a pdf-file with 'pdf' I get a file of size 13 MB. This is way too large to add it in my LaTeX-document, because there will be some more graphics of this type. Now I'm curious to learn wheter there is

I have a dataframe with 700,000 rows and 2 vectors (columns): ?group? and ?score?. I wish to calculate a third vector of length 700000: the average score by group. Even though the avarge value will repeat, I wish to return the average for that particular group for each row. (I know I can do this by calculating each group?s average and then using the merge command, but as my calculations get

hi, the help page for crossprod states that crossprod(A,B) is faster than t(A) %*% B; experimentation certainly bears this out. more alarming is the evidence that crossprod(t(A), B) is faster than A %*% B: on a PII laptop, 128MB memory, win98, R-1.5.0.-patched precompiled (no ATLAS): > A <- matrix(rnorm(250000),500,500) > B <- matrix(rnorm(250000),500,500) > for (i in 1:5) {

Dear all, I don't get it: First of all, the help doesn't say what are the memory limits of R. Say, what's the max heap size for instance ???? Secondly, I invoke R with the following commands each time: rgui --vsize 30M --nsize 1000K rgui --vsize 30M --nsize 2000K rgui --vsize 30M --nsize 3000K rgui --vsize 30M --nsize 4000K I try to open a matrix 8000x8000 by issuing

Dear all I use aggregate with variables of type numeric and dates. For type numeric functions, such as sum() are very fast, but similar simple functions, such as min() are much slower for the variables of type 'dates'. The difference gets bigger the larger the 'id' var is - but see this sample code: dts <- dates(c("02/27/92", "02/27/92",

i am trying to do some traffic classification using the PRIO qdisc and i am having a few problems. I have a root htb class: tc qdisc add dev imq0 root handle 1: htb default 255 r2q 1 tc class add dev imq0 parent 1: classid 1:1 htb rate 768kbit and a child PRIO tc class add dev imq0 parent 1:1 classid 1:99 htb rate 96kbit ceil 600kbit prio 0 tc qdisc add dev imq0 parent 1:99 prio tc filter

Here's a running instance caught by ps (lines broken for readibility): rsync --perms --times --timeout=3600 --stats --no-motd -hh --force --hard-links -ii --links --partial-dir=.~tmp~ --recursive --exclude <some excludes> --out-format=<format> --delete-during --max-delete=75000 --ignore-errors source dest During download files are being created at path/.filename.random-string

Hi, I am using Loess.smooth (Modreg) in order to infer certain relationship for the data set of ~130,000 observations with ~300 distinct values of single predictor. I understand that fitted values (y-hat) are just 300 Weighted LS fits in certain neighborhood of predictors. I am bit confused about how exactly is this neighborhood assigned . Say I choose spanning parameter = .5, for each LS