similar to: Capturing warning within user-defined function

1. I did not attempt to sort through your voluminous code. But I suspect you are trying to reinvent wheels. 2. I don't understand this: "I've failed to find a solution after much searching of various R related forums." A web search on "error handling in R" **immediately** brought up ?tryCatch, which I think is what you want. If not, you should probably explain why it

You can capture warnings by using withCallingHandlers. Here is an example, its help file has more information. dataList <- list( A = data.frame(y=c(TRUE,TRUE,TRUE,FALSE,FALSE), x=1:5), B = data.frame(y=c(TRUE,TRUE,FALSE,TRUE,FALSE), x=1:5), C = data.frame(y=c(FALSE,FALSE,TRUE,TRUE,TRUE), x=1:5)) withWarnings <- function(expr) { .warnings <- NULL # warning handler will

tryCatch() is good for catching errors but not so good for warnings, as it does not let you resume evaluating the expression that emitted the warning. withCallingHandlers(), with its companion invokeRestart(), lets you collect the warnings while letting the evaluation run to completion. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Mar 6, 2018 at 2:45 PM, Bert Gunter <bgunter.4567 at

Hi everyone, apologies if the answer to this is in an obvious place. I've been searching for about a day and haven't found anything.. I'm trying to replicate Stata's confidence intervals in R with the survey package, and the numbers are very very close but not exact. My ultimate goal is to replicate Berkeley's SDA website with R (http://sda.berkeley.edu/), which seems to

Hello, I am using the lmomco package (lmom.ub and pargev) to compute the GEV parameters (location, scale, and shape), which are used to estimate return values. I was wondering how/if I can calculate upper and lower confidence (CI_u, CI_l) intervals for each return frequency using the GEV parameters to fill-in the table below? Xi (location) = 35.396 Alpha (scale) = 1.726 Kappa (shape) =

Hello! So I am handling this problem with some arrays grp1-grp7, I want to write a loop to avoid tedious work, but I don't know how to transform string to boor? For example I used i=1 paste("grp",i, sep="") I only got "grp1" instead of grp1, which can't be manipulate using mean() or other function. I am not sure if I make myself clear... THANKS!!!!

I am trying to age standardize using the svystandardize package in R. I have successfully managed to hit my SUDAAN based targets for estimates by sex, but not the total. The total is only a little different, but I'd like some help knowing why it isn't exact. I've included the SUDAAN code that generates the targets and my R script (and output) that I have so far. I can't supply the

library(plyr) n<-100000 grp1<-sample(1:750, n, replace=T) grp2<-sample(1:750, n, replace=T) d<-data.frame(x=rnorm(n), y=rnorm(n), grp1=grp1, grp2=grp2) system.time({ d$avx1 <- ave(d$x, list(d$grp1, d$grp2)) d$avy1 <- ave(d$y, list(d$grp1, d$grp2)) }) # user system elapsed # 39.300 0.279 40.809 system.time({ d$avx2 <- ave(d$x, interaction(d$grp1, d$grp2, drop =

Hi All, I am using Samba 3.2.11 on sles 10. I have create a Unix group 'grp1' and mapped it to a ntgroup "Group One". Unix group 'grp1' has one user i.e 'usr1'. 'net groupmap' command show the mapping with correct SID and gid value. Please, have a look here. # cat /etc/group | grep grp1 grp1:!:12613:usr1,raj1,raj2 # # net groupmap list Group One

Hi, I have a data.frame that has a categorical variable, for which I would like to look at the distribution of levels of this variable, based on a grouping of two other variables. As an example: x <- data.frame(obs=sample(c('low', 'high'),100, replace=TRUE), grp1=sample(1:10, 100, replace=TRUE), grp2=runif(100)) cut.grp1 <- cut(x$grp1, 3) cut.grp2 <- cut(x$grp2, 3)

Dear Helpful R Users, I am graphing some data using the beanplot, but I am having trouble getting the output I desire. I have five tanks (A-E) and 2 groups for each tank grp1 or grp2, except tank C where there is only grp1. (I only changed the grouprep to "C grp1" for the example) When I plot them, I would like A B C(only grp1 - half of the bean plot) then D and E (as full beans).

On Wed, 5 Dec 2018, Alexander Dalloz wrote: >> I have a group alias (all at company.com). >> (1) Only company.com accounts should be able to send an email to everybody >> in that company via all at company.com. >> (2) - rather optional: refine the restrictions, e.g. two groups, >> grp1 at company.com and grp2 at company.com. Grp1 members should be able to send

Hi, I have a vector of data, that I group based on two factors via tapply. For each such grouping I would like to plot a pie chart. I can layout these pie charts in a matrix layout, correpsonding to the levels of the two factors. But I am getting stuck on how to label the rows and colums. My current approach looks like this: x <- data.frame(obs=sample(c('low', 'high'),100,

[R 2.0.0 on Linux] I tried: > df <- data.frame( grp1=factor( c('A' ,'A' ,'A' ,'D', 'D' ) ) , grp2=factor( c('a1','a2','a2','d1','d1') ) ) > df grp1 grp2 val 1 A a1 1 2 A a2 2 3 A a2 4 4 D d1 8 5 D d1 16 I got: > with( df, ave( val, grp1, grp2, FUN=sum ) )

Hi folks, has anybody a simple solution for the following request? I have a group alias (all at company.com). (1) Only company.com accounts should be able to send an email to everybody in that company via all at company.com. (2) - rather optional: refine the restrictions, e.g. two groups, grp1 at company.com and grp2 at company.com. Grp1 members should be able to send mails to grp2 but not vice

I'd like to summarize several variables in a data frame, for multiple groups, and store the results in a data.frame. To do so, I'm using by(). For example: df<-data.frame(a=1:10,b=11:20,c=21:30,grp1=c("x","y"),grp2=c("x","y"),grp3=c("x","y")) dfsum<-by(df[c("a","b","c")],

Dear R-users, I am using R version 2.9.1 and lattice 0.17-26 under windows. In a lattice boxplot, I would like to add information on how many observations each singel boxplot is based upon. For example (the basic plot): # Begin R-code library(lattice) dat <- data.frame(panvar = rep(c("A","B","A","B"), c(3,7,4,6)), grp = rep(c("grp1",

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() -

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() -

Dear all Is there a clean plyr version of the following by() and do.call(rbind, ...) construct: > df<-data.frame(a=1:10,b=11:20,c=21:30,grp1=c("x","y"),grp2=c("x","y"),grp3=c("x","y")) > dfsum<-by(df[c("a","b","c")], df[c("grp1","grp2","grp3")], range) >