similar to: Deleting for() loop in function

Dear useRs, I am trying to build my package (nonpareff) which deals with some models of data envelopment analysis. The building worked well, but checking complains when it tests examples. Zipped nonparaeff.Rcheck is attached. Following is the log. --------------------------------------------- arecibo:tmp arecibo$ R CMD build nonparaeff/ * checking for file 'nonparaeff/DESCRIPTION' ...

Dear useRs, I am trying to build my package (nonpareff) which deals with some models of data envelopment analysis. The building worked well, but checking complains when it tests examples. Zipped nonparaeff.Rcheck is attached. Following is the log. --------------------------------------------- arecibo:tmp arecibo$ R CMD build nonparaeff/ * checking for file 'nonparaeff/DESCRIPTION' ...

Dear list, I have data like this: dat1 <- data.frame(subject=rep(1:10,2), cond1=rep(c("A","B"),each=5), cond2=rep(c("C","D"),each=10), choice=sample(0:1,10,replace=TRUE)) I would like to compare subjects' "choice" for (cond1=="A" & cond2=="C") vs

Dear R-Users, I would like to know whether it is possible to draw several stacked barplots (i.e. side by side on the same sheet)... my data look like : Cond1 Cond1' Cond2 Cond2' Compartment 1 11,81 2,05 12,49 0,70 Compartment 2 10,51 1,98 13,56 0,85 Compartment 3 1,95 0,63 2,81 0,22 Compartment 4 2,08 0,17

Hi, I have a general question on LoopUnswtich pass. Consider the following IR snippet: define i32 @test(i1 %cond) { br label %loop_begin loop_begin: br i1 %cond, label %loop_body, label %loop_exit loop_body: br label %do_something do_something: call void @some_func() noreturn nounwind br label %loop_begin loop_exit: ret i32 0 } declare void @some_func() noreturn After running

I am evaluating a switch from Stata to R. I don't need to extensive Statistical methods, but the main reason I am exploring the switch is the coding flexibility in R (e.g. Stata does not support linear/quadratic programming). I have been going over the R syntax and I had a quick question: In Stata, one has a very useful construction called "by", e.g. by month signal: gen xxx =

Hi all, suppose there is a vector y with rownames: > y cond1 cond2 cond3 cond4 78.952 87.308 86.490 74.040 how can I easily plot this vector using the rownames? plot(y) gives me a plot with a x-axis from 1 to 4 in 0.5 steps, also plot(rownames(y), y) and plot(y ~ rownames(y) don't work. I know I can build a x-axis with axis(1, ...), but in this case I need a character string like

Hello everyone, I am working for a few days already on a basic algorithm, very common in applied agronomy, that aims to determine the degree-days necessary for a given individual to reach a given growth stade. The algorithm (and context) is explained here: http://www.oardc.ohio-state.edu/gdd/glossary.htm , and so I implemented my function in R as follows: DD <- function(Tmin, Tmax, Tseuil,

Some InstCombine transforms for select-of-select were added here: https://reviews.llvm.org/rL228409 But Alive says this is more poisonous: Name: selsel %s1 = select i1 %cond1, i8 C1, i8 C2 %s2 = select i1 %cond2, i8 %s1, i8 C2 => %andcond = and i1 %cond1, %cond2 %s2 = select i1 %andcond, i8 C1, i8 C2 http://rise4fun.com/Alive/JT6 Are those transforms legal? -------------- next part

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

Not a reproducible example, so a bit of guessing here, but a) don't try to assign results to variables inside the ifelse. That is, remove all the single-equals signs and "test" variables. If you really need to conditionally assign variables then use "if"... but chances are good you don't need that. b) "closure" is effectively another word

----- Original Message ----- > From: "Dehao Chen" <dehao at google.com> > To: "Hal Finkel" <hfinkel at anl.gov> > Cc: "llvm-dev" <llvm-dev at lists.llvm.org>, "Xinliang David Li" > <davidxl at google.com> > Sent: Tuesday, November 1, 2016 6:41:29 PM > Subject: Re: [llvm-dev] (RFC) Encoding code duplication factor in

----- Original Message ----- > From: "Dehao Chen" <dehao at google.com> > To: "Hal Finkel" <hfinkel at anl.gov> > Cc: "llvm-dev" <llvm-dev at lists.llvm.org>, "Xinliang David Li" > <davidxl at google.com> > Sent: Tuesday, November 1, 2016 8:24:30 PM > Subject: Re: [llvm-dev] (RFC) Encoding code duplication factor in

Hi all, I have a large dataset that has >10k entries. The dataset is stored in a dataframe with the headers: SubID Condition1 Condition2 Result1 Result2 There are multiple entries for a given SubID(Subject ID). Condition 1 has 3 levels and condition2 has 2 levels (therefore there are 6 possible combinations all together e.g. Cond1 Level1 x Cond2 Level 1 etc.) and i need to compute for 1. The

Dear R users, I have a matrix a and a classification vector b such that > str(a) num [1:50, 1:800000] and > str(b) Factor w/ 3 levels "cond1","cond2","cond3" I'd like to do an anova on all 800000 columns and record the F statistic for each test; I currently do this using f.stat.vec <- numeric(length(a[1,]) for (i in 1:length(a[1,]) { f.test.frame

Discussed with Hal, Adrain and Paul offline at the llvm dev meeting today. * trip count is not enough for vectorization, there is runtime check that might go false, which can be reflected in profile that we may want to preserve. * simply recording these context-profile may cause problems to iterative-sample-pgo. i.e. when you find a loop's vectorized version no executed (due to runtime

On 20.12.18 18:03, Connor Abbott wrote: > We already have the notion of "convergent" functions like > syncthreads(), to which we cannot add control-flow dependencies. > That is, it's legal to hoist syncthreads out of an "if", but it's > not legal to sink it into an "if".  It's not clear to me why we > can't have

I find myself writing the following frequently: r = MyActiveRecord.where(:attr1 => cond1, :attr2 => cond2, ...) r.exists? ? r.first : r.create ... which has the effect of returning an instance of MyActiveRecord if all the conditions are met, or creating one if it doesn''t. A variant of this: c = MyActiveRecord.new(:attr1 => val1, :attr2 => val2, ...) r =

In many cases, the line-table fussing to improve autoFDO/sample-PGO would also likely help the debugging experience for optimized code, certainly in cases where line attribution is inherently ambiguous. In those cases, I have no problem with Just Doing It. Something likely to pad the line table to benefit profiling without similarly benefiting debugging… that's probably worth inventing a