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Dear sir, I am extremely sorry for messing up the logic asking for help w.r.t. my earlier mails   I have tried to explain below what I am looking for.     I have a database (say, currency_rates) storing datewise currency exchange rates with some base currency XYZ.   currency_rates <- data.frame(date = c("12/31/2010", "12/30/2010", "12/29/2010",

Dear R forum helpers,I have following datatrans <- data.frame(currency_transacted = c("EURO", "USD", "USD", "GBP", "USD", "AUD"), position_amt = c(10000, 25000, 20000, 15000, 22000, 30000))date <- c("12/31/2010", "12/30/2010", "12/29/2010", "12/28/2010", "12/27/2010",

Dear R forum helpers, I am extremely sorry for the receipt of my incomplete mail yesterday. There was connectivity problem at my end and so I chose to send the mail through my cell, only to realize today about the way mail has been transmitted. I am again sending my complete mail through regular channel and sincerely apologize for the inconvenience caused. ## Here is my actual mail Dear R

My mistake sir. I was literally engrossed in my stupid logic, and while doing so, overlooked the simple and very effective solution you had offered. Sorry once again sir and will certainly try to be very careful in future. Thanks again and have a great weekend sir. Regards Amelia --- On Fri, 7/1/11, Petr PIKAL <petr.pikal@precheza.cz> wrote: From: Petr PIKAL

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

On Tue, Jan 29, 2013 at 3:59 PM, Murali, Sriram <sriram.murali at intel.com> wrote: > Our benchmark results show that the compilation time performance improved by > ~0.5%. That's fairly small; what was the standard deviation, confidence interval, etc? -- Sean Silva

The compilation time is measured for different benchmarks while compiling a .bc file into a shared object. The improvement across the range of benchmarks is listed in following table. If the reason behind the need for other performance metrics is to identify possible measurement errors, then I think this table would be of some help. However, we do not have the standard deviation and confidence

Hello, This patch aims to improve compile time performance by increasing the SCEV vector size in LoopStrengthReduce. It is observed that the BaseRegs vector size is 4 in most cases, and elements are frequently copied when it is initialized as SmallVector<const SCEV *, 2> BaseRegs. Our benchmark results show that the compilation time performance improved by ~0.5%. Patch by Wan Xiaofei.

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs