similar to: inference for customized regression in R?

Dear R helpers Suppose I have a data frame as given below mydat = data.frame(x = c(1,1,1, 2, 2, 2, 2, 2, 5, 5, 6), y = c(10, 10, 10, 8, 8, 8, 7, 7, 2, 2, 4)) mydat         x     y 1      1     10 2      1     10 3      1     10 4      2       8 5      2       8 6      2       8 7      2       7 8      2       7 9      5       2 10    5       2 11    6       4 unique(mydat$x) will give me 1,

Hola, Esta es una forma de hacerlo... Mira que lo primero que he modificado es el fichero "x.csv" para sustituir los espacios en los nombres por "_". Y también he quitado los acentos y las eñes... He utilizado el paquete RNNS y la función "mlp()" para ajustar la red. #------------------------------------------- > x <- read.csv("x.csv",

Hi, folks, Happy work in weekends >_< My question is how to plot the dependent variable against one of the predictors with other predictors as constant. Not for the original data, but after prediction. It means y is the predicted value of the dependent variables. The constane value of the other predictors may be the average or some fixed value. ####### y=1:10 x=10:1 z=2:11

A data set(dat),has 2 variables: x and a, and 100 rows. I wanna add 2 variables,and call the new data set dat1: var1:f = a/median(a) var2:x_new = x*f My solution: dat1<-transform(dat,f = a/median(a),x_new = x*f) But gets error reply which says that "f" is not exits since dat has no variables called "f". So I have to do through 2 steps:

Dear all, I have several character strings with a high number of different levels. unique(x) gives me values in the range of 100-200. This creates problems as I would like to use them as predictors in a coxph model. I therefore would like to convert each of these strings to a new string (x_new). x_new should be equal to x for the top n categories (i.e. the top n levels with the highest

Hi Chandler, I would like to run some benchmarks on ARM hardware and to look at impact of your patches on LTO. Kind regards, Evgeny Astigeevich From: llvmdev-bounces at cs.uiuc.edu [mailto:llvmdev-bounces at cs.uiuc.edu] On Behalf Of Chandler Carruth Sent: 15 July 2015 10:45 To: Chandler Carruth; Gerolf Hoflehner Cc: LLVM Developers Mailing List Subject: Re: [LLVMdev] GlobalsModRef

Replying here, but several of the questions raised boil down to "couldn't you make the usage of GetUnderlyingObject conservatively correct?". I'll try and address that. I think this *is* the right approach, but I think it is very hard to do without effectively disabling this part of GlobalsModRef. That is, the easy ways are likely to fire very frequently IMO. The core idea is

Can you say what Benchmark or give a test case so we understand the nature of the regression? As Gerolf said, that will be important to understand what is best to do. On Fri, Jul 17, 2015, 06:43 Evgeny Astigeevich <Evgeny.Astigeevich at arm.com> wrote: > Yes, the regression is stable. I double checked this. A full benchmark > run consists of at least 10 sub-runs to validate the

Before the fix, the compiler may simply return 'noalias' for cases it can not really prove to be noalias, but actually correct by luck (or even wrong noalias, but does not result in miscompile). It would be useful to find out the set of missed noalias queries from GlobalModRef with your benchmark and examine if there is some improvement can be done. David On Fri, Jul 17, 2015 at 6:32

Hey, thanks for benchmarking. How stable is the 2% regression? Michael ran some benchmarks with GlobalsModRef completely disabled and the only differences were in the noise. This was a complete spec2k6 run along with some others. Based on the number of benchmarks run there, I'm going to go ahead and submit these patches, but if you can clarify the impact here, we can look at potentially some

I have some data fit to a smooth.spline object as follows: (x=vector of data for the predictor variable, y=vector of data for the response variable) fit <- smooth.spline(x,y) Now, given a spline fit point y_new, I want to be able to find out what value of x_new yielded this fit value. How to do so? (This problem is the inverse of the predict.smooth.spline function, which takes x_new as input

On Fri, Jul 17, 2015 at 9:13 AM Evgeny Astigeevich < evgeny.astigeevich at arm.com> wrote: > It’s Dhrystone. > Dhrystone has historically not been a good indicator of real-world performance fluctuations, especially at this small of a shift. I'd like to see if we see any fluctuation on larger and more realistic application benchmarks. One advantage of the flag being set is that we

Greetings folks! I would like to enable globalsmodref-aa in the non-LTO pass pipeline so that it gets tested more and there are fewer differences between the two. For all of my benchmarks, this is performance neutral, but I'd appreciate others benchmarking this combination to see if they see any benefits or regressions. You can demo this mode easily: -mllvm -enable-non-lto-gmr Please let me

Dear R Helpers, I'm struggling with a data preparation problem. I feel that it is a quite easy task but I don't get it done. I hope you can help me with that. I have a data frame looking like this: ID <- c(1,1,1,2,2,3,3,3,3) T <- c(1,2,3,1,4,3,5,6,8) x <- rep(1,9) df <- data.frame(ID,T,x) >df ID T x 1 1 1 1 2 1 1 3 1 2 1 1 2 4 1 3 3 1 3 5 1 3 6 1 3 8 1 I want to

I've patched R so that it can be used as a scripting language, i.e. you can (on Unix-like systems) write files containing R code and execute them non-interactively, e.g.: #!/usr/local/lib/R/bin/R.bin --script cat("Hello World!\n") I've updated the patches for R-1.7.0; if you're interested in the patches, e-mail me at mckay@gmr.com, and I'll send them to you. --

Based on function names and structures, this is some version of GCC :) Any way you can post the entire .ll file? Because it's globalsmodref, it's hard to debug without the other functions, since it goes over all the functions to determine address takenness, etc :) On Tue, Jul 21, 2015 at 3:23 PM, Michael Zolotukhin <mzolotukhin at apple.com> wrote: > Hi Chandler, > > We

I am currently trying to find MLE of a function with four parameters. My codes run well but i don't get the results. I get the following message: BHHH maximisation Number of iterations: 0 Return code: 100 Initial value out of range. I don't know this is so because of the way i have written my loglikelihood or what. The loglikelihood LogLik<-function(param){ beta_1<-param[1]

I've hacked R so that it can be used to write shell and/or CGI scripts, e.g. "hello world" can be written as: #!/usr/local/lib/R/bin/R.bin --script cat("Hello, World!\n"); The mods required to enable this aren't particularly major. (Adds about 70 lines of code, IIRC.) Anyone else interested in adding this to the R core? -- Neil D. McKay, Mail Code 480-106-359

Estimados Les consulto por redes neuronales, hay diversos artículos como los siguientes (el último tienen un error actualmente). Pero mi pregunta va un poco por otro lado. http://www.r-bloggers.com/build-your-own-neural-network-classifier-in-r/ http://www.r-bloggers.com/classification-using-neural-net-in-r/ Básicamente se puede calcular un valor, por ejemplo doblar 2,4 grados a la derecha, luego 1

Is there anyway I can write a script which feed input datasource from stdin and let R process it (maybe frequency report) then output the report to stdout? I can't seem to find much info on documentation or FAQ on this topic. Thanks! Soichi Hayashi ********************************************************************** The information contained in this communication is confidential,