similar to: model.matrix metadata

Thanks everyone for your answers. Mats, my goal is do an alternate implementation of the subtyping test in the Pony compiler. I'll try to keep the explanation short to avoid cluttering the mail with irrelevant information. Every object in Pony has a type descriptor, which is used in the subtyping test to determine the real type of an object. The current algorithm is suboptimal and

Hi. First of all, some context. I'm trying to implement a new functionality in an LLVM-based compiler and I need to take various actions based on the value of a given pointer, the possible values being the addresses of various global constants. I tried to use a `switch` instruction but I encountered several problems. The "ideal switch" I'd like to have would look

Hi all, I have 2 simple questions... First, just for you to know, I'm making a compiler (kinda obvious) and the language definition is mine, so there is no much constraint for me, it's written in Java (it generates a .ll file as output) and it's just for fun by now, maybe a serious project after I learn all the stuff needed... and sorry bad english 1) I want to know if when I do

On 3/14/11 7:52 PM, Judison wrote: > Hi all, > > I have 2 simple questions... > > First, just for you to know, I'm making a compiler (kinda obvious) and > the language definition is mine, so there is no much constraint for > me, it's written in Java (it generates a .ll file as output) and it's > just for fun by now, maybe a serious project after I learn all

On Mar 14, 2011, at 5:56 PM, John Criswell wrote: > On 3/14/11 7:52 PM, Judison wrote: >> >> 2) does the LLVM optimizes this: >> >> ; this code (arg -> var) is very similar to what gcc generates >> define void @foo(i32 %arg0) { >> entry: >> %var0 = alloca i32 >> store i32 %arg0, i32* var0 >> ... >> %x = load i32*

Hi R-users, I'm trying to retrieve data from MS SQL database with RODBC's sqlQuery-function: temp <- sqlQuery(channel,"select *, (select text from table1 where koodi='paa' and koodi2=paa) as tempor from table2") str(temp) … $ var0 : num NA NA 1.6 NA NA 1.4 NA 1 NA NA ... $ var1 : Factor w/ 45 levels "

Hello, I have various datasets of zoo time series (merged into single frames via the "merge" command) such as date var0 var1 var2 var3 08/07/1996 652.54 0.223922 0.515819 0.502638 08/08/1996 662.59 0.997841 0.000383 0.999806 06/09/1996 655.68 0.901685 0.569763 0.866333 08/10/1996 700.64 0.268855 0.244701 0.329285 08/11/1996 730.82 0.438407 0.501427 0.461374

Thank you John and John :P these optimizations (mem2reg and the "few additional" ones) I have to enable then or something like this?? (I compile the .ll to .o with the sequence llvm-as, llc, as) Yet about dynamic stack allocation, what is (generally) better? to pre alloc everything at start, or let it be? Imagine this pseudo-code while (x) { int b = 0; ... } using alloca where b

Hi R-users, I would like to calculate weighted mean of several variables by two factors where the weight vector is the same for all variables. Below, there is a simple example where I have only two variables: "v1","v2" both weighted by "wt" and my factors are "gender" and "year". set.seed(1) df <- data.frame(gender = rep(c("M",

I've been running a glmer logit on a very large data set (600k obs). Running on a 10% subset works correctly, but for the complete data set, R completes apparently without error, but does not display the results. Given these jobs take about 200 hours, it's very hard to make progress by trial and error. I append the code and the sample and complete output. As is apparent, I upgraded R

Hi there, i can''t use array in puppet. Here is my codesnipplet: $callapidata = [ ''wert1'',''wert2''] notice $callapidata[1] A puppetrun results in: Could not retrieve catalog from remote server: Error 400 on SERVER: can''t convert String into Integer at /etc/puppet/myenv/modules/ uc4client/manifests/init.pp:41 on node testnode Thanks for your

Hello everybody, I'm trying to predict a linear regression model but it does not work. My Model: y = Worktime + Vacation + Illnes + Bankholidays My modelmatrix is of dimension 28x4 Then I want to make use of the function predict because there confidence.intervals are include. My idea was: mod <- lm(y~Worktime+Vacation+Illnes+Bankholidays) newdate=data.frame(x=c(324,123,0.9,0.1))

Dear list members, I am analysing my microarray data using limma package. Now I encounter several problems. Looking forward to your suggestions! Question 1: During the process of background correction using method="normexp", four warning messages appeared as "NaNs produced in: log(x)" (as you can see in the program posted below). What does that mean? How will it effect the

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance

Dear R-Help, I am using the LIMMA User's Guide 5 January 2007 PDF version. For the example show in Section 7.4 DIRECT TWO-COLOR DESIGNS (pgs. 33-34), I could not grasp the rationale in developing the contrast.matrix with these R statements (">" indicates the R command prompt): > contrast.matrix <-

Hi, I have a question about limma. I have data from spotted arrays. I have class A and Class B on the same slide. In Limma if I put class A as the reference (ref), are the list of genes I get in the output for class A or class B? #design <- modelMatrix(targets, ref="classA") design Josephine Brennan [[alternative HTML version deleted]]

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've finally been able to piece this together, but I wonder if I've got it right/whether there is anywhere the behaviour of `model.matrix` with respect to `na.action` is more *explicitly* documented. * model.matrix() respects the 'na.action' argument associated with its data. * If the 'data' argument is a model frame

Dear All,   please help with some thoughts on overcoming the following issues, if possible:   #R Code   require(deSolve) require(FME) pars <- list(k = 0.06,v=18) intimes <- c(0,0.5,12,12.5,50) input   <- c(800,0,800,0,0) forc <- approxfun(intimes, input, method="constant") model <- function(pars, times=seq(0, 50, by = 1)) {   derivs <- function(t, state, pars) {     

Time Series functions in R ========================== I think a good basic S-like functionality for library(ts) in base R would include ts class, tsp, is.ts, as.ts plot methods start end window frequency cycle deltat lag diff aggregate filter spectrum, spec.pgram, spec.taper, cumulative periodogram, spec.ar? ar -- at least univariate by Yule-Walker arima -- sim, filter, mle, diag, forecast

Dear R-colleagues, I am quite a newbie to R fighting my stupidity to solve a probably quite simple problem of generating artificial data with defined features. I am conducting a study of inter-observer-agreement in child-bronchoscopy. One of the most important measures is Kappa according to Fleiss, which is very comfortable available in R through the irr-package. Unfortunately medical doctors