similar to: R-devel does not update the C++ returned variables

Thanks! I went through the online posts which supports the power of .Call over .C. But my probably naive question is why does this work for my code with R but not R-devel? And another question is related to using .Call. Based on the manual page, I do not need to change the function parameters when using .Call. So I can run like this: .Call("sppedUp", D, S, pD, pS, nrow(D), as.integer(N),

On 02/03/2015 8:46 AM, sarah manderni wrote: > Thanks! I went through the online posts which supports the power of > .Call over .C. But my probably naive question is why does this work > for my code with R but not R-devel? Because of the change mentioned in the NEWS file. > And another question is related to using .Call. Based on the manual > page, I do not need to change the

On 02/03/2015 3:50 AM, sarah manderni wrote: > Hi, > > Within my R code, I am using a C++ function as below: > > overlaps <- matrix(0, nrow=B, ncol=length(N)) > overlaps.P <- matrix(0, nrow=B, ncol=length(N)) > > .C("speedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), > ssq[i], i, as.integer(B), overlaps,

Hi, I am using an R package which includes C++ code inside. The package is functioning well under latest official R version but not under R-devel. I know that the C++ part is not functioning properly and I get an error using R-devel (it returns nothing to the R part of the program). Do I need to debug the C++ code separately for R-devel? Would be grateful if you could suggest any links with

Hi I am using ggplot2 at the moment and I must say it is definitely better then ggplot - good work. My problem is that I am using facet_grid() in the following way: > p <- ggplot(ssq, aes(x=year, y=-log(ssq))) > p + geom_point() + facet_grid(me*gi~cs*rz) and it works nicely, except that I would like to have, in naddition to the values of me, gi, cs and rz the name of the variable.

Hi I have some density estimates obtained from density(). I would like to calculate the sum of squares of these. As the x values of the estimates are not the same, and I would prefer not to restrict the estiomate to a certain range of x values, how can I do the calculation? Lets say: d1 <- density(Data1) d2 <- density(Data2) If the x values would be the same, I would: ssq <- sum(

Hi everyone, Can someone explain me how to calculate SAS type III sums of squares in R? Not that I would like to use them, I know they are problematic. I would like to know how to calculate them in order to demonstrate that strange things happen when you use them (for a course for example). I know you can use drop1(lm(), test="F") but for an lm(y~A+B+A:B), type III SSQs are only

Hello all, I've tried to reproduce the contour plot that appears in the book of Venables and Ripley, at page 255. Is a F-statistic surface and a confidence region for the regression parameters of a non-linear model. It uses the stormer data that are in the MASS package. I haven't been able to reproduce the plot either in R ( version 1.5 ) and S. It makes the axes and it puts the

Dear Greg, Thanks for the new release. The decomposition of the SSQ is just what I need! Regards, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle >>> gregory_r_warnes at groton.pfizer.com 10/30/02 07:16PM >>> Version 0.7.3 of the gregmisc package

Dear R user, I have a question on using R to analyze data with repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I

Hi, recently I have started working with R (v. 2.7.2), and I have been using R's internal ARIMA_Like() function (from the "stats" package) to estimate some ARIMA models. In particular, I use ARIMA_Like() in a function "fn()" that I feed to the optim() method; the main goal is to find optimal ARIMA prediction models for some time series. The ARIMA_Like() function returns a

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is:

> From: kjetil halvorsen [mailto:kjetilh at umsanet.edu.bo] > > Martin Hoyle wrote: > > > > Dear Greg, > > Thanks for the new release. The decomposition of the SSQ is > just what I need! > > > I thought decomposition of the SSQ can be obtained with the split= > argument to summary.aov? That argument seems tro be new --- it didnt > exist two years

Hi, I installed and tested your version, but the indexer process crashes reproducible with the following command after about 2000 messages were indexed: doveadm index -u paul at iwascoding.com -q \* Jan 14 09:26:15 mail dovecot: indexer-worker(paul at iwascoding.com)<16777><IKpfOqBHPFyJQQAADYqDFA>: Debug: Mailbox sent: UID 2038: Opened mail because: fts indexing Jan 14 09:26:15 mail

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,

# Dear R list, # # A have a doubt about SSQ decomposition and contrasts with ANOVAs. # So, I would like a tip from more advanced R users. # Below my data, the basic script and my doubts: # Data r = paste('r', gl(3, 8), sep='') e = paste('e', rep(gl(2, 4), 3), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4,

Before re-inventing the wheel I would like to ask: does anyone know about an optimizer in R which can reliably identify which value of X (Xopt) leads to Y (Yopt) closest to Ytarget in Y <- MonotoneStepFun(X) optionally with the restriction that Yopt <= Ytarget (at least if any Y <= Ytarget, otherwise any Yopt > Ytarget would be the preferred answer) If none is known, I will write

Dear R-list! My question is related to an Anova including within and between subject factors and unequal group sizes. Here is a minimal example of what I did: library(car) within1 <- c(1,2,3,4,5,6,4,5,3,2); within2 <- c(3,4,3,4,3,4,3,4,5,4) values <- data.frame(w1 = within1, w2 = within2) values <- as.matrix(values) between <- factor(c(rep(1,4), rep(2,6))) betweenanova <-

Ok, just to clarify, RegUnits, as far I understand, are Physical registers or alias to Physical registers. They exist because some instructions use physical registers directly rather than virtual register. It's right? And why this RegUnits should be present in the Interference Graph? I thought were only the Live Intervals would be the nodes of the graph. Sorry about the trouble to

I have two character vectors x and y that have the following characteristics: length(x) # same as length(y) # 872099 length(unique(x)) # 47740 length(unique(y)) # 52478 I need to crosstabulate them, which would lead to a table with 47740*52478 # 2505299720 cells, which is more than 2^31 # 2147483648 cells, which seems to be R's limit because I am getting the error message Error in