similar to: running aov() and lme() on 64-bit

Good day, I need to subset a data by removing several rows. I know the %in% operator, i.e. sub <- mydata[group %in% c("A","B","E","G"), ] What I need is the opposite, that is remove rows and/or columns. What is the operator for "NOT IN"? I tried (i)! %in% and (ii) ^%in% and both resulted in a "could not find function" error.

I'm running 10, 000 iterations each for the bridge and blasso. 3, 000 iterations roughly takes a week in a core-duo processor with 16GB RAM. I'll have access to a 6C processor machine and I came across the multicore package. Can I use multicore with the bridge and blasso function? That is, is parallel processing possible for the bridge and blasso functions by using the multicore

Dear all, I imagine that this is a trival question, but it has perplexed for most of the day. Any help would be greatly appreciated. Below is an example of what I'm trying to do. Essentially I want to produce all unique 1 x 1 combinations of certain columns from a dataframe, and join these to other columns from the same dataframe. I'm having problems with the nested loop as I can only

Hi! I have fitted two glms assuming a poisson distribution which are: fit1 <- glm(Aids ~ Year, data=aids, family=poisson()) fit2 <- glm(Aids ~ Year+I(Year^2), data=aids, family=poisson()) I am trying to work out how to represent the fitted regression curves of fit1 and fit2 on the one graph. I have tried: graphics.off() plot(Aids ~ Year, data = aids) line(glm(Aids ~ Year,

Hi, I know that is not possible make a stepwise procedure using REML in R, I can use ML for this. For nested design it may be very dangerous due the difference in variance structure, mainly in a splitplot design. ML make significative variables that REML dont make. I read an article that is made a stepwise procedure using GENSTAT. from article: "Terms were dropped from a model in a

Hi all, How can I have a dynamic list for different combinations of grouping factors in the following example? Thanks. dat <- data.frame(x=rnorm(100), a=sample(letters[1:5], replace = T), b=sample(letters[1:5], replace = T), c=sample(letters[1:5], replace = T), d=sample(letters[1:5], replace = T)) ## define grouping

Hi, I make this model using lme m.lme <- lme(Glycogen~Treatment,random=~1|rTrt/Liver) How to make this using lmer? I try > m.lmer <- lmer(Glycogen~Treatment+(1|rTrt/Liver)) Erro em lmer(Glycogen ~ Treatment + (1 | rTrt/Liver)) : entry 0 in matrix[0,0] has row 2147483647 and column 2147483647 Al??m disso: Mensagem de aviso: / not meaningful for factors in: Ops.factor(rTrt, Liver)

Hi! How to calculate the correct SE of mean in a nested or spliplot anova? Nested example: --------------------- m <- aov(Glycogen~Treatment+Error(Treatment/Rat/Liver)) > m Call: aov(formula = Glycogen ~ Treatment + Error(Treatment/Rat/Liver)) Grand Mean: 142.2222 Stratum 1: Treatment Terms: Treatment Sum of Squares 1557.556 Deg. of Freedom 2 Estimated

Hello everybody, I am wondering if it is possible to create a gui to plot a time series that is very big, it's an EEG signal of 20mins. What I would like to do is plot the first 5mins, then have a button on the gui that plots the next 5mins when pushed. Is it possible? Thanks in advance ! Gael.

Hi, I have this: lme(y~x1+x2,random=~1|x1/x2) How to make this random effect using lmer? I try this: lmer(y~x1+x2+(1|x1/x2) But it dont work. Any idea? Thanks Ronaldo -- System halted! -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior ] |> V [UFV/DBA-Entomologia ] | / \ [36570-000 Vi??osa -

Hi all, I need to run a program (asreml) thousands of times and each time I have to provide a slightly different dataset. Because I have to run asreml under Windows (DOS or scripts) environment, I have trouble to pass a command variable (or pointer variable or %counter in the following example) to R program so at each counter R can generate a different data set for asreml to run. Any

Hi, Sean: I'm sorry I lie. I didn't mean to lie. I did try to avoid making a *BIG* change to the IPO pass-ordering for now. However, when I make a minor change to populateLTOPassManager() by separating module-pass and non-module-passes, I saw quite a few performance difference, most of them are degradations. Attacking these degradations one by one in a piecemeal manner is wasting

Hi all, While dat['a1',] and dat['a10',] produce the same results in the following example, I'd like dat['a1',] to return NAs. dat <- data.frame(x1 = paste(letters[1:5],10, sep=''), x2=rnorm(5)) rownames(dat) <- dat$x1 dat['a1',] dat['a10',] > sessionInfo() R version 2.7.2 (2008-08-25) i386-pc-mingw32 locale: