similar to: Fwd: R SOFTWARE

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

hello everyone, I'm wondering whether llvm can work together with simplescalar. Would anyone please give me some clarification? Thanks a lot!

hello everyone, I'm wondering whether llvm can work together with simplescalar. Would anyone please give me some clarification? Thanks a lot!

Dear Bert, thank you for your response. here it is the piece of R code : given 3 data frames below --- N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"),

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Hi Bogdan, Kinda messy, but: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))

This email is also sent to cluster-devel at redhat.com. Since this issue is about both dlm and ocfs2, I send the email here to look for help from upstream. This is an already known issue. on ocfs2 with user space cluster stack, run the test script from http://people.redhat.com/~teigland/make_panic on the mounted ocfs2 volume from 2 nodes simultaneously, the access to ocfs2 volume on both nodes

Hi all, I am new to LLVM (even the field of compiler) and currently I am engaged in the work of adapting LLVM IR to M5 simulator to observe the enhancement of the novel architecture we design. Simply speaking if you know little about M5, my aim is to know how LLVM IR is interpreted and encoded, then try to implement it in the framework of M5. I have read the LLVM documents, yet I still have some

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben

Thank you for your answers and suggestions! Well, I want to do some experiments about the compiler optimization in llvm under SPM and I need performance evaluation. As I know, alpha is a general-purpose CPU, so I consider that arm may be an alternative. Simplesim-arm is able to do performance simulation while m5-arm is still under development. Then, what should I do? Do the experiment under

Thank you David. Using xtabs operation simplifies the code very much, many thanks ;) On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <-

Hello all, Probably my concepts about the data.frame and matrix and array in R are not clear, I need some clarification to help me understand them better. >M <- read.table("test1.csv",sep=",",row.names=NULL,header=T) gives me: M as M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 1 9 11 14 15 18 20 20 20 20 20 2 3 4 8 9 11 12 14 15 15 15 3 4 5 8 8 9 9 9 9 9 9 4 4

Simple matrix indexing suffices without any fancier functionality. ## First convert M and N to character vectors -- which they should have been in the first place! M <- sort(as.character(M[,1])) N <- sort(as.character(N[,1])) ## This could be a one-liner, but I'll split it up for clarity. res <-matrix(NA, length(M),length(N),dimnames = list(M,N)) res[as.matrix(C[,2:1])] <-

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model with the lmer function including some main effects, a two-way interaction and a random effect. Now I am searching for a way to save the predicted values for this model. As far as I can see, there is no command in lme4 to save the predicted values (like the predict(model) function in e.g.

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Dear all, please could you advise on the R code I could use in order to do the following operation : a. -- I have 2 lists of "genome coordinates" : a list is composed by numbers that represent genome coordinates; let's say list N : n1 n2 n3 n4 and a list M: m1 m2 m3 m4 m5 2 -- and a data frame C, where for some pairs of coordinates (n,m) from the lists above, we have a

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <-

Is there any known incompatibility with the latest 32-bit (i386) CentOS 6 and the latest Lenovo x3650 M5 servers? I?ve been running i386 CentOS 6.X on 3 year old x3650 M4 servers without any issues. Our development environment has not been ported to 64-bit (x86_64) yet, so we are stuck using i386 for another few months. When I try to boot from the netinstall ISO image I just get a ?Boot Failed?

Hi, May be this helps: m1<- as.matrix(read.table(text=" y1 g24 y1 0 1 g24 1 0 ",sep="",header=TRUE)) m2<-as.matrix(read.table(text="y1 c1 c2 l17 ?y1 0 1 1 1 ?c1 1 0 1 1 ?c2 1 1 0 1 ?l17 1 1 1 0",sep="",header=TRUE)) m3<- as.matrix(read.table(text="y1 h4??? s2???? s30 ?y1 0 1 1 1 ?h4 1 0 1 1 ?s2 1 1 0 1 ?s30 1 1 1