similar to: Integrate and mapply

I have two matrix with same dimensions. I want to do t.test using each column from 2 different matrix. Row n Column names in both matrix are same. e.g. Matrix1 id VC1 VC2 VC3 R 1 2 3 R1 4 5 6 R3 7 8 9 Matrix2 id VC1 VC2 VC3 R 10 11 12 R1 13 14 15 R3 16 17 18 want to do t.test using each column (with same name ) using Matrix1 and Matrix2 for eg

Dear R users, I am trying to read in a file with 105 columns, and when trying to attach it, get an error as follows: > vc1<-read.table("P:\\R\\Everything-I.txt", header=T, sep=" ", dec=".", na.strings=NA, strip.white=T) > attach(vc1) Error in attach(vc1) : variable names are limited to 256 bytes Is there a way to get around this, and make R accept the

On Fri, Feb 13, 2015 at 9:52 PM, Ilia Mirkin <imirkin at alum.mit.edu> wrote: > On Fri, Feb 13, 2015 at 8:59 PM, Andy Lutomirski <luto at amacapital.net> wrote: >> My new GTX 960 (GM206, I think) says: >> >> [ 10.329351] nouveau ![ DEVICE][0000:09:00.0] unknown Maxwell chipset >> [ 10.329355] nouveau E[ DEVICE][0000:09:00.0] unknown chipset, 0x126010a1

I have two matrix with same dimensions. I want to do t.test using each column from 2 different matrix. Column names in both matrix are same. e.g. Matrix1 id VC1 VC2 VC3 R 1 2 3 R1 4 5 6 R3 7 8 9 Matrix2 id VC1 VC2 VC3 R 10 11 12 R1 13 14 15 R3 16 17 18 want to do t.test for eg t.test(Matrix1$VC1, Matrix2$VC1)$p.value What is the best way to do it. I have dataset with

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

Hi, everyone, I have a data.frame named "eva" like this: IND PARTNO VC1 EO1 EO2 EO3 EO4 EO5 114 114001 2 5 4 4 5 4 114 114001 2 4 4 4 4 4 114 114001 2 4 NA NA NA NA 112 112002 2 3 3 6 2 6 112 112002 2 1 1 3 4 4 112 112003 2 6 6 6 5 6 112 112003 2 5 7 6 6 6 112 112003 2 6 6 6 4 5 114 114004 2

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

Hi, On my phone right now but I'll fish out the pertinent threads when I get to the office. Keyword searches for 'norecurse' on llvm-dev will probably get most of them. Indeed, this correctness improvement caused a performance regression on some programs. There is a way to revert to the old, broken behaviour: '-mllvm -force-attribute=main:norecurse'. Given how many people run

On 4 May 2015 at 08:37, Shahid, Asghar-ahmad <Asghar-ahmad.Shahid at amd.com> wrote: > My worry is regarding the query for cost calculation for specific SAD > instructions such as ‘psad’ (X86) or ‘usad’ (ARM) in Loop Vectorizer. Hi Shahid, The vectorizer's cost model has the ability to return different costs for the same instruction based on the arguments (scalar/vector,

Using R 2.6.2, say I have the following list of lists, "comb": data1 <- list(a = 1, b = 2, c = 3) data2 <- list(a = 4, b = 5, c = 6) data3 <- list(a = 3, b = 6, c = 9) comb <- list(data1 = data1, data2 = data2, data3 = data3) So that all names for the lowest level list are common. How can I most efficiently access all of the sublist items "a" indexed by the outer

Hi friends, Please can anyone help me with an easier solution of doing the below mentioned work. Suppose i have a dataset like this:--- i1 i2 i3 i4 i5 1 7 13 1 2 2 8 14 2 2 3 9 15 3 3 4 10 16 4 4 5 11 17 5 5 6 12 18 6 7 *i1,i2,i3,i4,i5 are my items.I am able to find all possible pairs i.e Say this dataframe is "item_pairs" **i1,i2 **i1,i3 **i1,i4 i1,i5 **i2,i1

Thanks alot. How can we view those optimization which are enabled when we do llc -O3 comb.ll Are these the same as the ones produced by the following command line llvm-as < /dev/null | opt -O3 -disable-output -debug-pass=Arguments Regards Shahzad On Tue, Jun 12, 2012 at 1:01 PM, Duncan Sands <baldrick at free.fr> wrote: >> Yes. But how exactly code generation (optimized one)

Hi, all When I am using mle.cv(wle), I find a interesting problem: I can't do leave-one-out cross-validation with mle.cv(wle). I will illustrate the problem as following: > xx=matrix(rnorm(20*3),ncol=3) > bb=c(1,2,0) > yy=xx%*%bb+rnorm(20,0,0.001)+0 > summary(mle.cv(yy~xx,split=nrow(xx)-1,monte.carlo=2*nrow(xx),verbose=T), num.max=1)[[1]] mle.cv: dimension of the split subsample

Thanks again. I executed the following command line llc -O3 comb.ll.bc -debug-pass=Arguments and got Pass Arguments: -targetdata -targetpassconfig -no-aa -tbaa -targetlibinfo -basicaa -collector-metadata -machinemoduleinfo -machine-branch-prob -preverify -domtree -verify -loops -loop-simplify -scalar-evolution -loop-simplify -iv-users -loop-reduce -gc-lowering -unreachableblockelim

Hi friends, I need to generate a list, that should contain the quantile value of one column in a data frame. while I am compiling this one i am facing one bug,, Really I cannot find out that where the problem has occurred? Could any one help me to come out from this bug?? Here is the code, data <- lapply(comb.data$zFE, function(x) aggregate(x,

Hi Yes. But how exactly code generation (optimized one) be done without clang. Is it possible that we can specify those optimization (individual ones instead of standard ones like -O3) some how when generating code as is done by clang or llc? Regards Shahzad On Tue, Jun 12, 2012 at 12:42 PM, Duncan Sands <baldrick at free.fr> wrote: > Hi, the reason is that lli does optimized code

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =

I am trying to multiply a 3D array of 4x4x4 by the 4 3D arrays of a 4D array with dimensions 4x4x4x4 (the last dimension being the one that I want to split by). (4x4x4 array) > hiaAry , , a1 i1 i2 i3 i4 h1 9.5936098 6.001040 0.08772 0.3138600 h2 1.2003500 1.454570 2.79248 0.0000000 h3 0.1346500 0.201220 0.39256 0.5464000 h4 0.0109000 0.012270 0.16417 0.2766900 ,

Hi, ? Is there some easy way to split the grid.newpage() into two columns? For example, how could I put the two forest plots below (meta1 and meta2) next to each other? library(meta) data(Olkin95) meta1 <- metabin(event.e, n.e, event.c, n.c,data=Olkin95, subset=c(41,47,51,59),sm="RR", meth="I",studlab=author) meta2=meta1 meta2$studlab=rep("",length(meta1$studlab)

Hi, On 12/06/12 13:09, Abdul Wahid Memon wrote: > Thanks alot. How can we view those optimization which are enabled when we do > > llc -O3 comb.ll llc -O3 comb.ll -debug-pass=Arguments > > Are these the same as the ones produced by the following command line no, as I mentioned before they are of a completely different nature to the optimizations that opt does. Ciao, Duncan.