similar to: bind matrices by row

Hi, thank you for taking the time and reading my question. My question is twofold: 1. I have several matrices with variables and one matrix with water levels. I want to predict the water level with the data in the other matrices. Basically, * mod<-lm(matrix1 ~ matrix2+matrix3)* ( What looks like a minus is meant to be the wiggly minus.) Of course I could dissemble the matrices and paste

I have a matrix with a lot of values inside.. when I execute the folowing command matrix2=subset(martix, condition.....) it works... but after the previous command I execute another "subset" matrix3=subset(martix2, condition2.....) and appears the following error: (subscript) logical subscript too long How can I solve this??? I think the probles is the size of the matrix...

What are the current limitations of using -O4 in llvm-gcc-4.2? As a first test, I compiled the molscript program first as -O3 and it works fine. However if i try to compile the same code as -O4, the build fails with... ar -cru clib.a args.o str_utils.o dynstring.o err.o indent.o vector3.o matrix3.o quaternion.o body3d.o extent3d.o io_utils.o colour.o key_value.o named_data.o double_hash.o

I'm performing item response theory with eRm packages I am excluding the persons that doesn't fit in the infit/outfit persons. for that I created a condition. then I have to create a new subset or matrix but with the condition. So: ORIGINAL matrix<-cbind(item1, item2, item3, item4) IF I PERFORM A head(matrix) item1 item2 item3 item4 3 2 3 1 3 1

Using -O4 requires support from the system linker, because the files written out are LLVM bitcode files rather than object files. --Owen On Dec 29, 2007, at 11:43 AM, Jack Howarth wrote: > What are the current limitations of using -O4 in > llvm-gcc-4.2? As a first test, I compiled the molscript > program first as -O3 and it works fine. However if i > try to compile the same code

I will test your suggestion, but I designed the test case to load the memory directly into <4 x float> registers. So there is absolutely no permutation and other swizzle or move operations. Maybe the heuristic should not only count the depth but also the surrounding load/store operations. Are the load/store operations vectorized, too? (I designed the test case to completely fit the SSE

On Mon, 2012-02-13 at 11:11 +0100, Carl-Philip Hänsch wrote: > I will test your suggestion, but I designed the test case to load the > memory directly into <4 x float> registers. So there is absolutely no > permutation and other swizzle or move operations. Maybe the heuristic > should not only count the depth but also the surrounding load/store > operations. I've attached

Carl-Philip, The reason that this does not vectorize is that it cannot vectorize the stores; this leaves only the mul-add chains (and some chains with loads), and they only have a depth of 2 (the threshold is 6). If you give clang -mllvm -bb-vectorize-req-chain-depth=2 then it will vectorize. The reason the heuristic has such a large default value is to prevent cases where it costs more to

If you look at the structure, you'll see: > x$V4 <- 0 > str(x) `data.frame': 4 obs. of 4 variables: $ V1: int 1 2 3 4 $ V2: int 5 6 7 8 $ V3: int 9 10 11 12 $ V4: num 0 Don't know if this is the intended result. In any case, you're probably better off using data.matrix, as > data.matrix(x) V1 V2 V3 V4 1 1 5 9 0 2 2 6 10 0 3 3 7 11 0 4 4 8 12

Dear list, If anyone knows how to assign dimnames to matrices or arrays I would be most grateful for help. I've tried various permutations of likely-looking code but get error messages every time. I could find no example in the documentation. Many thanks, Dan Bebber Department of Plant Sciences University of Oxford South Parks Road Oxford OX1 3RB UK Tel. 01865 275000

I have a class that extends array and my method for "[" stops with an error: > setClass("A", contains="array") [1] "A" > setMethod("[", "A", function(x, i, j, ..., drop = TRUE) new("A", callNextMethod())) [1] "[" > a<-new("A",array(1:12,c(4,3,1))) > a An object of class "A" , , 1

is there a smart way of determining the number of pairwise present data in a data matrix with missings (maybe as a by-product of some statistical function?) so far, i used several loops like: for (column1 in 1:99) { for (column2 in 2:100) { for (row in 1:500) { if (!is.na(matrix[row,column1]) & !is.na(matrix[row,column2])) { pairs[col1,col2] <- pairs[col1,col2]+1