search for: subspace

I use the very nice expm functions available from the expm and Matrix packages. My understanding is that for large sparse matrices the currently best methods available are Krylov subspace methods, but they are as far as I can tell not implemented in either of the packages mentioned, nor in any other R package I have found. Does anybody know if Krylov subspace methods are available from any R package? If not, is there anybody working on this or planning to do so? If not, I might b...

As of version 0.9.2 the program continuum has started, and not failed right at the beginning like it did in 0.9.1. Now, due to its anti cheating measures, it goes through many kernel32, ntdll, gdi32, advapi32, and comctl32 calls along with some winex11drv calls. For some reason, when i try to log it to a file, it grows to 2 GB or greater. I haven't waited for it to finish, as i don't

Dear R users, When fitting a lme() object (from the nlme library), is it possible to test interactions *before* main effects? As I understand, R conventionally re-orders all terms such that highest-order interactions come last - but I??d like to know if it??s possible (and sensible) to change this ordering of terms. I??ve tried the terms() command (from aov) but I don??t know if something

.... but that could be used maliciously to override other > (important) values in the stats core, like "number of users", etc. So to do > this we'd need to put all the metadata fields into some sub-namespace in the > stats core, which I don't think is currently possible? Subspaces were sort of the point, exactly for this reason. Did I forget to build that in? I'm pretty sure it's doable, or else I was an idiot. Either is possible, of course. In any case, putting ALL metadata into the stats is hte right solution I think, and if that means subspaces, so be it :) ja...

Suppose I have the following hat matrix: H=X(X'X)^{-1}X' X is a n by p matrix, where n >= p and X_{i,1} = 1 I'm wondering why H1 = 1. (Here, 1 is column vector, whose each element is the number 1) Thank you!

Dear all, Does anybody have an idea or suggestion how to construct (plot) 4-dimensional hypercube in R. Thanks in advance for any pointers. Regards, Andrej

I have a question regarding the most efficient way to select a substring of a vector: I have a vector of value v, and I want to select a subspace of this vector called w such that: w=v[1:n] where sum(w) = x I am interested in what you thing would be the most efficient way to do this - I would like to avoid slowing down my simulations as much as possible. Thank you very much for any help that anyone is able to give. [[alternative HTML...

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3 y<-rpois(500,exp(linp)) sum(y) library(mgcv) #basis dimension k=5...

> A patch that put _configurable_ extra fields from vorbis comment headers into > the stats core might be a good idea, though. Want to do that? Or perhaps a patch which puts all fields into the stats core. Why be selective? jack. --- >8 ---- List archives: http://www.xiph.org/archives/ icecast project homepage: http://www.icecast.org/ To unsubscribe from this list, send a message to

...usly to override other > > (important) values in the stats core, like "number of users", etc. So > to do > > this we'd need to put all the metadata fields into some sub-namespace > in the > > stats core, which I don't think is currently possible? > >Subspaces were sort of the point, exactly for this reason. Did I forget >to build that in? I'm pretty sure it's doable, or else I was an idiot. >Either is possible, of course. > >In any case, putting ALL metadata into the stats is hte right solution I >think, and if that means subspa...

Hi, there: Can anyone here kindly point some good reference or links on this topic? Esp. some solutions from BioConductor or R, when dealing with microarray-like, "fat" data? thanks, -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[alternative HTML version deleted]]

hi all i have a question that about the eigen analysis found in R and in eviews. i used the same data set in the two packages and found different answers. which is incorrect? the data is: aa ( a correlation matrix) 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 now > svd(aa) $d [1] 4.9204

...de. This means that, for example, i32 addrspace(1) * 100 points to a different piece of memory than i32 addrspace(2) * 100. Also, this means that a bitcast from one address space to another (possibly 0), makes the pointer point to something different when loaded. b) Every address space is really a subspace of the full range of addresses, but always disjoint. This means that, for example, i32 addrspace(1) * 100 points to the same memory as i32 addrspace(2) * 100, though one, or possibly both of them can be invalid (since the pointer lies outside of that address space). This also means that bitcasting...

...e others colinear with it. Then, using that information to split the vector (explanatory variable) in question, into two new vectors, one should correspond to the fitted values and one the residuals of the (I think you could call it nested) model. One vector therefore should be aligned with the subspacespace defined by the other variables colinear with it, and the other will be residual, and so orthogonal to the subspace of the colinear variables. Then by including these two variables in the origional model - the one that showed the order dependency, you can see how much explanatory power the...

Dear All Maybe the following is a stupid question. Assume I have 3 coordinate points (not limited to be in 2D or 3D space) a, b, c. It is known that these 3 points will define a plane. The problem is how to get the normal direction that is orthogonal to this plane. Is there an easy way to calculate it using the values of a, b, and c? Thanks for any point or help on this. Fred

Dear R people, I am trying to plot the results from running svm in library(e1071). I use plot.svm. After searching through the help archives and FAQ, I still have several questions: 1. In default, crosses indicate support vectors. But why are there two colors of crosses? What do they represent? 2. I want to draw a white-gray colored plot and modify the different colored crosses or circles by

...example, i32 addrspace(1) * 100 points > to a different piece of memory than i32 addrspace(2) * 100. Also, this means > that a bitcast from one address space to another (possibly 0), makes the > pointer point to something different when loaded. > > b) Every address space is really a subspace of the full range of addresses, > but always disjoint. This means that, for example, i32 addrspace(1) * 100 > points to the same memory as i32 addrspace(2) * 100, though one, or possibly > both of them can be invalid (since the pointer lies outside of that address > space). This also me...

Hello! Does anyone now if there exist a function that would compute a "null space" (or "kernel" - "Ker") of a matrix and maybe also one that would compute an "image" ("Im") of a matrix. I tried R-site search and google, However I found notnihg useful! Thanks for any sugestions! I am also not sure what an "image" of a matrix is, so

Hello. Consider the following matrix: mp <- matrix(c(0,1/4,1/4,3/4,0,1/4,1/4,3/4,1/2),3,3,byrow=T) > mp [,1] [,2] [,3] [1,] 0.00 0.25 0.25 [2,] 0.75 0.00 0.25 [3,] 0.25 0.75 0.50 The eigenvectors of the previous matrix are 1, 0.25 and 0.25 and it is not a diagonalizable matrix. When you try to find the eigenvalues and eigenvectors with R, R responses: > eigen(mp) $values [1]

...ry > wary of taking away the thing about factors that makes them fundamentally > not characters, and removing the effectiveness of the level restriction, in > practice, does that. For what it's worth, I always thought about factors as fundamentally characters, but with restrictions: a subspace of all possible strings. And I'd say that a non-negligible number of R users may think about them in a similar way. In fact, if you search "concatenation factors", you'll see that back in 2008 somebody asked on R-help [1] because he wanted to do exactly what Hadley is describing...