similar to: How to get the normal direction to a plane?

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 R users, I want to know, given a curve f in d-dimensional space, It is possible to keep the curve’s shape and size unchanged by an arbitrary dxd orthnormal matrix A? That is, the new curve g = A*f is still the same shape and size as f? Thanks for your advices and answers. Fred [[alternative HTML version deleted]]

I believe eigen(), svd() and qr() can all do it. Andy > From: Jonathan Baron > > On 06/17/04 19:04, Fred wrote: > >Dear R-listers: > > > >I am trying to test an algorithm on a set of linearly > independent vectors > >{x1,x2,...,xn}. > > Well, here's an idea, for 10 vectors of length 10, > as columns of a matrix m1. The 11th seems to be needed.

Hi All, (I have a degree in math, but I am too embarassed to ask my colleagues, so here goes:) I would like to get a vector normal (orthogonal) to a plane formed by two other vectors. In matlab I do this: v1 = [.4, .6, .8]; v2 = [.9, .7, .2]; nn = cross(v1,v2) (gives ~[-.48, .65, -.24] if I do R> cross(v1, v2), I get .94. Huh? Thanks for all your help, again. W

Hi, I want to calculate expected likelihood over a 2D discrete distribution, something like \sum_k \sum_l p_{kl} L(v_k, v_l) It is trivial to write a cycle like for(k in 1:K) for(l in 1:L) sum <- sum + p[k,l]*L(v[k], v[l]) But is there a more clever way for R? Best wishes, Ott

i am trying to use matinv from the Design package to compute the generalized inverse of the normal equations of a 3x3 design via the sweep operator. That is, for the linear model y = ? + x1 + x2 + x1*x2 where x1, x2 are 3-level factors and dummy coding is being used the matrix to be inverted is X'X = 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0 3 0 3 0 1 1 1 0 1 0 0 1

Hi everyone, (This is related to my posting on chi-squared from a day ago. I have tried simulating this but I am still unable to calculate it analytically.) Let y be an n times 1 vector of random normal variables mean zero variance 1 and x be an n times k vector of random normal variables mean zero variance 1. x and y are independent. Then P is the projection matrix P=x*inv(x'*x)*x'

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

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

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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

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less

Dear R helpers, I have a 3D scatter plot that I have generated from scatterplot3d (which looks great- thanks!) and I can see that the points in my graph fall in a plane. Following the example 5 from 3D scatter plot (below) I have fitted a regression plane. Now what I would like to do is a rotation so that my new co-ordinate system is about the fitted plane (by finding the normal to the plane

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

Hi, I am new to R and I would like to display an image on a plane in a 3D plot, i.e. I would like to be able to specify a theta and a phi parameters like in the function persp to display a 2D image on an inclined plane. Regards, vincent [[alternative HTML version deleted]]

Hi, I am new to R and I would like to display an image on a plane in a 3D plot, i.e. I would like to be able to specify a theta and a phi parameters like in the function persp to display a 2D image on an inclined plane. Regards, vincent [[alternative HTML version deleted]]

On Thu, Mar 22, 2018 at 05:51:35PM +0100, Noralf Tr?nnes wrote: > tinydrm is also using plane->fb: > > $ grep -r "plane\.fb" drivers/gpu/drm/tinydrm/ > drivers/gpu/drm/tinydrm/repaper.c:????? if (tdev->pipe.plane.fb != fb) > drivers/gpu/drm/tinydrm/mipi-dbi.c:???? if (tdev->pipe.plane.fb != fb) > drivers/gpu/drm/tinydrm/mipi-dbi.c:???? struct drm_framebuffer

Otherwise none of the format checks pass, since the width was still in 16.16 encoding. Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> --- This must have been some sort of last-second cleanup I made and forgot to test, because with this code, there's no way it could ever have worked... drivers/gpu/drm/nouveau/dispnv04/overlay.c | 17 +++++++++-------- 1 file changed, 9

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the