similar to: matrix multiplication, tensor product, block-diagonal and fast computation

I'd like to compare tests based on the mixed model representation of additive models, testing among others y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2) (testing for additivity) In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12

Dear all, I need to compute tensor product of B-spline defined over equi-spaced break-points. I wrote my own program (it works in a 2-dimensional setting) library(splines) # set the break-points Knots = seq(-1,1,length=10) # number of splines M = (length(Knots)-4)^2 # short cut to splineDesign function bspline = function(x) splineDesign(Knots,x,outer.ok = T) # bivariate tensor product of

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

Hi Everyone, To complete the outer() and kronecker() functions in the base, may I suggest the following tensor() function, which allows the multiplication of arrays through sets of conformable dimensions. I am happy to write a help page if required. The code also needs a setdiff() function which prompts me to ask: what about simple set functions? I expect many of us have written our own

Hi, I am trying to fit a smoothing model where there are three dimensions over which I can smooth (x,y,z). I expect interactions between some, or all, of these terms, and so I have set up my model as mdl <- gam(PA ~ s(x) + s(y) + s(z) + te(x,y) + te(x,z) + te(y,z) + te(x,y,z),...) I have recently read about the ti(), "tensor product interaction smoother", which takes care of these

Hi guys, I need some help to analyzing my data. I start to describe my data: I have 21 matrices, every matrix on the rows has users and on columns has items, in my case films. Element of index (i, j) represent the rating expressed by user i about item j. I have a matrix for each of professions. An example of a this type of matrix is: item 1 item 2 item 3 item4 id

Folks: In answer to a query, Andy Liaw recently submitted some code to construct a block diagonal matrix. For what seemed a fairly straightforward task, the code seemed a little "overweight" to me (that's an American stock analyst's term, btw), so I came up with a slightly cleaner version (with help from Andy): bdiag<-function(...){ mlist<-list(...) ## handle case in

Dear All, I have tried to install the tensor package (from: http://cran.r-project.org/src/contrib/PACKAGES.html#tensor ), using the comand: " shell("Rcmd INSTALL tensor") " then I got the following mensage: ---------------------------------------------------------------------------------- make: Entering directory `/d/bin/cran/rw1022/src/gnuwin32' make DLLNM= EXTRADOCS= \

Hi, I have tried to install 'tensor' as a new package in these phases: 1. via 'Install package from local Zip-File' in Rgui/Windows98: install.packages("D:/R/_Pakets12MB/tensor.zip", .lib.loc[1], CRAN = NULL) 2. Looking at the library folder, there was now a new entry 'tensor' including some files e.g. \library\tensor\R\tensor.R 3. Then I have done

I mistakenly left a write in "/tmp" in the rockchalk package (version 1.8.109) that I uploaded last Friday. Kurt H wrote and asked me to fix today. While uploading a new one, I became aware of a problem I had not seen. The version I uploaded last Friday, 1.8.109, has OK status on all platforms except r-release-windows-ix86+x86_64. I get OK on oldrel-windows and also on devel-windows.

Hello List I have just written a little function that takes two matrices as arguments and returns a large matrix that is composed of the two input matrices in upper-left position and lower-right position with a padding value everywhere else. (function definition and toy example below). I need nonsquare matrices and rowname() and colname() inherited appropriately. Two questions: (1) Is there a

Hi everyone, I ran a binomial GAM consisting of a tensor product of two continuous variables, a continuous parametric term and crossed random intercepts on a data set with 13,042 rows. When trying to plot the tensor product with vis.gam(), I get the following error message: Error in persp.default(m1, m2, z, col = col, zlim = c(min.z, max.z), xlab = view[1], : invalid 'z' limits In

I've set up a simple tensor with indices 'a' and 'b'. > ftable(B) b u v w x y a a1 0.001868954 0.403345197 0.030088185 0.137252368 0.142634612 a2 0.396935972 0.945219795 0.068828465 0.314180585 0.446338719 a3 0.752412200 0.748810918 0.125532631 0.471686930 0.345062348 a4 0.191103000 0.536607533 0.257740399

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Hi all, In preparation for teaching a class next week, I've been reviewing R's standard modelling algebra. I've used it for a long time and have a pretty good intuitive feel for how it works, but would like to understand more of the technical details. The best (online) reference I've found so far is the section in "An Introduction to R"

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Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and

Avi, I was not trying to provide the most economical solution. I was trying to anticipate that people (either the OP or others searching for how to stack columns of a matrix) might be motivated by calculations in multilinear algebra, in which case they might be interested in the rTensor package. On Sun, Aug 6, 2023 at 6:16?PM <avi.e.gross at gmail.com> wrote: > > Eric, > > I