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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

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

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

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

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

Dear R-users, I am searching to the "best" way to compute a series of n matrix multiplications between each matrix (mXm) in an array (mXmXn), and each column of a matrix (mXn). Please find below an example with four possible solutions. The first is a simple for-loop which one might avoid; the second solution employs the tensor product but then manually selects the right outcomes. The

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

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

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

mgcv version 1.2 is on CRAN now. mgcv provides generalized additive models and generalized additive mixed models with automatic estimation of the smoothness of model components. Changes in this version are: * A new gam fitting method is implemented for the generalized case. It provides more reliable convergence than the previous default, but can be a little slower. See ?gam.method,

mgcv version 1.2 is on CRAN now. mgcv provides generalized additive models and generalized additive mixed models with automatic estimation of the smoothness of model components. Changes in this version are: * A new gam fitting method is implemented for the generalized case. It provides more reliable convergence than the previous default, but can be a little slower. See ?gam.method,

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 dti is a new contributed package for R, that implements functions for smoothing Diffusion-weighted MR data with structural adaptive smoothing methods. Version 0.5-2 contains functions for smoothing DW data in the context of the Diffusion Tensor Model and for visualization of Diffusion Tensor Data and anisotropy maps derived thereof. The tensor

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 dti is a new contributed package for R, that implements functions for smoothing Diffusion-weighted MR data with structural adaptive smoothing methods. Version 0.5-2 contains functions for smoothing DW data in the context of the Diffusion Tensor Model and for visualization of Diffusion Tensor Data and anisotropy maps derived thereof. The tensor

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"

I upgraded rsync from 2.3.1 to 2.5.5, and now one of my nightly jobs fails with building file list ... done rsync: connection unexpectedly closed (11980581 bytes read so far) rsync error: error in rsync protocol data stream (code 12) at io.c(150) rsync: connection unexpectedly closed (8 bytes read so far) rsync error: error in rsync protocol data stream (code 12) at io.c(150)

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

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

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