similar to: tensor() function and sets

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 suppose I have two arrays x1,x2 of dimensions a1,b1,c1 and a2,b2,c2 respectively. I want x = x1 "+" x2 with dimensions c(max(a1,a2), max(b1,b2),max (c1,c2)) with x[a,b,c] = x1[a1,b1,c1] + x2[a2,b2,c2] if a <=min(a1,a2) , b<=min (b1,b2), c<=min(c1,c2) and the other bits either x1 or x2 or zero according to whether the coordinates are "in range" for

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 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

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, 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

I would like to suggest a slight modification to diag(). In the case where x is a matrix with both row names and column names the same, it would be reasonable if the resulting vector also had those names. I often use diag() on variance matrices, where this modification is helpful. The modification requires replacing if (is.matrix(x) && nargs() == 1) return(c(x)[1 +

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, 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

Hi Ahmed, When reading from a ncdf file you can use the 'varname', 'lvar' and 'level' arguments - see the 'Details' section in the docs https://www.rdocumentation.org/packages/raster/versions/2.6-7/topics/raster <https://www.rdocumentation.org/packages/raster/versions/2.6-7/topics/raster> We can't tell what is in the ncdf file from what you report other

Hi, I'm working on R trying to find a distribution that fits data from a csv file. The csv contains data on pressure exerted by a certain vehicle in terms of pressure [kPa] and I have around 3000 data points. I want to call forth this csv and by using (fitdistr) or if you could recommend a function to use, get a plot of my csv and the distributions I can compare it to (Weibull, chi, beta,

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

Hello, I am trying to create data frame subsets based on binned temperature data. I have code working to create the bins (d.1 and d.2), but it takes two steps, I was wondering if I could merge into one step. See Below d n year mo da hr t td tw rh kPa 1 1 1945 3 1 0 1.1 0.0 0.6 92 101.7 2 2 1945 3 1 1 2.8 -1.1 1.1 76 101.8 3 3 1945 3 1 2 2.2 -1.7 0.6 75 101.9 4 4

Hi I have a netCDF file of volumetric soil water content at four different soil layers and want to convert each soil layer in the netCDF file to a GeoTIFF layer. This code converts the netCDF file to one GeoTIFF layer, i.e. unclear which soil depth. file.nc <- "C:/Soil_Weather_data/Agro/VMC21/VMC21.nc" file.tiff <- "C:/Soil_Weather_data/Agro /VMC21/VMC21" importnetcdf

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"

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

I'm starting a new thread on this, because I have more concrete information than I did the first time around. The full rebalance log from the machine where I started the rebalance can be found at the following link. It is slightly redacted - one search/replace was made to replace an identifying word with REDACTED. https://dl.dropboxusercontent.com/u/97770508/mdfs-rebalance-redacted.zip