Displaying 20 results from an estimated 700 matches similar to: "Package Install Problem under Win98"
2003 Jan 03
4
factor analysis (pca): how to get the 'communalities'?
Dear expe-R-ts,
I try some test data for a factorAnalysis (resp. pca) in the sense of Prof.
Ripley's MASS ? 11.1, p. 330 ff., just to prepare myself for an analysis of my
own empirical data using R (instead of SPSS).
1. the data.
## The test data is (from the book of Backhaus et al.: Multivariate ##
Analysemethoden. Springer 2000 [9th ed.], p. 300 ff):
2001 Apr 27
1
INSTALL Problems
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= \
1999 Jul 20
2
tensor() function and sets
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
2006 Nov 07
1
gamm(): nested tensor product smooths
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
2012 Jul 17
2
Problem creation tensor
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
2012 Jul 30
2
mgcv 1.7-19, vis.gam(): "invalid 'z' limits'
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
2008 Dec 03
1
Taking slices with tensorA
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
2008 Jul 29
1
tensor product of equi-spaced B-splines in the unit square
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
2009 Mar 11
1
matrix multiplication, tensor product, block-diagonal and fast computation
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
2012 Apr 02
1
gamm: tensor product and interaction
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
2011 Sep 20
2
Multivariate spline regression and predicted values
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
2010 Jul 02
4
Some questions about R's modelling algebra
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"
2011 Jun 07
2
gam() (in mgcv) with multiple interactions
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
2001 Apr 24
1
New Package Released: PTAk
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
2001 Apr 24
1
New Package Released: PTAk
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
2013 Aug 23
1
Setting up 3D tensor product interactions in mgcv
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
2023 Aug 06
1
Stacking matrix columns
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
2007 Jun 21
1
mgcv: lowest estimated degrees of freedom
Dear list,
I do apologize if these are basic questions. I am fitting some GAM
models using the mgcv package and following the model selection criteria
proposed by Wood and Augustin (2002, Ecol. Model. 157, p. 157-177). One
criterion to decide if a term should be dropped from a model is if the
estimated degrees of freedom (EDF) for the term are close to their lower
limit.
What would be the
2023 Aug 06
1
Stacking matrix columns
Eric,
I am not sure your solution is particularly economical albeit it works for arbitrary arrays of any dimension, presumably. But it seems to involve converting a matrix to a tensor just to undo it back to a vector. Other solutions offered here, simply manipulate the dim attribute of the data structure.
Of course, the OP may have uses in mind which the package might make easier. We often get
2006 Feb 27
3
how to use the basis matrix of "ns" in R? really confused by multi-dim spline filtering?
Hi all,
Could anybody recommend some easy-to-understand and example based
notes/tutorials on how to use cubic splines to do filtering on
multi-dimension data?
I am confused by the 1-dimensional case, and more confused by
multi-dimensional case.
I found all the books suddenly become very abstract when it comes to this
subject.
They don't provide examples in R or Splus at all.
Specifically,