similar to: caching frequently used values

I generally run 'make; make check' (with more settings) when building the Debian package. Running 3.2.4 rc from last night, I see a lot of package loading issues during 'make check'. Here is splines as one examples: checking package 'splines' * using log directory '/build/r-base-3.2.3.20160303/tests/splines.Rcheck' * using R version 3.2.4 RC (2016-03-02 r70270) *

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

--- On Mon, 3/12/12, aleksandr shfets <a_shfets at mail.ru> wrote: > From: aleksandr shfets <a_shfets at mail.ru> > Subject: Fwd: Re[2]: [R] B-spline/smooth.basis derivative matrices > To: "Vassily Shvets" <shv736 at yahoo.com> > Received: Monday, March 12, 2012, 5:15 PM > > > > -------- ???????????? ????????? > -------- > ?? ????:

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

Hello, I've noticed that SPLUS seems to have a function for evaluating derivative matrices of splines. I've found the R function that evaluates matrices from 'smooth.spline'; maybe someone has written something to do the same with smooth.basis? regards, s

Dear R developers, The trace of the hat matrix H~(n,n) is computed as follows: tr(H) = tr(BS^-1B') = tr(S^-1B'B) := tr(X) = sum(diag(X)) with B~(n,p), S~(p,p). Since p is of the order 10^3 but S is sparse I would like to employ Taucs linear solver ( http://www.tau.ac.il/~stoledo/taucs/ ) on SX = B'B. (Further improvement by implying a looping over i=1,...,p, calling

Thanks for the info, Dirk. The tarball builds don't run make check (because of a policy decision that it is better to have the sources available on all platforms for testing than to have none if it breaks on a single platform). However the build as of tonight has no problem with make check on the build machine. Did you by any chance forget that Matrix is a recommended package and expected to

Hi, I would like to fit an (interpolating) spline to data where the derivatives at the endpoints of the interval are nonzero, thus the natural spline endpoint-specification does not make sense. Books (de Boor, etc) suggest that in this case I use not-a-knot splines. I know what not-a-knot splines are (so if I were solving for the coefficients directly I knew how to do this), but I don't

On 4 March 2016 at 09:11, peter dalgaard wrote: | Thanks for the info, Dirk. | | The tarball builds don't run make check (because of a policy decision that it is better to have the sources available on all platforms for testing than to have none if it breaks on a single platform). However the build as of tonight has no problem with make check on the build machine. Did you by any chance forget

On 08/02/2012 05:00 AM, r-devel-request at r-project.org wrote: > Now I just have to grovel over the R code in ns() and bs() to figure > out how exactly they pick knots and handle boundary conditions, plus > there is some code that I don't understand in ns() that uses qr() to > postprocess the output from spline.des. I assume this is involved > somehow in imposing the boundary

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

This is a low priority bug that has been around for a while, but I came acr= oss it again while alpha testing 2.8. The resulting function for splinefun gives incorrect deriv values when x is= less than the smallest x-value used to create the function (at least in on= e circumstance), but does the correct thing for x greater than the largest = x-value. Here is an example: > x <- 1:10 >

Hi, Let's play a little game. Forget all you know, or think you know. Without any context at all, what does the following pattern make you think of? <p> X -> X X -> X | ^ v | X <- X X <- X | ^ v | X X -> X X | ^ | ^ v | v | X -> X

Dear R-users, I would like to calculate elasticities and sensitivities of each parameters involved in the following transition matrix: A <- matrix(c( sigma*s0*f1, sigma*s0*f2, s, v ), nrow=2, byrow=TRUE,dimnames=list(stage,stage)) The command "eigen.analysis" avaliable in package "popbio" provides sensibility matrix and elasticity matrix

HI there, I have been trying to use a code posted on R help to be able to calculate area under the curve for complicated data points and there seems to be an issue with the code: no "b" object found. I am not a good R user and can''t find were the problem is. Any help? Thanks!! This is the code ( as a test run I gave it this info because I know the answer: x<-seq(1:50)

The build system rolled up R-3.4.2.tar.gz (codename "Short Summer") this morning. The list below details the changes in this release. You can get the source code from http://cran.r-project.org/src/base/R-3/R-3.4.2.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For the R Core Team, Peter Dalgaard

The build system rolled up R-3.4.2.tar.gz (codename "Short Summer") this morning. The list below details the changes in this release. You can get the source code from http://cran.r-project.org/src/base/R-3/R-3.4.2.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For the R Core Team, Peter Dalgaard

I like what smooth.spline does but I am unclear on the output. I can see from the documentation that there are fit.coef but I am unclear what those coeficients are applied to.With spline I understand the "noraml" coefficients applied to a cubic polynomial. But these coefficients I am not sure how to interpret. If I had a description of the algorithm maybe I could figure it out but as it

Dear Martin, I have been playing a lot with the glkerns() function in the "lokern" package for "automatic" smoothing of time-series data. This kernel smoothing approach of Gasser and Mueller seems to perform quite well for estimating the function and its derivatives (first and second derivatives). In fact, this is one of the best methods based on my simulation studies for

Hello, and please excuse this off-topic question, but I have not been able to find an answer elsewhere. Consider a value Z that is calculated using the product (or ratio) of two means X_mean and Y_mean: Z=X_mean*Y_mean. More generally, Z=f(X_mean, Y_mean). The standard error of Z will be a function of the standard errors of the means of X and Y. I want to calculate this se of Z. Can someone