similar to: Error in Fields TPS function {svd ...} again

Hello, I was wondering whether anyone out there knows of the solution to a problem that I'm having with the Fields package. I am getting the error message when I try and run the fields function tps (thin plate splines). Namely, for two different sets of variables, I get: > bout <- Tps( bvolcap, bdsm) Error in svd(tempM) : error 159 in dsvdc > wout <- Tps( wvolcap, wdsm)

Hi, I am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

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Hello list-mailers, I'm trying to use the 'Tps' function from the 'fields' package and I can getting this error message: > fishplot <- Tps(nodes.catch, res2) Error in if ((il > 1) & (il < nstep.cv)) { : missing value where TRUE/FALSE needed In addition: Warning message: no non-missing arguments to min; returning Inf nodes.catch is a 1600x2 matrix and

Hello there, I am using the Tps function to fit a spline to my data which is measurements of a sedimentary layer on an xy plane to see how the thickness of the layer changes. I was wondering if it was possible to change the Tps code so that no minus values are calculated by the spline as these are obviously unfeasible. Any help would be much appreciated, Thanks, Sam -- View this message in

Hi, I am trying to use the Tps procedure to visualize 3 variables in XYZ space, but keep getting the error messages below: Warning messages: 1: In Krig.find.gcvmin(info, lambda.grid, gcv.grid$GCV, Krig.fgcv, : GCV search gives a minimum at the endpoints of the grid search 2: In Krig.find.gcvmin(info, lambda.grid, gcv.grid$GCV.one, Krig.fgcv.one, : GCV search gives a minimum at the endpoints

CC'ing virtualization list. On (Thu) 31 Mar 2016 [19:00:24], Dr. David Alan Gilbert wrote: > Hi, > I was reading the balloon code and am confused as to how/if ballooning > works on hosts where the host page size is larger than the > target page size. > > static void balloon_page(void *addr, int deflate) > { > #if defined(__linux__) > if

CC'ing virtualization list. On (Thu) 31 Mar 2016 [19:00:24], Dr. David Alan Gilbert wrote: > Hi, > I was reading the balloon code and am confused as to how/if ballooning > works on hosts where the host page size is larger than the > target page size. > > static void balloon_page(void *addr, int deflate) > { > #if defined(__linux__) > if

Dear all, I'm a beginner in spatial regression, therefore, excuse me in advance for the stupid question. I'm using the function Tps of field library. As I read in help, the assumed model for this regression is additive Y = f(X) + e where f(X) is a d dimensional surface. I have understood that d in not an argument to specify in Tps(x, Y, ...). How can I obtain d ? Thanks a lot.

Hi, I have a 4 column file (long/lat/elev/variable) and I tried to fit the values of my variable to the XYZ space using Tps and I keep getting the following message: Warning messages: 1: GCV search gives a minumum at the endpoints of the grid search in: Krig.find.gcvmin(info, lambda.grid, gcv.grid$GCV, Krig.fgcv, 2: GCV search gives a minumum at the endpoints of the grid search in:

Hi mailinglist members, I’m actually working on a time series prediction and my current approach is to decompose the series first into a trend, a seasonal component and a remainder. Therefore I’m using the stl() function. But I’m wondering how to get the single components in order to predict the particular fitted series’. This code snippet illustrates my problem: series <-

Dear all: In what I am doing I sometimes get a (Hessian) matrix that has a couple of tiny negative eigenvalues (e.g. -6 * 10^-17). So, I can't run a Cholesky decomp on it - but I need to. Is there an established way to regularize my (Hessian) matrix (e.g., via some transformation) that would allow me to get a semi-positive definite matrix to be used in Cholesky decomp? Or should I try some

Thank you so much David, here is correction: d1=suppressWarnings(read.csv("/Users/anamaria/Downloads/B1.csv", stringsAsFactors=FALSE, header=TRUE)) d1$X <- NULL d2=as.matrix(sapply(d1, as.numeric)) pdf("~/graph.pdf") b<-barplot(d2, legend= c("SYCL", "CUDA"), beside= TRUE,las=2,cex.axis=0.7,cex.names=0.7,ylim=c(0,80), col=c("#9e9ac8",

Hello, I have a data frame like this: d11=suppressWarnings(read.csv("/Users/anamaria/Downloads/B1.csv", stringsAsFactors=FALSE, header=TRUE)) > d11 X Domain.decomp. DD.com..load Neighbor.search Launch.PP.GPU.ops. Comm..coord. 1 SYCL 2. 1 0 3.7 0. 1 1 .6 2 CUDA 2 0 3. 1 0 1 .0

Is there any way to give a "decent" title after I plot something generated by decompose? For example: # generate something with period 12 x <- rnorm(600) + sin(2 * pi * (1:600) / 12) # transform to a monthy time series y <- ts(x, frequency=12, start=c(1950,1)) # decompose z <- decompose(y) # plot plot(z) Now, the title is the ugly "Decomposition of additive time

I have a recurring graphics issue that I've not been able to resolve with R. If I make a series of regression estimates and then plot the estimated function for the regression lines over a scatter plot of the data, e.g., using a sequence of plot( ) and lines ( ) similar to those below

I need to perform a QR-decomposition of a sparse matrix, so I've been trying to use the Matrix package. Unfortunately I don't seem to be getting exactly the same results as if I had used the qr() command from the base package. Here is an example of what I'm doing. > spdata <-rpois(50,1) > y <- rnorm(10,0,1) > S <-

I have been struggling to find some informaation on what lm exactly does. I know it uses the QR decomp. However, I was recently faced with a somewhat badly scaled matrix and summary(lm) said Coefficients: ( 4 not defined because of singularities) does anyone know how lm chooses these 4 coef. is it forward building of the model --> drop last when qr sends a non full rank design matrix? My

Dear R contributors, Considering the following sample C code, that illustrates two possible uses of a Cholesky decomp for inverting a matrix, equally valid at least in theory: SEXP test() { int d = 2; int info = 0; double mat[4] = {2.5, 0.4, 0.4, 1.7}; double id[4] = {1.0, 0.0, 0.0, 1.0}; double lmat[3]; F77_CALL(dpotrf)("L", &d, mat, &d, &info); lmat[0] = mat[0]; lmat[1]

I am experiencing some odd behavior with the .Call interface, and I am hoping someone out there can help me with it. Below is a simple example (in that there are R packages that do exactly what I want), but this code illustrates the problem. Thanks in advance for any help you can provide. Suppose I want to compute the log density of a multivariate normal distribution using C code and the gsl