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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 <- vector(length=300) noise...

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 other decomp method on the back end that is less sensitive than Choles...

...x file for reference. Again, if I use the fields function, as is, I get the message: Error in svd(tempM) : error 159 in dsvdc using traceback, I get: > traceback() 4: stop(paste("error ", z$info, " in dsvdc")) 3: svd(tempM) 2: Krig(x, Y, cov.function = rad.cov, m = m, decomp = decomp, scale.type = scale.type, outputcall = Tpscall, p = p, ...) 1: Tps(bvolcap, bdsm) > if I change the occurrence of svd in the fields package to La.svd, I get the error message > bout <- Tps( bvolcap, bdsm) Error in "[<-"(*tmp*, (nt + 1):np, (nt + 1):np,...

...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", "#6a51a3")) dev.off() > dput(head(d1)) structure(list(Domain.decomp. = c("2. 1", "2"), DD.com..load = c(0L, 0L), Neighbor.search = c("3.7", "3. 1"), Launch.PP.GPU.ops. = c("0. 1", "0"), Comm..coord. = c("1 .6", "1 .0"), Force = c("1 . 5", "1 .2" ), Wait...Comm..F = c...

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 Force Wait...Comm..F PIE.mesh Wait.Bonded.GPU wait.GPU.NB.nonloc. 1 1 . 5...

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...

...9;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 plot(dep12spp13ph1$DAYSWETm2,dep12spp13ph1$AFTERDECOMP,pch=dep12spp13ph1$spp,cex=0.75,ylab="Proportion of biomass after leaching", xlab="Number of days in wetland",xlim=c(0,250),ylim=c(0,1)) xplot<- 0:243 xplotbsk64deg1<-bs(xplot,knot=62,degree=1,Boundary.knots=c(0,243)) lines(xplot,exp(xplotbsk64deg1 %*% ph1.decomp.75.bs$c...

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(1...

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 other...

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] = mat[1]; lmat[2]...

...double gsl_MB_mvnorm_logpdf(gsl_vector * beta, gsl_vector * betaMean, gsl_matrix * sigma, int k) { // computes density of multivariate normal vector at vector beta, with mean betaMean and cov sigma double logdetSigma = 0; double res; double * kern; int i, err; // pointer to Cholesky decomp of sigma gsl_matrix * sigmaChol = gsl_matrix_alloc(k, k); // define matrix that will store Chol decomp gsl_matrix_memcpy(sigmaChol, sigma); gsl_linalg_cholesky_decomp(sigmaChol); // compute logdet of sigma by 2*sum of log of diagomal elements of chol decomp for (i=0; i<k; i++) { lo...

...data types > header1 <- readBin(zz, integer(), 8, size = 4) > header2 <- readBin(zz, numeric(), 5, size = 8) > header3 <- readBin(zz, integer(), 14, size = 4) > data.raw <- readBin(zz, raw(), 1e6) > close(zz) > ## it works fine up to here > mydata <- .Call('R_decompress1', data.raw) Error: cannot allocate vector of size 1976123 Kb In addition: Warning messages: 1: Reached total allocation of 1534Mb: see help(memory.size) 2: Reached total allocation of 1534Mb: see help(memory.size) > length(data.raw) [1] 481 The .Call is, where R complains about not hav...

...rm(100) + x1/5 x3 <- rnorm(100) + x2/5 x4 <- rnorm(100) + x3/5 # find current correlations cor1 <- cor( cbind(x1,x2,x3,x4) ) cor1 # create 1st version of z z <- rnorm(100) # combine in a matrix m1 <- cbind( x1, x2, x3, x4, z ) # center and scale m2 <- scale(m1) # find cholesky decomp c1 <- chol(var(m2)) # force to be independent m3 <- m2 %*% solve(c1) # create new correlation matrix: cor2 <- cbind( rbind( cor1, z=c(.5,.3,.1,.05) ), z=c(.5,.3,.1,.05,1) ) # create new matrix m4 <- m3 %*% chol(cor2) # uncenter and unscale m5 <- sweep( m4, 2, attr(m2, 'scaled...

...H0 defines p-value and it might relate to the strength of association somehow. But I read the following statement from Alan Agresti's "An Introduction to Categorical Data Analysis" : "Chi-squared tests simply indicate the degree of EVIDENCE for an association....It is sensible to decompose chi-squared into components, study residuals, and estimate parameters such as odds ratios that describe the STRENGTH OF ASSOCIATION". Can I do this "decomposition" in R for the following example including 2 contingency tables? > tab1<-array(c(11266, 125, 2151526, 31734), d...

...+ return r; + ssh->state->compress_zstd_out_comp += out_buff.pos; + } while (comp > 0); + return 0; +} + +static int uncompress_buffer_zstd(struct ssh *ssh, struct sshbuf *in, + struct sshbuf *out) +{ + u_char buf[4096]; + ZSTD_inBuffer in_buff; + ZSTD_outBuffer out_buff; + int r, decomp; + + if (ssh->state->compression_in_started != COMP_ZSTD) + return SSH_ERR_INTERNAL_ERROR; + + in_buff.src = sshbuf_mutable_ptr(in); + if (in_buff.src == NULL) + return SSH_ERR_INTERNAL_ERROR; + in_buff.size = sshbuf_len(in); + in_buff.pos = 0; + ssh->state->compress_zstd_in_comp += i...

...an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need to decompose that mixture into its 2 components which will need to be plotted. What I don't know how to do is: (1) restrict the number of components to 2 in the "EMclust" function (2) obtain and plot a component gaussian density Regarding (1), I think this should be the 'G' value b...

I added ZSTD support to OpenSSH roughly three years ago and I've been playing with it ever since. The nice part is that ZSTD achieves reasonable compression (like zlib) but consumes little CPU so it is unlikely that compression becomes the bottle neck of a transfer. The compression overhead (CPU) is negligible even when uncompressed data is tunneled over the SSH connection (SOCKS proxy, port

...xh 1.0 -resethway -noconfout", "gmx mdrun -ntmpi 8 -ntomp 1 -s benchPEP.tpr -nsteps -1 -maxh 1.0 -resethway -noconfout" ), Annee = c("SYCL", "CUDA", "SYCL", "CUDA", "SYCL", "CUDA", "SYCL", "CUDA"), Domain.decomp. = c("2. 1", "2", "2. 1", "2. 1", "2.1", "2", "2. 1", "2"), DD.com..load = c(0, 0, 0, 0, 3.7, 3, 0, 0), Neighbor.search = c("3.7", "3. 1", "3.7", "3.9", "0. 1", "O....

...n Fri, Nov 21, 2014 at 1:16 AM, Andy Ritger <aritger at nvidia.com> wrote: >>>>> Hi Ilia, >>>>> >>>>> Actually 0x90b8 is different than copy engine. I'm not very familiar >>>>> with it, but 0x90b8 is an engine for performing LZO decompression as >>>>> part of performing the copy. It has a variety of limitations (e.g., >>>>> cannot handle blocklinear format), and was only in a few Fermi chips, >>>>> as I understand it. >>>> >>>> According to our driver source, G...

This isn't the help list for LAPACK, but as far as I can tell, dppsv expects a symmetric matrix input compacted as triangular, not a Choleski decomposed one. So try assigning lmat before the call to dpotrf. -pd > On 20 Dec 2014, at 22:06 , Pierrick Bruneau <pbruneau at gmail.com> wrote: > > Dear R contributors, > > Considering the following sample C code, that illustrates two possible > uses of a Cholesky decomp for...