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Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.

I get some failing tests from compiler-rt. FAIL: cfi :: cross-dso/stats.cpp (30831 of 30893) ******************** TEST 'cfi :: cross-dso/stats.cpp' FAILED ******************** Script: -- /usr/local/google/home/prazek/llvm-build-release/./bin/clang -fuse-ld=gold -flto -fsanitize=cfi -fwhole-program-vtables --driver-mode=g++ -fsanitize-cfi-cross-dso -fvisibility=default -DSHARED_LIB -fPIC

Hi all! I want to cross compile openssh to our own CPU and our embedded Linux platform, however I get at least the following errors when running configure: checking whether snprintf correctly terminates long strings... configure: error: can not run test program while cross compiling checking whether getpgrp takes no argument... configure: error: cannot check getpgrp if cross compiling checking

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

Hi all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independent testing set, can I just use the

Hello all, * * I'm having some difficulty, and I think the problem is with how I'm using append() nested inside a for loop. The data are: y,x 237537.61,873 5007.148438,227 17705.77306,400 12396.64369,427 228703.4021,1173 350181.9752,1538 59967.79376,630 140322.7774,710 42650.07251,630 5382.858702,264 34405.82429,637 92261.34614,980 144927.1713,1094 362998.7355,1420 203313.6442,1070

Compiling llvm-gcc on an x86 machine with --target=powerpc-ibm-aix5.1.0.0, I get the following error: make \ CFLAGS="-g -O2 -W -Wall -Wwrite-strings -Wstrict-prototypes -Wmissing-prototypes -pedantic -Wno-long-long -Wno-variadic-macros -Wno-overlength-strings -Wold-style-definition -Wmissing-format-attribute " \ CONFIG_H="config.h auto-host.h

Dear R-help, How could a cross-correlation plot be optimized such that the relationship between seasonal time-series can be studied? We are working with strong seasonal time-series and derived a cross-correlation plot to study the relationship between time-series. The seasonal variation however strongly influences the cross-correlation plot and the plot seems to be ?rather? symmetrical (max

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

Hi It clear that to do a classification with svm under 10-fold cross validation one uses svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross = 10) What corresponds to the nnet? nnet(.....,cross=10)? Regards

Hi list, Could someone help me to explain why the leave-one-out cross validation results I got from svm using the internal option "cross" are different from those I got manually? It seems using "cross" to do cross validation, the results are always better. Please see the code below. I also include lda as a comparison. I'm using WinXP, R-2.9.0, and e1071_1.5-19. Many

You could try using zig for this. Here's an example build script of zig cross compiling llvm, lld, clang, and finally itself: https://github.com/ziglang/zig-bootstrap You can see the build script is not too complicated. The main trick is using CC and CXX to use `zig cc` and `zig c++` as drop-in replacements for a C and C++ compiler, respectively. I wrote some more details about this

Hello group, For my PhD I'm working on a spatial sampling grid. I do have two data sets which I'd like to compare using cross-correlograms or cross-variograms. Is this an option in one of the R-packages? I've been searching the R-help archive and the available package-documentations, but I can't find how to do this. Thanks in advance, Ren?.

Now I believe that I am getting the same error as I was getting last time I tried to build llvm-gcc. It may possibly be my Cygwin instillation but am not sure. makeinfo --split-size=5000000 -I ../../../src/llvm-gcc/gcc/doc -I ../../../src/ llvm-gcc/gcc/doc/include \ -o ../../../src/llvm-gcc/gcc/doc/cpp.info ../../../src/llvm-gcc/gcc/doc/cpp.texi ' in

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

Hi everyone, I recently sent a message that may or may not have made it to the list, on problems building an LLVM cross-compiler. Now the issue seems clear to me and is irrelevant at this point, I would like to discuss the general issue. Suppose you have a host system, a Linux distribution on an x86_64 machine, and want to build a cross-compiler for aarch64 (or any platform supported by LLVM,

Hi everyone, I would like to compute the sum of cross products of rows of a matrix. Does anyone know how to do this without an explicit loop for computational efficiency? Below is a code example. Thanks! Stephan # I.e., if I have a matrix like this x <- matrix(rep(c(1,2,3),3),ncol=3) ## > x ## [,1] [,2] [,3] ## [1,] 1 1 1 ## [2,] 2 2 2 ## [3,] 3 3

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

There seem to be some recent regressions in clang/llvm. I see some test failing, and there is also this issue: https://llvm.org/bugs/show_bug.cgi?id=30466 Is anyone working on it? Piotr -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20160920/ade5e8cd/attachment.html>

Hi all, I'm cross-compiling speex for a GNU/Linux ARM target. I want to compile only the libraries, and not the speex executables. I don't have a libogg cross-compiled, and would prefer the need to have to set it up. Unfortunately: 1/ the speex executables depend on libogg header files, although, the speex executables are only compiled if the libogg header files are available 2/ the