similar to: Initial values of the parameters of a garch-Model

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.

Dear R-tisans, I am trying to calculate the 12th root of a transition (square) matrix, but can't seem to obtain an accurate result. I realize that this post is laced with intimations of quantitative finance, but the question is both R-related and broadly mathematical. That said, I'm happy to post this to R-SIG-Finance if I've erred in posting this to the general list. I've

Hello, I have used R in the past to conduct multiple comparisons on standard linear models, but am a bit confused as to how to go about doing it with a mixed effects model. I am conducting a bioindication study using carabid beetles in which I have four treatment types (forest harvest types with varying levels of canopy structure retention), and am using canopy closure percent as a covariate in

Disclaimer : Short of having local statistical expertise at hand, I'm using this list because I use R for variable selection in the context of linear multiple regression but the questions I have relate more to basic statistics than to R per se. Please redirect me to another appropriate list if such a list exists. I have the very common problem of identifying which (subset of) variables are

Hi all: Can someone advice me on how to hold the residuals Mean sq value on a string so it can be used in other calculations. I was trying something like this: Msquare<-dfr$Mean sq but fails..Thanks dfr <- read.table(textConnection("percentQ Efficiency 1.565 0.0125 1.94 0.0213 0.876 0.003736 1.027 0.006 1.536 0.0148 1.536 0.0162 2.607 0.02 1.456 0.0157 2.16 0.0103

Hi all: I have always used SPSS to estimate weekly covariance based on a linear regression model but have to hard code the model Std. Error and the Mean-Square and then execute one week a the time. I was wondering if someone could give me an idea on how to estimate weekly(WK) covariance using the summary and anova of "dfr"(lineal model below). I have to do this for 52

On Jul 24, 2011, at 3:02 AM, Duncan Sands wrote: > A big compile time regression. Any ideas? > > Ciao, Duncan. False alarm. For some reason that I have not yet been able to figure out, these tests run significantly more slowly when I run them during the daytime, which I did for that run. I checked a few of the worst regressions reported here and they all recovered in subsequent

Greetings R-ers, A colleague and I have been exploring the behaviour of glmmPQL in R and S-PLUS 6 and we appear to get different results using the same code and the same data set, which worries us. I have checked the behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the results are the same, but differ from S-PLUS 6 with the latest Mass and nlme libraries (Windows XP). Here

Hello, I have two data frames, X and Y, with two columns each and different numbers of rows. # creation of data frame X Loc1.alleles <- c(1,5,6,7,8) Loc1.Freq <- c(0.35, 0.15, 0.05, 0.10, 0.35) Loc1 <- cbind( Loc1.alleles,Loc1.Freq) X <- data.frame(Loc1) #creation of data frame Y Loc2.alleles <- c(1,4,6,8) Loc2.Freq <- c(0.35, 0.35,

Hello, my question is about the data handling. I have a data set that is lined as: 4 1 17 1 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445 -0.0578 -0.1175 -0.1232 0.8673 -0.1033 -0.0796 -0.0341 -0.1716 -0.1801 -0.7014 0.6578 0.5611 4 1 17 2 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445

I am new to R (ex SAS user) , and I cannot merge two data frames without getting duplicated rows in the results. How to avoid this happening without using the unique() function? 1. First data frame is called "tmv" with 6 variables and 239 rows: > tmv[1:10,] temps nom prenom sexe dist style 1 01:59:36 Cyr Steve H 45 free 2 02:09:55 Gosselin

dear sir, my data larger than this example but is of the following format: y x Age 30 0.0323 O 24 0.0389 Y 158 0.058 Y 120 0.0581 O 100 0.0471 Y 102 0.0615 Y 160 0.0546 O i ma making a scatter plot of y~x and want to specify different coloured and filled shaped for the points according the the third categorical variable A. the code i have managed is : plot(y~x,pch=as.numeric(factor(Age)))

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04

hello all, Quick question, how do I get the p value out of the anova? Thanks, Paul > pb<-aov(as.numeric(diff[5,16:33]) ~ grF) > summary(pb) Df Sum Sq Mean Sq F value Pr(>F) grF 3 2.7860e+10 9.2867e+09 4.2236 0.02534 * Residuals 14 3.0783e+10 2.1988e+09 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '

Dear list members, I have a 2-factor ANOVA where the summary.lm output looks like this (using treatment contrasts): Value Std. Error t value Pr(>|t|) (Intercept) 0.0389 0.0220 1.7695 0.0817 as.factor(Block)1 0.0156 0.0066 2.3597 0.0215 as.factor(Block)2 -0.0018 0.0037 -0.4857 0.6289 as.factor(Block)3 -0.0007 0.0026 -0.2812 0.7795

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

Hi, I'm wondering if you can help me, this is a really simple query but I keep getting confused. I have run a GLM to see how boldness varies over time following a particular treatment. The results are as follows... Call: glm(formula = boldtwentyfour ~ treatment + boldcontrol) Deviance Residuals: Min 1Q Median 3Q Max -1.7577 -0.5469 0.0456 0.5515 1.5327