search for: 0.0016

Hi I am trying to do this: chisq.test(c(11, 13, 12, 18, 21, 43, 15, 12, 9, 10, 5, 28, 22, 11, 15, 11, 18, 28, 16, 8, 15, 19, 44, 18, 11, 23, 15, 23, 2, 5, 4, 14, 3, 22, 9, 0, 6, 19, 15, 32, 3, 16, 14, 10, 24, 16, 24, 31, 29, 28, 16, 26, 11, 11, 4, 17, 16, 13, 20, 26, 16, 19, 34, 19, 17, 14, 22, 25, 17, 12, 23, 14, 19, 30, 18, 10, 23, 21, 17, 16, 10, 14, 6, 17, 17, 10, 21, 25, 20, 4, 11, 4,

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

Dear R Helpers, I need to find the root of following equation. 0.0016^2 = (0.001*x)^2 + (0.002 * (1-x))^2 + 2 * 0.7 *0.001*0.002 * x * (1-x). I had tried using "animation " package as follows. # My Code library(animation) ani.options(nmax = 500) solu = newton.method(function(x) 0.0016^2 - 0.001^2*x^2 - 0.002^2*(1-x)^2 - 2*0.7*0.001*0.002*x*(1-x), 1, c(-1,1)) solu$root #

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

Hi I noticed a small inconsistency when using sum() vs cumsum() I have a char-based series > tryjpy$long [1] "0.0022" "-0.0002" "-0.0149" "-0.0023" "-0.0342" "-0.0245" "-0.0022" [8] "0.0003" "-0.0001" "-0.0004" "-0.0036" "-0.001" "-0.0011"

In SAS, for a two-way (or 3-way, stratified) table, the CMH option in SAS PROC FREQ gives 3 tests that take ordinality of the factors into account, for both variables, just the column variable or neither. Is there an equivalent in R? The mantelhaen.test in stats gives something quite different (a test of conditional independence for *nominal* factors in a 3-way table). e.g. I'd like to

Did something change with exception handling recently? A bunch of lit bots are showing slower compile times for many tests. Ciao, Duncan. On 20/06/12 07:53, llvm-testresults at cs.uiuc.edu wrote: > > lab-mini-03__O0-g__clang_DEV__x86_64 test results > <http://llvm.org/perf/db_default/v4/nts/1283?compare_to=1278&baseline=999> > > Run Order Start Time Duration >

Hello All, I posted a similar question before, but the direction was driven to whether my case is suitable for a wilcoxon test. After research about the appropriateness, I am pretty sure that a wilcoxon test is the right tool for my case. But how to compute the power of the test is still an unanswered question bothering me. The basic stats of my two paired samples are: mean1 = 0.0032, sd1 =

LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When

Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? -bw On Jun 20, 2012, at 12:38 AM, Duncan Sands <baldrick at free.fr> wrote: > Did something change with exception handling recently? A bunch of lit bots are > showing slower compile times for many tests. > > Ciao, Duncan. > > On 20/06/12 07:53, llvm-testresults at cs.uiuc.edu

Hi, I am trying to fit a model with lmer in R and proc glimmix in SAS. I have simplified my code but I am surprised to see I get different results from the two softwares. My R code is : lmer(y~age_cat + (1|cat),data=fic,family=binomial(link = "logit"), NaGQ=1) My SAS code is : ods output Glimmix.Glimmix.ParameterEstimates=t_estimates; proc glimmix data=tab_psi method=laplace;

Hi Bill, > Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? I don't see any relevant LLVM changes, so I guess clang C++ compilation slowed down due to some clang changes. I'm not going to investigate this. Ciao, Duncan. > > -bw > > On Jun 20, 2012, at 12:38 AM, Duncan Sands <baldrick at free.fr> wrote: > >> Did

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do

>>>>> Tomas Kalibera >>>>> on Tue, 25 Aug 2020 09:29:05 +0200 writes: > On 8/23/20 5:02 PM, Rory Winston wrote: >> Hi >> >> I noticed a small inconsistency when using sum() vs cumsum() >> >> I have a char-based series >> >> > tryjpy$long >> >> [1]

I am getting the following errors when I am trying to plot the data below. I cannot figure out the error. Error in plot.window(...) : need finite 'xlim' values In addition: Warning messages: 1: In min(x) : no non-missing arguments to min; returning Inf 2: In max(x) : no non-missing arguments to max; returning -Inf 3: In min(x) : no non-missing arguments to min; returning Inf 4: In max(x) :

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

On Jul 5, 2012, at 1:33 AM, Duncan Sands wrote: > Hi Bill, > >> Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? > > I don't see any relevant LLVM changes, so I guess clang C++ compilation slowed > down due to some clang changes. I'm not going to investigate this. > Crumbs. John, Do you know of anything that went into

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

Dear R users, I noticed a problem in the anova command when applied on a single coxph object if there are missing observations in the data: This example code was run on R-2.6.1: > library(survival) > data(colon) > colondeath = colon[colon$etype==2, ] > m = coxph(Surv(time, status) ~ rx + sex + age + perfor, data=colondeath) > m Call: coxph(formula = Surv(time, status) ~ rx +

Hi, I've a list of list. I want to extract an element by the rownames. I can extract it by: data[[1]][[1]][[4]][1] But I want to exctract it by a command like this: data[[1]][["B0"]][["smac"]][["cont"]][1] It's possible? Thanks, Alfredo > str(data) List of 1 $ :List of 4 ..$ :List of 4 .. ..$ : num [1, 1:3] 0.4 0.458 0.5 .. ..$ : num