similar to: Performance tests of the current version (git-b1b6caf)

  Hi r-users,   I would like to use gev and my data (annual rainfall ) is as follows:   > head(dat,20) A B C D E F G H I J 1 45.1 41.5 58.5 50.1 46.0 49.1 37.7 49.1 59.8 54.0 2 50.3 39.8 49.4 56.4 49.4 48.8 42.1 49.8 49.4 58.3 3 41.7 39.3 44.6 39.1 35.7 41.5 40.8 40.8 38.5 45.6 4 50.7 33.9 48.4 28.2 35.5 39.1 61.4 17.0 30.7 38.3 5 39.3 30.6 46.9 23.8 25.8

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

I've got a list of 5 matrices that are each 5 x 6. I'd like to end up with a 5 x 6 matrix that contains the mean value of the 5 original matrices. I can do this by brute force, but there must be a better way than making each matrix into a vector and then remaking a matrix thanks very much for any help david freedman ll=list(structure(c(9.7, 17.6, 20.8, 24.1, 33.8, 14.5, 25.7, 29.8,

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

Hi, I tweaked my scripts to avoid removing includes when it doesn't give any significant benefits, which made the patches significantly smaller. This time the patches should not try to remove includes of header files, which are transitively included from other included header files. The gains mostly remained the same (plus/minus noise), the tables are in the end of the email. I also included

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

2017-12-09 12:54 GMT-08:00 Chris Lattner via llvm-dev < llvm-dev at lists.llvm.org>: > > > On Dec 8, 2017, at 5:01 PM, Mikhail Zolotukhin via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > > Hi, > > I tweaked my scripts to avoid removing includes when it doesn't give any > significant benefits, which made the patches significantly smaller. This >

Estimada comunidad tengo un problema del que no encuentro datos que me ayuden mucho en la web. Estoy haciendo calculos por grupos con data,table. Tengo un archivo (zp.res) con tres columnas que clasifican los datos (sol, con, dia) y una columna de datos numericos (media), de la siguiente forma: sol con dia media 1: con 0 1 -22.6 2: con 0 1 -36.6 3: con 0 1 -35.6 y

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear R list members, I'm encountering a problem with expand.model.frame(): Suppose that I define the following simple function (meant just to illustrate the problem): > fun <- function(model){ + expand.model.frame(model, all.vars(formula(model))) + } > and I have the following model, created with an explicit data argument: > mod Call:

Dear R-helpers, thanks for yesterday's speeding-up tip. Here is my next query: I have lots of polygons (not necessarily convex ones, and they never have holes) given by x,y coordinates. I want to get the polygon that is the union of these polygons. This is my current method, but I am hoping there is a faster method (up to thousands of polygons, each with ca. 40 xy points). Example:

Has anyone else noticed a significant zfs performance deterioration when running recent opensolaris bits? My 32-bit / 768 MB Toshiba Tecra S1 notebook was able to do a full opensolaris release build in ~ 4 hours 45 minutes (gcc shadow compilation disabled; using an lzjb compressed zpool / zfs on a single notebook hdd p-ata drive). After upgrading to 2007-05-25 opensolaris release bits

Hi I use sweave for excellent pdf output (thank you- Friedrich Leisch). I have just one problem. Quite often it happens, that the graphics are not at the place where I expect them, but (often on a separate page) later on in the pdf. How can I fix this, means how can I define, that I want a graphic exactly here and now in the document? Many thanks and best regards Christoph -- Christoph

Hey all - Was playing a little with zfs today and noticed that when I was untarring a 2.5gb archive both from and onto the same spindle in my laptop, I noticed that the bytes red and written over time was seesawing between approximately 23MB/s and 0MB/s. It seemed like we read and read and read till we were all full up, then wrote until we were empty, and so the cycle went. Now: as it happens,

Dear all, I am trying to validate a model by comparing simulated output values against observed values. I have produced a simple X-y scatter plot with a 1:1 line, so that the closer the points fall to this line, the better the 'fit' between the modelled data and the observation data. I am now attempting to quantify the strength of this fit by using a statistical test in R. I am no

Y X Z 42.0 7.0 33.0 33.0 4.0 41.0 75.0 16.0 7.0 28.0 3.0 49.0 91.0 21.0 5.0 55.0 8.0 31.0 data<-read.table("d.txt",header=TRUE) mod<-lm(data$Y~data$X+data$Z) --------------------------------------- I would like to know the predict value at a new level, say X=10 Z=30 Is there a function available to calculate it

On 2019/7/19 22:29, Jiang wrote: > > On 2019/7/19 10:36, Jason Wang wrote: >> >> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: >>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >>>> On Thu, Jul 18, 2019 at 10:01:05PM +0800, Jason Wang wrote: >>>>> On 2019/7/18 ??9:04, Michael S. Tsirkin wrote: >>>>>> On

On 2019/7/20 0:13, Michael S. Tsirkin wrote: > On Fri, Jul 19, 2019 at 03:31:29PM +0000, ? jiang wrote: >> On 2019/7/19 22:29, Jiang wrote: >>> On 2019/7/19 10:36, Jason Wang wrote: >>>> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: >>>>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >>>>>> On Thu, Jul 18, 2019