similar to: R2WinBUGS problem: bugs - storage.mode error

Dear All,   #I have the following code   Dose<-1000 Tinf <-0.5 INTERVAL <-8 TIME8 <-matrix(c((0*INTERVAL):(1*INTERVAL))) TIME7 <-matrix(c((0*INTERVAL):(2*INTERVAL))) TIME6 <-matrix(c((0*INTERVAL):(3*INTERVAL))) TIME5 <-matrix(c((0*INTERVAL):(4*INTERVAL))) TIME4 <-matrix(c((0*INTERVAL):(5*INTERVAL))) TIME3 <-matrix(c((0*INTERVAL):(6*INTERVAL))) TIME2

Hi R user, I have two dataframe with different variables and coordinates : X Y sp bio3 bio5 bio6 bio13 bio14 1 -70.91667 -45.08333 0 47 194 -27 47 12 2 -86.58333 66.25000 0 16 119 -345 42 3 3 -62.58333 -17.91667 0 68 334 152 144 28 4 -68.91667 -31.25000 0 54 235 -45 25 7 5 55.58333 48.41667 0 23 319 -172 23 14 6 66.25000

Hello, I have a strange problem inside my Xen DomU. Inside my domU, I have a tool running (lookbusy) that generates a certain amount of cpu load. Let''s say, I generate 50% of CPU load on 1 core. When I check the cpu load with top, I get something strange. Here''s the output from top: top - 11:36:06 up 52 min, 1 user, load average: 0.54, 0.52, 0.46 Tasks: 68 total, 2

Hello R developers. I encountered the same problem as Uwe Ligges with as.matrix.data.frame() in bug reports 3229 and 3242 - under section not-reproducible. Example I have is: > tmp level 2100-D 1 biological_process unknown NA 2 cellular process -5.88 3 development -8.42 4 physiological process -6.55 5

Ben, I would like to test the sulfur on the clover field, nitrogen on the clover field and then test for the presence of interaction. Sorry about the last email, seems it really screwed itself over, here it is again, hopefully nicer: Nitrogen(0) Nitrogen(20) Sulfur(0) 4.54 5.73 Sulfur(3) 4.64

Given the data structure below, how can I create a bar plot for the values of disc for each area grouped by year? bar <-structure(list(year = c(2003, 2003, 2003, 2003, 2003, 2003, 2003, 2007, 2007, 2007, 2007, 2007, 2007, 2007), area = structure(c(6L, 4L, 1L, 2L, 3L, 5L, 7L, 6L, 4L, 1L, 2L, 3L, 5L, 7L), .Label = c("AK", "ID", "MT", "NW",

Dear all, I'm trying to analyze data with the following structure: ind cat tx age 40.2 por fol peq vh 35 41.9 por fol med vh 35 68.9 por fol preov vh 35 71.5 por fol peq ser 37 67.5 por fol med ser 37 76.9 por fol preov ser 37 78.7 por fol peq otr 37 78.3 por fol med otr 37 82.1 por fol

Dear list, First my information: platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 2 minor 3.1 year 2006 month 06 day 01 svn rev 38247 language R version.string Version 2.3.1 (2006-06-01) Now my question: How is it possible that a command in an R script is not

Hi all, I guess this might be an easy question, but I've searched multiple help pages without finding any answear... so now I put my trust in you! I have a data frame (36 variables and 556 observations). One column contains three factors, and I would like to divide the data frame into three new ones, based on the value of the factors, thereby having only one value for all elements of the

A rather notorious aspect of TableGen is the time required to run the -emit-dag-isel backend on some targets, including AMDGPU and X86. I added a timing feature to TableGen and timed the AMDGPU run. ===-------------------------------------------------------------------------=== TableGen Phase Timing

Hi, I want to remove the entire row from the dataset if any of the row contains blank or NA my dataset look like State Year Y X2 X3 X4 X5 X6 State1 1960 27.8 397.5 42.2 50.7 78.3 65.8 State2 1960 29.9 413.3 38.1 52 79.2 66.9 State1 1961 29.8 439.2 40.3 54 79.2 67.8 State2 1961 30.8 459.7 39.5 79.2 69.6 State1 1962 31.2 492.9 37.3 54.7 77.4 68.7 State2 1962 528.6 38.1 80.2 73.6 State1 1963

Hi I'm performing a PLS This is my data present in a file Year Y X2 X3 X4 X5 X6 1960 27.8 397.5 42.2 50.7 78.3 65.8 1960 29.9 413.3 38.1 52 79.2 66.9 1961 29.8 439.2 40.3 54 79.2 67.8 1961 30.8 459.7 39.5 55.3 79.2 69.6 1962 31.2 492.9 37.3 54.7 77.4 68.7 My R-code Data <- read.csv("C:/TestData.csv") variable=names(Data)[4:8] dataset=NULL dataset$X=NULL len=length(variable)

Dear List I hope you can help me: I?ve got a dataframe (df) within which I am looking for Peak Over Threshold values as well as the length of the events. An event starts when walevel equals 5.8 and it should end when walevel equals the lower threshold value (5.35). I tried ?clusters (?)? from ?evd package?, and varied r (see example) but it did not work for all events (again

Here is a piece of code fitting a model to a (part) of a dataset, just for illustration. I can extract the random interaction and the residual variance in group meth==1 using VarCorr, but how do I get the other residual variance? Is there any way to get the other variances in numerical form directly - it seems a litte contraintuitive to use "as.numeric" when extracting estimates,

Given the data structure below and the call to ggplot2, how can I increase the size of the axis scale points, the line weight, and the size of the legend? ddata <-structure(list(year = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("2003", "2007"), class = "factor"), area = structure(c(7L, 6L, 1L, 2L, 3L, 4L, 5L, 7L, 6L, 1L,

Hello, I would like to get a correlation coefficient (R-squared) for my model. I don't know how to calculate it in R. What I've done so far: x<-8.5:32.5 #Vektor x y<-c(NA ,5.88 , 6.95 , 7.2 , 7.66 , 8.02 , 8.44 , 9.06, 9.65, 10.22 , 10.63 ,11.06, 11.37, 11.91 ,12.28, 12.69 ,13.07 , 13.5 , 13.3 ,14.14 , NA , NA , NA , NA , NA) #Vektor y

I wouldn't want to be too hasty about simply removing the relaxation algorithm. The size and speed of the compiler affects all users, but the time to compile the compiler "only" affects us compiler developers. And I speak as a developer who is heavily affected by the time to compile the AMDGPU backend. One off-the-cuff idea (I haven't even looked at the code yet): could we pass

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

Recollected the data from trunk head with stddev data and more threshold data points attached: Performance: stddev/mean 300 450 600 750 403 0.37% 0.11% 0.11% 0.09% 0.79% 433 0.14% 0.51% 0.25% -0.63% -0.29% 445 0.08% 0.48% 0.89% 0.12% 0.83% 447 0.16% 3.50% 2.69% 3.66% 3.59% 453 0.11% 1.49% 0.45% -0.07% 0.78% 464 0.17% 0.75% 1.80% 1.86% 1.54% Code size: 300 450 600 750 403 0.56% 2.41% 2.74% 3.75%

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm