similar to: indexing within the function "aggregate"

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of

Hello I am running R 2.0.1 on Windows, I am attempting to use Frank Harrell's 'fastbw' function (from the Design library), but I get an error that the fit was not created with a Design library fitting function; yet when I go to the help for fastbw (and also look in Frank's book Regression Modeling Strategies) it appears that fastbw should work with a model created with lm.....

Hello again I am trying to fit an ordinal logistic model using the polr function from MASS. When I run model.loan.ordinal <- polr(loancat~age + sex + racgp + yrseduc + needlchg + gallery + sniffball + smokeball + sniffher + smokeher + nicocaine + inject + poly(year.of.int,3) + druginj + inj.years) I get an error Error in optim(start, fmin, gmin, method = "BFGS", hessian =

Dear R users, I have two data frames that were read from text files as follows: x_data <- read.table("x.txt", header = TRUE, sep = "|", quote = "\"'", dec = ".",as.is = TRUE,na.strings = "NA",colClasses = NA, nrows = 3864284, skip = 0, check.names = TRUE,fill=TRUE, strip.white = TRUE,

Hi all, first of all thanks for the answer to my previous question on lattice some time ago. In particular to Patrick Connolly for advices on netiquette (I hope this time I'm doing right....) and to Deepayan Sarkar fro the help on lattice. Now, my nowaday problem. Please consider the following mydf<-cbind.data.frame( RESPONSE = c(rnorm(9,rep(2:4,each=3),10),

Hi all, I have a data set that looks a bit like this. feed1 RFU Site Vial Time lnRFU 1 44448 1 1 10 10.702075 2 47521 1 1 20 10.768927 3 42905 1 1 30 10.66674 4 46867 1 1 40 10.755069 5 42995 1 1 50 10.668839 6 43074 1 1 60 10.670675 7 41195 1 1 70 10.626072 8 47090 1 2 10 10.759816 9 48100 1

##I am trying to test for fixed factor main effects in an unbalanced mixed effects model but when I fit the reduced model for "mic" factor effects, the extra degrees of freedom are being allocated to a nested term rather than the residuals. The model has inc, mic and spp are independent variables and vial nested within spp. inc and spp are already coded as factors since they were

Hello, I am not entirely sure the subject line captures what I am trying to do, but hopefully this description of the problem will help folks to see my challenge and hopefully offer constructive assistance. I have an experimental setup where I measure the decrease in oxygen in small vials as an organism, such as an oyster, consumes the oxygen. Each vial is calibrated before the experiment and

Hi all, Say I have a data frame something like the one below with different sample vials, measured before(B) and after(A) some process, with a value recorded at each measurement point vial measure value 1 B 26 1 A 12 2 B 45 2 A 30 3 B 32 3 A 27 4 B

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit

I hope that the mailing list is the correct forum for the question below. I have trouble calling functions nlsList and nlme from another function. Any help would be greatly appreciated. Jens Praestgaard Human Genome Sciences Rockville MD. I have a data set v with two components, v$mixeddat and v$init. They are listed below: > v$mixeddat conc result rep sample z 11 20.00000000

Hi all, I have a data frame that looks like such: LATITUDE LONGITUDE TEMPERATURE TIME 36.73 -176.43 58.32 1 50.95 90.00 74.39 1 -30.42 5.45 23.26 1 15.81 -109.31 52.44 1 -80.75 -144.95 66.19 2 90.00 100.55 37.50 2

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Dear r-help users, I have the following simple problem: Reading data from a file. The file is a .txt file exported ("save as...") from Excel (see below for an example). The Excel file consists of two header rows (first row consists of ticker symbols of stocks, the second row consists of column explanations ("Date","Px Last"), followed by several rows of

On Wed, May 29, 2013 at 03:25:30PM +0200, Duncan Sands wrote: > Hi Jack, I pulled the loop vectorizer and fast math changes into the 3.3 branch, > so hopefully they will be part of 3.3 rc3 (and 3.3 final!). It would be great > if you could redo the benchmarks rc3. > Duncan, As requested, appended are the updated Polyhedron 2005 benchmark results with both RC1 and RC3 llvm 3.3

Ah, yes. One of the many differences between the US and the rest of the world. >>> support@drdos.info 4/3/2007 2:52:16 PM >>> john beaman wrote: > I too was curious about this, so I copied the text into Babel Fish, and this is the result: > > I miss of the 2/04/2007 to the 11/04/2007. I will answer your message as of my return. For any urgency, to contact Emmanuelle

I've been using parApply() in snow package for parallel computing with the following lines in R 2.8.1: library(snow) nNodes <- 4 cl <- makeCluster(nNodes, type = "SOCK") fm <- parApply(cl, myData, c(1,2), func1, ...) Since I have a Mac OS X (version 10.4.11) with two dual-core processors, I thought that I could run 4 simultaneous clusters. However with the 1st job

Below are the results for the Polyhedron 2005 benchmarks compiled with the llvm/compiler-rt/dragonegg 3.3 release and 3.4svn against FSF gcc 4.8.2. The *-stock-de runs omit the -fplugin-arg-dragonegg-enable-gcc-optzns flag and the *-de-optnz runs include it. There seems to be little improvement between llvm 3.3 and 3.4 in the stock case which relies entirely on llvm for vectorization. Duncan, has