similar to: truncating values into separate categories

COnsider the following: Age<-c(48, 57, 56, 76, 76, 66, 70, 14, 7, 3, 62, 62, 30, 10, 7, 53, 44, 29, 46, 47, 15, 13, 84, 77, 26) SerialNo<-c(001147, 005979, 005979, 006128, 006128, 007004, 007004, 007004, 007004, 007004, 007438, 007438,009402,009402, 009402, 012693, 012693, 012693, 014063,014063, 014063, 014063, 014811, 014811,016570) TestSet<-cbind(Age,SerialNo)

This may not actually be an R/Splus problem, but it started off that way ..... ===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+=== Executive summary: ================== Simulations involving extreme value distributions seem to ``work'' when the underlying distribution is exponential(1) or exponential(2) == chi-squared_2, but NOT when the underlying distribution is

Hello All: Can anybody tell me what is the problem with my program please. I have an error message as appears below. My program is: ifn <- "Jul08_09.LM" data <- read.table(ifn) ofn <- "Jul.png" bitmap(ofn, type = "png256", width = 30, height = 30, pointsize = 30, bg = "white",res=50) par(mar=c(5, 5, 3, 2),lwd=5)

Hi all, I have a matrix simulating migration in a spatial model. I want to be able to define movement (the values of m1, m2 and m3) as only != 0 between adjacent patches and contingent on certain conditions as defined in the function. Here is the code: WET<-function(t) {everglades$precipitation[t]} #simply reads precipitation data from a csv, value is numeric AB<-function(WET,t)

Dear all, I am trying to use the Matrix package to do some calculations on rather large and sparse matrices. An example of such a matrix is given below. mig<-0.2 side<-10 np<-side^2 mig.mat<-matrix(0,np,np) diag(mig.mat[1:(np-side),(side+1):np])<-mig/4 diag(mig.mat[(side+1):np,1:(np-side)])<-mig/4 diag(mig.mat[-np,-1])<-mig/4

Dear all, I have divided two vectors: Np.occup97.98<- as.data.frame(cbind(site = levels(sums$site), Np.occup97.98 = sums$Ant.Nptrad97.98/Ant.trad$Ant.trad97.98)) > Np.occup97.98 site Np.occup97.98 1 erken97 0.342592592592593 2 erken98 0.333333333333333 3 rormyran 0.48471615720524 4 valkror 0.286026200873362 However, at a later stage of the analysis I want >

R 2.6.1 on a Thinkpad T60 running up-to-date Gentoo: Despite the documentation, which says: 'strftime' is an alias for 'format.POSIXlt', and 'format.POSIXct' first converts to class '"POSIXlt"' by calling 'as.POSIXlt'. Note that only that conversion depends on the time zone. strftime fails on POSIXct objects: > foo <-

Dear list, When using as.POSIXlt with times measured down to microseconds the default format.POSIXlt seems to cause some possibly undesirable behaviour: According to the code in format.POSIXlt the maximum accuracy of printing fractional seconds is 1 microsecond, but if I do; options( digits.secs = 6 ) as.POSIXlt( 1.000002 , tz="", origin="1970-01-01") as.POSIXlt( 1.999998 ,

Dear all, I have a function that for a variable number of inputs plots them to the same plot I am doing this quite simply by plot(seq(from=start, to=stop, length.out=np), datalist[[1]]$dataset                                                                  xlim=c(start, stop), ylim=c(0, 1), type="l")                                                                                      

On Wed, Jan 11, 2012 at 04:52:36PM +0800, Zhenzhong Duan wrote: > add polling interface to xen-netfront device to support netconsole > Ian, any thoughts on the spinlock changes? > Signed-off-by: Tina.Yang <tina.yang@oracle.com> > Cc: Konrad Rzeszutek Wilk <konrad.wilk@oracle.com> > Cc: Jeremy Fitzhardinge <jeremy@goop.org> > Signed-off-by: Zhenzhong.Duan

Not tested in any way. The original code for offload setting seems broken as it resets the features on every netback reconnect. This will set GSO_ROBUST at device creation time (earlier than connect time). RX checksum offload is forced on - so advertise as it is. Signed-off-by: Micha? Miros?aw <mirq-linux at rere.qmqm.pl> --- [I don't know Xen code enough to say this is correct. There

Not tested in any way. The original code for offload setting seems broken as it resets the features on every netback reconnect. This will set GSO_ROBUST at device creation time (earlier than connect time). RX checksum offload is forced on - so advertise as it is. Signed-off-by: Micha? Miros?aw <mirq-linux at rere.qmqm.pl> --- [I don't know Xen code enough to say this is correct. There

Not tested in any way. The original code for offload setting seems broken as it resets the features on every netback reconnect. This will set GSO_ROBUST at device creation time (earlier than connect time). RX checksum offload is forced on - so advertise as it is. Signed-off-by: Micha? Miros?aw <mirq-linux at rere.qmqm.pl> --- [I don't know Xen code enough to say this is correct. There

Dear Sir, My name is ChenHua, and I'm writing on behalf of the China-Lutong mechanical company. Located in the south east of China, we specialize in hydraulic heads for the VE distributor pump. We can supply standard, good quality units at a very competitive price. The following types are available: Engine model VE PUMS code NO UNIT PRICE(EX WORKS) ISUZU) NP-VE4/11L

Dear Sir, My name is ChenHua, and I'm writing on behalf of the China-Lutong mechanical company. Located in the south east of China, we specialize in hydraulic heads for the VE distributor pump. We can supply standard, good quality units at a very competitive price. The following types are available: Engine model VE PUMS code NO UNIT PRICE(EX WORKS) ISUZU) NP-VE4/11L

Dear Sir, My name is ChenHua, and I'm writing on behalf of the China-Lutong mechanical company. Located in the south east of China, we specialize in hydraulic heads for the VE distributor pump. We can supply standard, good quality units at a very competitive price. The following types are available: Engine model VE PUMS code NO UNIT PRICE(EX WORKS) ISUZU) NP-VE4/11L

Hello, I am working on a simple non-parametric (Theil) regression function and and am following Hollander and Wolfe 1999 text. I would like some help making my function faster. I have compared with pre-packaged version from "MBLM", which isnt very fast either, but it appears mine is faster with N = 1000 (see results below). I plan on running this function repeatedly, and I generally

Dear all, I am encountering some issues with my data and need some help. I am trying to run glm analysis with a presence/absence variable as response variable and several explanatory variable (time, location, presence/absence data, abundance data). First I tried to use the glm() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2:

Hello, I am trying to write a script with the end goal of graphing power (y) as a result of sample size (x) at a variety of effects sizes. I am new to loops, and I think my problem is there. Here's the script, which is modified from the script found at the bootom of http://www.statmethods.net/stats/power.html. ANy help would be much appreciated!mtesche at ualberta.ca library(pwr)

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence