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I have a data set like this. I want to do glm, but I get this error: Error in model.matrix.default(mt, mf, contrasts) : cannot allocate vector of length 932889958 I am wondering if my data set is too large or I did something wrong. Is there some limitation for data size for R? thanks, Aimin > p1982<- read.csv("p_1982_aa.csv") > names(p1982) [1] "p"

I have a data set p1982, its structure is the following Then I take 20 observations from this dataset, and assign to pr. in p1982, p has 1982 levels, in dataset pr, p should have 1 levels. But I do str(pr), it shows that p still has 1982 levels. also for these > pr$aa [1] ARG THR ASP CYS TYR ASN VAL ASN ARG ILE ASP THR THR ALA SER CYS LYS THR ALA LYS Levels: ALA ARG ASN ASP CYS GLN

I have a data set like this I want to assign "outward" to Y if sc <90 and assign "inward" to Y if sc>=90. then cbind(p1982,Y) to get like these p aa as ms cur sc Y 1 154l_aa ARG 152.04 108.83 -0.1020140 92.10410 inward 2 154l_aa THR 15.86 28.32 0.2563560 103.67100 inward 3 154l_aa ASP 65.13 59.16 0.0312137 7.27311 outward 4 154l_aa CYS 57.20 49.85

Hi, I work with SEXP C code and with xts and quantmod packages. I try to touch how xts internal works. So we have R session and: > ls() character(0) > getSymbols('AAPL') # quantmod package [1] "AAPL" > ls() [1] "AAPL" > str(AAPL) An ?xts? object from 2007-01-03 to 2010-09-09 containing: Data: num [1:929, 1:6] 86.3 84 85.8 86 86.5 ... - attr(*,

Hi everyone, I determined the presence of three types parasites in a passerine bird over two years. I would like to create a bar chart that shows the proportion infected on the y and year/parasite on the x such that each type of parasite is grouped together (single label) and a bar for each year . This would show if there have been changes in the prevalence of a the parasite over two years.

Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate

Hi, I tried to inverse a matrix but it doesn`t work. I hope somebody can help me. This is what I did. > kurse <- read.table("kurse.txt", header=T, dec=",") > x <- cbind(1,kurse[,-c(1)]) > y <- kurse$index > t(x) %*% x Error in t(x) %*% x : requires numeric matrix/vector arguments > x <- as.matrix(x) > xtxi <- solve(t(x) %*% x) Error in

Hi All, Please pardon me if I am missing something obvious here. How do I get the Kaplan-Meier estimate function that is created by survfit and plotted by the code. fit <- survfit(Surv(time, status) , data=aml) plot(fit) That is, I need a function that will give me the survival estimate at a given time: \hat{S}(t). Thanks in advance. Ritwik Sinha ritwik.sinha at gmail.com | +12033042111 |

I am trying to use lm for a simple linear fit with weights. The results I get from IDL (which I am more familiar with) seem correct and intuitive, but the "lm" function in R gives outputs that seem strange to me. Unweighted case: > x<-1:4 > y<-(1:4)^2 > summary(lm(y~x)) Call: lm(formula = y ~ x) Residuals: 1 2 3 4 1 -1 -1 1 Coefficients:

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

Dear List, i am trying to produce a 3d plot using wireframe using the code: wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE) As you can see when the code (using the data below) is run the plot area is set-up correctly but the actual surface is missing? Any help would be greatly appreciated. Chris #data Elevation

Full_Name: Richard Huggins Version: 1.7.1 OS: windows 2000 Submission from: (NULL) (131.172.4.44) > x<-rnorm(100,2,1) > mean(x) [1] 1.73299 > summary(fivenum(x)) Min. 1st Qu. Median Mean 3rd Qu. Max. -0.3655 1.1070 1.7430 1.7320 2.3840 3.7910 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. -0.3655 1.1070 1.7430 1.7330 2.3830 3.7910 >

Hi, i am working in the forecast of the daily price crude . The last prices of this data are the following: 100.60 101.47 100.20 100.06 98.68 101.28 101.05 102.13 101.70 98.27 101.00 100.50 100.03 102.23 102.68 103.32 102.67 102.23 102.14 101.25 101.11 99.90 98.53 96.76 96.12 96.54 96.30 95.92 95.92 93.45 93.71 96.42 93.99 93.76 95.24 95.63 95.95 95.83 95.65

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Hello list, how could this happen? The Server doesn't respond from time to time with a high load avarage. We found a suspicious smbd process: top - 13:43:07 up 1 day, 2:27, 5 users, load average: 32.49, 58.41, 37.95 Tasks: 1196 total, 5 running, 1190 sleeping, 0 stopped, 1 zombie Cpu0 : 14.7% us, 3.8% sy, 0.0% ni, 79.8% id, 1.3% wa, 0.0% hi, 0.3% si Cpu1 : 1.3% us, 84.6%

Hi, I think that it would be a good thing to know more about those 2 projects (and also the future patent free format). I think that many people as me know about Lame, but not about Vorbis, and vice-versa. It would be fine that someone (perhaps the maintainer) of every project would introduce to both group of people those projects. 2 things would be interesting (to my mind): - to know about the

This patch adds the necessary KVM specific code to allow KVM to support the CPU halting and kicking operations needed by the queue spinlock PV code. Two KVM guests of 20 CPU cores (2 nodes) were created for performance testing in one of the following three configurations: 1) Only 1 VM is active 2) Both VMs are active and they share the same 20 physical CPUs (200% overcommit) The tests run

This patch adds the necessary KVM specific code to allow KVM to support the CPU halting and kicking operations needed by the queue spinlock PV code. Two KVM guests of 20 CPU cores (2 nodes) were created for performance testing in one of the following three configurations: 1) Only 1 VM is active 2) Both VMs are active and they share the same 20 physical CPUs (200% overcommit) The tests run

Consider the following.... > fileLines V1 V2 V3 V4 V5 V6 V7 V8 1 AB 20091224 156.0 156.0 154.00 154.00 55 1198 2 AB.C 20091224 156.0 156.0 156.00 156.00 0 0 3 ABF10 20091224 156.0 156.0 156.00 156.00 55 444 4 ABH10 20091224 156.0 156.0 156.00 156.00 0 749 5 ABH11 20091224 157.2 157.2 157.20 157.20 0 0 6 ABH12

Hi, I haven't tried the code yet. Is there a way to parse this data using R and create bar plots so that each program's 'RAM used' figures are grouped together. So 'uuidd' bars will be together. The data will have about 50 sets. So if there are 100 processes each will have about 50 bars. What is the recommended way to graph these big barplots ? I am looking