similar to: how to create many variables at one time?

I need to create 100 variable ,whose name is id.1,id.2~~~~id.100 then I need to let a vector say id<-c(id.1,id.2....id.100) any easy way to do this? thanks a lot~ [[alternative HTML version deleted]]

Hi everyone: I have been struggling with this repeated data type for whole afternoon,I sent two emails to server for help,many people kindly responded , hereby thank you so much,but since I dont want to write to much in email,so I divide the problem in parts,so far this seem did not work out very well,so this is my whole problem~ first I have example of data here:

here is the data: y<-c(5,2,3,7,9,0,1,4,5) id<-c(1,1,6,6,7,8,15,15,19) t<-c(50,56,50,56,50,50,50,60,50) table1<-data.frame(y,id,t)//longitudinal data what I want to do is to use the linear model for each id ,then get the estimate value,like: fit1<-lm(y~t,data=table1,subset=(id==1)) but ,you can see the variable "id" is quite irregular,they are not arranaged in order

Hi everyone: I have a density function f(x|theta)=theta*x^(theta-1),where 0<x<1,0<theta<infinite I want to pratice on R to find the MLE of this function,here is my code: x <- (0:10)/10 f<-function(theta) prod(theta*x^(theta-1)) mle(f) and r gave me :Error in eval(expr, envir, enclos) : argument is missing, with no default what mistake I just made?and how to add a

hi: just a very simple quesion,how to do multiple multiplication in R x<-c(1,2,3,4,5,6) how to get 1*2*3*4*5*6? I checked the Arithmetic Operators in R,but did not found the operators for this function,anyway can do this except the loop? [[alternative HTML version deleted]]

Hi everyone: I try to use r to do the Cholesky Decomposition,which is A=LDL',so far I only found how to decomposite A in to LL' by using chol(A),the function Cholesky(A) doesnt work,any one know other command to decomposte A in to LDL' My r code is: library(Matrix) A=matrix(c(1,1,1,1,5,5,1,5,14),nrow=3) > chol(A) [,1] [,2] [,3] [1,] 1 1 1 [2,] 0 2 2

a table with three varialbe:treatment (low,high),size(large,small),response y I want all response y with treatment=low and size=large, I try to write as : treatment<-c("low","low","high","high") size<-c("small","large","small","large") y<-c(1,2,4,5) table1<-data.frame(treatment,size,y)

here is the data: y<-c(5,2,3,7,9,0,1,4,5) id<-c(1,1,6,6,7,8,15,15,19) t<-c(50,56,50,56,50,50,50,60,50) table1<-data.frame(y,id,t)//longitudinal data the above is only part of data. what I want to do is to use the linear model for each id ,then get the estimate value,like: fit1<-lm(y~t,data=table1,subset=(id==1)) but ,you can see the variable "id" is quite

I have a 50*50 matrix,some entry are NAs,I want to replace these NA by 0,so can I use some syntax to do so other than using ifelse? I tried to use replace(a,NA,0),it didnt work~~(a is matrix name) Thanks~ [[alternative HTML version deleted]]

https://bugzilla.samba.org/show_bug.cgi?id=7809 Summary: I/O errors other than IOERR_GENERAL should not suppress deletion Product: rsync Version: 3.1.0 Platform: All OS/Version: All Status: NEW Severity: normal Priority: P3 Component: core AssignedTo: wayned at samba.org

assume dependent variable y( continuous),independent variable x ( continuous),I try to categorize x with some interval,such that,those intervals would has most significant different effect on y. any one knows which method I should apply,I really need some hints,thanks so much~ [[alternative HTML version deleted]]

Hello R people, I have a data file with 101 numeric variables: one variable called IDN (the individual's unique id number, which I need to retain, and which ranges from 1000 to 1320; some numbers are obviously skipped), and V1 to V100 (each has a value of 0 or 1; these 100 variables represent sequentially ordered days and whether a characteristic was present or absent--e.g., v1 is day 1 and a

I was looking at the Sparc 64 4.7 or 4.8 port, wondering if its in any state to put on my Solaris v100. Just need the basics, eide, network, kbm,mouse, graphics (x). A bonus would be support for the lom (light out management) features and the flash card. Reason for change is I HOPE that the math (ssl) performance is better on FBSD, as well as I am just tired of keeping track of

Hi, I have a question of the method as how to normalize the data sets according to a set of the internal measurements. For example, I have performed two batches of experiments contrasting two different conditions (positive versus negative conditions): one at a time. 1. each experiment, I measure signals of variable v1 to v100. I want to understand v1 to v100 change under these two contrasting

Thanks for the advice. My question is more on how to do this? Let me use a biology gene analysis example to illustrate: In biology, there are always some house keeping genes which differ little even at pathological conditions. We know that at different batches, there are external factors affect the measurements. For example, overall signal intensity might be different due to lab reagents. A

Hi: when add a new voicemailbox, asterisk will create a new directory to it. since linux has limitation for the number of subdirectory. i wonder how many voicemailbox can asterisk support? thanks. yan -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20030430/bd36cdaa/attachment.htm

I have code like this: data <- read.csv("test1.csv", stringsAsFactors=FALSE, header=TRUE) # Graph myplot=ggplot(data, aes(fill=condition, y=value, x=condition)) + geom_bar(position="dodge", stat="identity", width=0.5) + scale_fill_manual(values=c("#7b3294", "#c2a5cf", "#a6dba0", "#008837"))+

HI there, i know this is a basic question, though i need some help because this is somewhat away from my current issue, but nevertheless interesting to me... Lets assume i have some estimated probabilities, say estimated by a logit model. i know i can also state them as an odds ratio. Now i?d like to state these odds ratios as a reference to a specific outcome of my investigated

Hi all, I’m working on my thesis in which I need to count the number of memory access (i.e. load and stores) of each variable of program. how can I do that? these are some approaches came to my mind : I need to know during the execution of the program how many load and store instructions are executed on a specific variable, hence simply counting static load and store instructions doesn’t help.

I'm looking for a function in R similar to t.test() which was generously pointed out to me yesterday, but which can be used for normally distributed data. To recap yesterday: > x <- scan() 1: 62 52 68 23 34 45 27 42 83 56 40 12: Read 11 items > alpha<- .05 > t.test(x) One Sample t-test data: x t = 8.8696, df = 10, p-value = 4.717e-06 alternative hypothesis: true