similar to: vertical lines in R plot

Dear R-users, May I seek some suggestions from you. I have a long programme written in R with several 'for' loops inside. I just want to get them out by any elegant way (if there is!) to reduce the computational time of the main programme. For instance, is there any smart way for the following programme that will lessen time?

Dear R-users, I am facing problem with integrating in R a likelihood function which is a function of four parameters. It's giving me the result at the end but taking more than half an hour to run. I'm wondering is there any other efficient way deal with. The following is my code. I am ready to provide any other description of my function if you need to move forward.

Dear R-user, May I seek your suggestion. I have a data file 'stop' to be scanned in R. But I want to ignore one specific number '21' there. Putting differently, I want to get all numbers in the file except 21. Is there any command to achieve it? -------------------------------------------------------------- b<-scan("F:\\stop.txt")

Hi R-users. I'm having difficulty with an integration in R via the package "cubature". I'm putting it with a simple example here. I wish to integrate a function like: f(x1,x2)=2/3*(x1+x2) in the interval 0<x1<x2<7. To be sure I tried it by hand and got 114.33, but the following R code is giving me 102.6667.

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Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Hello R and BUGS users, I am writing a heirarchical model in R to send to BUGS via R2WinBUGS and I am finding it difficult to get the model to run. I seem to be having two problems. 1) I can't seem to send variables classed as factors (Month), is there a way do this? 2) Checking the Log in WinBUGS I can see that the model is Syntactically correct, but Bugs is not able to recognise the the

Dear: I try to revise the maximum likelihood function below using something constrains. But it seems something wrong with it. Becasue R would not allow me to edit the function like this. It is very appreciate if you can help. function (parameters,y,x1,x2) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3)] delta1<-parameters[4] alpha2<-parameters[5]

Dear R-users, Though it's a silly thing to ask, but I'm not getting a way out. I wish to find the percentage distribution for a data vector 'stop'. The coomand below is giving the frequency distribution. May I know the option to see the percentages instead of frequencies. Similarly, what option I should put in a histogram plot to see the percentages instead of frequencies?

Hi jpm miao, You can get CSV files that can be imported into Excel like this: library(prettyR) sink("excel_table1.csv") delim.table(table(df[,c("y","z")])) sink() sink("excel_table2.csv") delim.table(as.data.frame(table(df[,c("y","z")])),label="") sink() sink("excel_table3.csv")

There is a thread on this topic already: http://finzi.psych.upenn.edu/Rhelp10/2010-August/250934.html I'm rather mystified by a similar problem and wondering whether I've overlooked something obvious. I'm running with latest versions of R and all packages, and latest version of JAGS running under Windows 7. Here's the problem. I have some source code. It's given below -

Hi, I apologize for this often/old question. I found some hints but couldn't solve the problem so far. I have C functions (incl. the header files) as well as the R wrapper functions which I want to use for faster calculations. These functions are included in a R package. The installation process seems to be ok (no errors). I also can load the package without errors. But when I call the

I have both 1.0 alpha1 and alpha2 installed on my test mail server. SSL/TLS does not work on alpha2. I'm using the same config file for both. No errrors in dovecot.log either. Is there a way to increase debugging info without recompiling?

Hi, How could we print the frequency table (produced by "table") to an Excel file? Is there an easy way to do so? Thanks, Miao > df <- data.frame(x = 1:3, y = 3:1, z = letters[1:3]) > table(df[,c("y","z")]) z y a b c 1 0 0 1 2 0 1 0 3 1 0 0 > test<-table(df[,c("y","z")]) > as.data.frame(test) y z Freq 1 1 a

Dear R-user, I'm having some difficulty with working PFIM 3.2, a package for implementing population PK/PD in R. I wish to evaluate the determinant of Fisher information matrix each time with successive dose from a pre defined sequence of doses and want to store those values in a vector. It's important to note that in my 'stdin.r' file, dose<-c(u) and each time u is to be

I am trying to run WinBUGS 1.4 from the Ubuntu 6.10 Linux distribution. I am using the R2WinBUGS packages with the source file listed below. WinBUGS appears to run properly, but I get the following message after WinBUGS starts in WINE. Does anyone know what may be causing this error and what the correction may be? Thanks ERROR MESSAGE: fixme:ole:GetHGlobalFromILockBytes cbSize is 13824

Dear: Continuing the issue of 'ifelse'! I selecting the data whose 'x2'=1 for maximizing likelihood. I used two way to do this but the results are different. 1.Way one I use the data for x2=1 and run the program. It works for me. Tthe program is described as below: function (parameters,y1,x11) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3]

Hello there, I am new to using regression in R. I wanted to solve a simple regression problem where I have 2 equations and 2 unknowns. So lets say - y1 = alpha1*A + beta1*B y2 = alpha2*A + beta2*B y1 <- runif(100000, 0,1) y2 <- runif(100000,0,1) alpha1 <- 0.6 alpha2 <- 0.75 beta1 <- 1-alpha1 beta2 <- 1-apha2 I now want this equation to estimate the values of A and B. Both A