similar to: Random Integers

Hi, Is the following function built in somewhere? concat = function(v) { res = "" for (i in 1:length(v)) res = paste(res,v[i],sep="") res } e.g. > concat(c("12","3","45")) [1] "12345" Cheers, Andy -- Andy Fugard, Postgraduate Research Student Psychology (Room F3), The University of Edinburgh, 7 George

Hi there, Am I correct to believe that tetrachoric correlation is a special case of polychoric correlation when there are only two levels to the ordered factor? Thus it should be okay to use hetcor from the polycor package to build a matrix of correlations for binary variables? If this is true, how can one estimate 95% confidence intervals for the correlations? My guess would be mat =

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

Hi, My name is Laura. I'm a PhD student at Queen's University Belfast and have just started learning R. I was wondering if somebody could help me to see where I am going wrong in my code for estimating the parameters [mu1, mu2, lambda1] of a 2-phase Coxian Distribution. cox2.lik<-function(theta, y){ mu1<-theta[1] mu2<-theta[2] lambda1<-theta[3]

Dear Friends, I'm trying to generate a sequence of 100 observations with either a 1 or -1. In other words the sequence should look something like this. y = 1 1 -1 1 -1 -1 -1 1 1 ...... Can somebody please give me some direction on how I can do this in R. Thanks Anup ____________________________________________________________________________________ Don't get soaked. Take a

Hello everyone, I have a dataset which includes the first three variables from the demo data below (year, id and var). I need to create the new variable ans as follows If var=1, then for each year (where var=1), i need to create a new dummy ans which takes the value of 1 for all corresponding id's where an instance of one was recorded. Sample data with the output is shown below. year

Dear friends, I have a basic question with R. I'm generating a set of random variables and then combining them using the cbind statement. The code for that is given below. for (i in 1:100) { y <- rpois(i,lambda=10) X0 <- seq(1,1,length=i) X1 <- rnorm(i,mean=5,sd=10) X2 <- rnorm(i,mean=17,sd=12) X3 <- rnorm(i,mean=3, sd=24) ind <- rep(1:5,20) }

Hello all, There's a new mailing list for researchers in psychology who are learning and using R. New users of R are especially welcome. To join, venture to http://www.jiscmail.ac.uk/archives/psych-r.html and click "Join or leave the list (or change settings)" You can also peruse the archives from that page. Best wishes, Andy -- Andy Fugard, Postgraduate Research

Dear Friends, I have data set with around 220,000 rows and 17 columns. One of the columns is an id variable which is grouped from 1000 through 9000. I need to perform the following operations. 1) Remove all the observations with id's between 6000 and 6999 I tried using this method. remdat1 <- subset(data, ID<6000) remdat2 <- subset(data, ID>=7000) donedat <- rbind(remdat1,

Dear friends, I'm writing a function with three arguments myfun <- function(theta, X, values) { .... .... } in this function, I'm trying to write consistency checks. In order to compute the statistic of interest I only need theta and values. The idea of having X in there is that, if values is not provided by the user, then values is computed from X. my problem is I'm trying to

Dear Friends, I'm trying to see if there is some possibility that I can do the following computations without a loop. I have attached a toy example below. A <- c(1,2,3,4,5) B <- rnorm(100) store <- matrix(0,5,2) for (i in 1:5) { store[i,1] <- mean(pnorm(A[i]*B)) store[i,2] <- var(pnorm(A[i]*B)) } store Thanks in advance for your help. Kind Regards Anup [[alternative

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

Dear Friends, I'm trying to learn to how to get Bootstrapped standard errors for estimated coefficients from a regression. For instance suppose I have the following model logitmodel <- glm (y~X1+X2+X3, family=binomial(link="logit")) beta <- logitmodel$coef can somebody please guide me on how to use the package boot to obtain bootstrapped SE's for the associated betas.

Dear Friends, Suppose I have a vector as follows RI Value 1 10 2 11 3 8 4 4 6 12 I would like a function which returns the"row index number" for the minimum value of VALUE. Can somebody please give me some direction on how I can do this. In effect I'm trying to find a comparable function for the GAUSS command "minindc" >From the GAUSS Manual

Friends, I'm trying to pass an equation as an argument to a function. The idea is as follows. Let us say i write an independent function Ideal Situation: ifunc <- function(x) { return((x*x)-2) } mainfunc <- function(a,b) { evala <- ifunc(a) evalb <- ifunc(b) if (evala>evalb){return(evala)} else return(evalb) } Now I want to try and write this entire program in a single

Dear Friends, I have a matrix of size 5000 X 20. The first two columns are indicator variables taking the value of either 0 or 1. Let us call the first two columns Y1 and Y2. I need to randomly sample 1000 rows with all the associated columns, in other words my new matrix should be of size 1000 X 20. I realize that using this command newmat <- mainmat[sample(1000,replace=F),] achieves

Dear friends, I have a data set with 23 columns and 38000 rows. It is a panel running from the years 1991 through 2005. I want to aggregate the data and get the medians of each of the 23 columns for each of the years. In other words my output should be like this Year Median 1991 123 1992 145 1993 132 etc. The sample lines of code to do this operation is set1 <-

Dear Friends, My objective is to do element wise multiplication of two vectors. For example suppose I have a <- (1,1,1) b <- (2,4) My output should be (2,4,2,4,2,4). I managed to write it down with loops as follows r <- c(1,1,1) l <- c(2,4) x <- 1 for (j in 1:3) { for (i in 1:2) { new[x,] <- r[j]*l[i] x <- x+1 } } Is there a simpler solution to

Dear friends, Please find below the code that I have employed for a rejection sampler to draw from asymmetric laplace distributions. I was wondering if this code can be written more efficiently? Are there more efficient ways of drawing random numbers from asymmetric laplace distributions?? Thanks in advance for your help and have a great weekend. Regards Anup

 Dear R-helpers, My question is related to how to impose constraints when when sampling from a distribution. For example, suppose I'm sampling a vector from a multivariate normal distribution vbeta <- 100*diag(2) mbeta <- c(1,1) ans <- beta <- c(rmvnorm(1,mbeta,vbeta)) ans will thus be a vector with two elements. My question is how do I place a restriction on one of the