similar to: 'save' saved object names instead of objects

Hello, I'm running a very long for loop that usually takes hours. For my own piece of mind, it would be nice if I could check periodically and see which iteration the loop is on. A line of code that told R to print the iteration number every 100 or 200 iterations would be perfect. Does anyone know something like this? I've never known how to print anything within a for loop before the

Hello, Is there a way to get a single TRUE or FALSE statement from comparing two vectors? For example, c(1,2,3) == c(1,2,3) produces TRUE TRUE TRUE where I would like it to produce only TRUE for use in an if statement. Likewise, when two vectors are not exactly identical (in all elements) I would like a single FALSE result, as opposed to c(1,2,3) == c(1,2,5) TRUE TRUE FALSE Any ideas?

Hello, I have a longitudinal dataset where each individual has a different number of entries. Thus, it is of the following structure: x <- runif(12) id.var <- factor(c(rep("D1",4),rep("D2",2),rep("D3",3),rep("D4",3))) dat <- as.data.frame(x) dat$id.var <- id.var dat > dat x id.var 1 0.9611269 D1 2 0.6738606 D1 3

Hello, I'm clearly confused about the merge function. In the following r <- merge(x,y,all.x=T,all.y=F) my y vector has only unique values (no duplicates). So I don't understand how this can ever generate an r which is of greater length than x. I thought the default behavior was only matching rows are included, but that using all.x=T included rows with unmatched x's as well. If

Hello, I hate to ask a question which is directly addressed in the documentation, but can someone please give me an example of how to change the maximum number of iterations used by lmer. I'm having a hard time understanding this: control a list of control parameters. See below for details. control a named list of control parameters for the estimation algorithm, specifying only

Hello, I'm attempting to install the splm package from R-Forge. https://r-forge.r-project.org/R/?group_id=352 The page says, "In order to successfully install the packages provided on R-Forge, you have to switch to the most recent version of R..." It later says "To install this package directly within R type: install.packages("splm",

Dear r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

Hello, A colleague is trying to do a fairly complicated power analysis for a project. The project would be evaluating random assignment to one of three conditions within each of 8 sites. The dependent variable would be binary (we do not care at this point whether it would be analyzed with logit or probit). We can simulate the data in a bunch of iterations and do analyses in each iteration, but

Hello, I'd like to plot an empirical cumulative distribution function, except instead of the fraction of values < x, I'd like the fraction of values > x. I think this can be done using the ecdf function in {Hmisc}. I installed the package and loaded it. However, when following the example given in the documentation, I get an error: x <- rnorm(100) ecdf(x,what='1-F')

Hi, I would like to estimate a VAR of the form: Ay_t = By_t-1 + Cy_t-2 + ... + Dx_t + e_t Where A is a non-diagonal matrix of coefficients, B and C are matricies of coefficients and D is a matrix of coefficients for the exogenous variables. I don't think the package {vars} can do this because I want to include contemporaneous cross-variable impacts. So I want y1_t to affect y2_t and I

Hi to every body. I would like assistance on how to implement the log-log link function for binary response. Is there any package that implements it? Many thanks Endy [[alternative HTML version deleted]]

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf =

hi all why does the following not work??? this was someone elses code and i couldnt explain why it doesn't work. m=matrix(c(0,0),2,1) v=matrix(c(1,0,0,1),2,2) Y=function(X1,X2,mu=m,V=v) { X=matrix(c(X1,X2),2,1) a=(1/((2*pi)*sqrt(det(V))))*exp((-0.5)*(t(X-mu)%*%solve(V)%*%(X-mu))) a[1] } x1=seq(-1,1) x2=x1 Z=outer(x1,x2,FUN="Y",mu=m,V=v) persp(x1,x2,Z) my code:

I have coded a time series from simulated data: simtimeseries <- arima.sim(n=1024,list(order=c(4,0,0),ar=c(2.7607, -3.8106, 2.6535, -0.9258),sd=sqrt(1))) #show roots are outside unit circle plot.ts(simtimeseries, xlab="", ylab="", main="Time Series of Simulated Data") # Yule ---------------------------------------------------------------------------- q1 <-

The R translation teams have done a great job in making R usable for people who do not have English as their mother tongue. However, even within English speaking countries, there are groups which have trouble with the language, and it may be valuable to support the Sociolects of these groups too. Thanks to a generous contribution from Lars Polifo, these features will be made available in an

Is there an efficient way to use rbind() with the five dataframes described in the following example: a <- c(1:5) list.foo <- lapply(a, function(x) data.frame(beta=a*rnorm(10), deta=a*rnorm(10))) big.data.frame <- rbind(list.foo[[1]], list.foo[[2]], list.foo[[3]], list.foo[[4]], list.foo[[5]]) #is there an easier method? For example, I naively thought you could do something like

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

Dear All, Can you help me, with the code below how do I obtain the fisher information from it. Is my q<-replicate(1000,x) the right way to do simulation. thank you. x<-rweibull(100,0.8,1.5) q<-replicate(1000,x) z<-function(p){ beta<-p[1] eta<-p[2] log1<-(n*log(beta)-n*beta*log(eta)+(beta-1)*sum(log(x))-sum((x/eta)^beta)) return(-log1) } zz<-optim(c(0.5,0.5),z) zz Chris

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =