similar to: Appropriate method for sharing data across functions

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",

Is it possible to avoid the loop in the following function (or make the function otherwise more efficient) and can someone point me to a possible solution? (It would be great if hours could be reduced to seconds :-). # --------------------------------------------- RanEigen=function(items=x,cases=y,sample=z) { X=matrix(rnorm(cases*items),nrow=cases,byrow=F) S=crossprod(X-rep(1,cases) %*%

The update of VR by downloading VR_7.1-6.zip and using install.packages (from local zip files) fails with the following error message: Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `VR/DESCRIPTION' Other packages can be installed without problems, except of dse_2003.4-1 with a similar error message. Why? Operating System:

Dear All, I have a codes which calculates the result of Ripley's K function of my data. I want to repeat this process 999 times. However, i am getting an error when i use the "for i in" function. Is there any way to repeat this analysis 999 times. Here are the codes i used ; data4 <- matrix(c(sample(id),data1),203,3) a <- data4[,1] random.case=data4[a==0,]

Hi, I have a dataset with x and y coordinates and in each point I have an identity of point, in some cases I can have more then one identity by point. My dataset is something like this: > x <- rep(c(1:4),4) > y <- rep(c(1:4),c(4,4,4,4)) > area1 <- sample(factor(rep(c("a","b","c","d"),4))) > area2 <-

Dear R users, I am running a maximum likelihood model with optim. I chose the simulated annealing method (method="SANN"). SANN is not performing bad, but I guess it would be much more effecive if I could set the `parscale' parameter. The help sais: `parscale' A vector of scaling values for the parameters. Optimization is performed on `par/parscale' and these

Dear all, Firstly, I do apologize if my question is simple and posted in the wrong place but I had no reply from the R-help mailing list (maybe it is too simple!). I was wondering why parscale is set to 20 in the "wild" function example used in ?optim. This function has only one parameter and if we set parscale equal to 1 then the solution near the global minimum is not found. I

Hi there, The optim(par, fn, ...parscale...) function in R requires 'parscale' which is defined as: "A vector of scaling values for the parameters. Optimisation is performed on 'par/parscale' and these should be comparable in the sense that a unit change in any element (??) produces a unit change in the scaled value". I am just not understanding the

Dear R users, I am trying to figure out the control parameter in "optim," especially, "fnscale" and "parscale." In the R docu., ------------------------------------------------------ fnscale An overall scaling to be applied to the value of fn and gr during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on

Hi everybody, I am using the L-BFGS-B method of the mle2() function to estimate the values of 6 parameters. mle2 uses the methods implemented in optim. As I got it from the descriptions available online, one can use the parscale parameter to tell R somehow what the values of the estimated parameters should be . . . Could somebody please help me understand what one has to do actually with the

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Dear all, I?m having some problems getting optim with method="Nelder-Mead" to work properly. It seems like there is no way of controlling the step size, and the step size seems to depend on the *difference* between the initial values, which makes no sense. Example: f=function(xy, mu1, mu2) { print(xy) dnorm(xy[1]-mu1)*dnorm(xy[2]-mu2) } f1=function(xy) -f(xy, 0,

Hi, When using method L-BFGS-B along with a parscale argument, should the lower and upper bounds provided be on the scaled or unscaled values? Thanks. Cheers, -- Seb

I am using the function optim and I get the error message ABNORMAL_TERMINATION_IN_LNSRCH. Reason for this could be a scaling problem. Thus, I used parscale in order to scale the parameters. But I still have the error message. For example, with parscale=c(rep(1,n), 0.01,1,0.01): return(optim(c(mu1,b,k,phi), neg2loglikelihood, method = "L-BFGS-B",

Dear list, I am brand new to R and using Dalgaard's (2002) book Introductory Statistics with R (thus, some of my terminology may be incorrect). I am fitting regression models and I want to use Hurvich and Tsai's AICC statistic to examine my regression models. This penalty can be expressed as: 2*npar * (n/(n-npar-1)). While you can obtain AIC, BIC, and logLik, I want to impose the AICC

Dear all, I'd like tweaking the ... arguments that one user can pass in my function for fitting a model. More precisely, my objective function is (really) problematic to optimize using the "optim" function. Consequently, I'd like to add in the "control" argument of the latter function a "ndeps = rep(something, #par)" and/or "parscale =

Hello, there, I got this error when i tried to run " data.kr <- surf.gls(2, expcov, data, d=0.7);" "Error: cannot allocate vector of size 382890 Kb Execution halted" My data is 100x100 grid. the following is the summary of "data": > summary(data); x y z Min. : 1.00 Min. : 1.00 Min. :-1.0172 1st Qu.: 26.00

Dear R Users on a self-written function for calculating maximum likelihood probability (plz check function code at the bottom of this message), one value, wden, suddenly jump to zero. detail info as following: w[11]=2.14 lnw =2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182 0.178 0.179... w[11]=2.14 lnw=2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182

Can someone show me how to specify zero inflated poisson and zero inflated negative poisson models in R? I would like to replicate the example given in Long (1997: Regression Models for Categorical and Limited Dependent Variables) in Chapter 8.5 (pp. 242-247). TIA Dirk ************************************************* Dr. Dirk Enzmann Criminological Research Institute of Lower Saxony