similar to: expand.grid without expanding

Hi, I wonder how to pass several functions and their arguments as arguments to a function. For example, the main function is f = function(X ) { process1(X) ... process2(X) } I have a few functions that operate on X, e.g. g1(X, par1), g2(X, par2), g3(X, par3). par1, par2 and par3 are parameters and of different types. I would like to pass g1, g2, g3 and their arguments to f and g1,

After a few days of work, I think I nearly have it. Unfortunately, theta is unchanged after I run this (as a script from a file). I thought that theta would contain the fitted parameters. The goal here is to find the least squares fit according to the function defined as "rss" subject to the constraints defined as ui and ci. I defined ui and ci to (hopefully) force par2 and par3

Hi, I wonder how to pass several functions and their arguments as arguments to a function. For example, the main function is f = function(X ) { process(X) ... process(X) } I have a few functions that operate on X, e.g. g1(X, par1), g2(X, par2), g3(X, par3). par1, par2 and par3 are parameters and of different types.

Hi, I am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

Dear all, Using: library(vegan) data(BCI) mod <- radfit(BCI[1,]) mod RAD models, family poisson No. of species 93, total abundance 448 par1 par2 par3 Deviance AIC BIC Null 39.5261 315.4362 315.4362 Preemption 0.042797 21.8939 299.8041 302.3367 Lognormal 1.0687 1.0186 25.1528 305.0629 310.1281

Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =

Hi, I have a data frame DATA, which (simplified of course) looks like this: know1 = c("Y","N","N","Y","N","N","Y","Y","N") par1=c(1,4,5,3,3,2,3,3,5) know2 = c("Y","Y","N","Y","N","N","N","Y","Y")

Hi R users: I'm trying to make a function where two of the parameters are functions, but I don't know how to put each set of parameters for each function. What am I missing? I try this code: f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){ force(fun1) force(fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2)

Dear R users: I have a problem about catch the value from function. I have following two functions (part): sbolus1 <- function() { ....... for( i in 1:Subject) { kel<-par1 Vd<-par2 PKindex<-sbolus1.out(PKtime,kel,Vd,defun,par1,par2,Dose,i) } savefile(PKindex) } sbolus1.out<-function(PKtime,kel,Vd,defun,par1,par2,Dose,i) { time<-PKtime$time

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

Hi R-devel, I am facing a situation where the number of options I would like to propose to the user is somewhat big (and could easily increase more and more as I will code up a little more - even coming to a point where an user should be able to implement his own options). What we have to handle options is the couple: options(par=value) and getOption("par") I was aking myselft what

Hi there again, I have a problem with the "parse"-command. First of all, I show you in a simplified way, what I am trying to do and what "R" answers: > test [1] "u.g$par1, u.g$par2" > mode(test) [1] "character" > ausdruck <- parse(text = test) Error in parse(file, n, text, prompt) : syntax error in "u.g$par1,"

Hi all, I'm trying to use lapply on a list with the following command: out<-lapply(mylist,myfun,par1=p,par2=d) (1) where myfun<-function(x,par1,par1) {.....} (2) now this function is in fact a wrapper for some Fortran code I have written so I think this might be the problem. When I call lapply() as in (1) I get the following message: Error in get(x,

Dear all, I'm calling Winbugs (1.4.3) through R2WinBugs (2.1-18 coda_0.14-7) to fit a switching random walk model, but come up with an instant trap with the log only displaying 'check('. I will paste the trap with session info below; I'd be very grateful for any ideas. Couple of leads: 1. I presume the problem relates to the r package itself or the way I call bugs(), because I

Dear R useRs, I have a problem with nls.lm function of minpackl.lm package. I need to fit the Van Genuchten Model to a set of data of Theta and hydraulic conductivity with nls.lm function of minpack.lm package. For the first fit, the parameter estimates keep changing even after 1000 iterations (Th) and I have a following error message for fit of hydraulic conductivity (k); Reason for

Dear R Helper Community! Thank you for all your help and suggestions. For your reference and any future person searching the archives, here is the solution that did what I wanted it to do. As background, my goal was to find the coefficients for the following equation form: y ~ c1 * x1 * x2 ^ -c2 *x3 ^ c3 where y, x1, x2, and x3 are observed data, and c1, c2 and c3 are regression

 Dear R-helpers, I'm a new R-user and I was trying to gain some experience with the GNLS function of the NLME package.  This is an extract from my dataset (it's a 432x6 data.frame) called "input", in the first column I have the values that I need to fit, while the remaining columns are input variables for the theoretical model, the function "mymodel" (which returns a

Hi, I would like some help regarding the LSR pass. It seems that it likes to duplicate address calculations as in the case above, which is highly undesirable on my target. I wonder if there is any way to tell LSR to not duplicate the code in cases like this? Or could I perhaps run CSE after LSR again? What is the logic behind this transformation? It seems that a LSR pass should not insert a

width <- 7 height <- 5 # create whatever device type # and have cex=1 before setting par(mai=) windows(width=width, height=height, rescale="R") oldmai <- par("mai") par(cex=1) par(mai=oldmai) plot.new() par(usr=c(0,2,0,2)) par(cex=2) par1 <- par() text(1,1,"there is hardly anything i know for sure\nneither of the future nor of the past\nnor should i know what

Dear R-list, I'm working with a data frame which dimensions are > dim(GERU) [1] 3468 318 and looks like > GERU[1:10,1:10] ped ind par1 par2 sex sta rs7696470 rs7696470.1 rs1032896 rs1032896.1 1 USA5854 2 0 0 2 1 4 4 1 1 2 USA5854 3 1 2 1 1 4 4 1 1 3 USA5854 4 1 2 2 2