similar to: debugging substitute function in R 2.11

Hi, this relates to the question "How to set a former environment?" asked yesterday. What is the best way to to return a function with a minimal environment from a function? Here is a dummy example: foo <- function(huge) { scale <- mean(huge) function(x) { scale * x } } fcn <- foo(1:10e5) The problem with this approach is that the environment of 'fcn' does not

Just checking in to see whether it is intended or not that assigning new formals to a function/closure causes any attributes to be dropped: EXAMPLE: > fcn <- structure(function() {}, foo="foo", class=c("foo")) > str(fcn) function () - attr(*, "srcref")=Class 'srcref' atomic [1:8] 1 18 1 30 18 30 1 1 .. ..- attr(*, "srcfile")=Classes

I would like to define a function at run-time, but I just can't make it work (please don't question why I want to do this, because that would be too much to explain). For example, I want to define the the following function foo <- function(x) { cat("Function", "foo", "was called with argument", x, ".\n") } However, I would like to

Hi, is there away to assure that a package is detached when R quits? I know about .Last(), but that requires the user to setup that function. What I am looking for is a way for the package to do this itself, without asking the user to edit "their" .Last(). From ?setHook I know that: "...when an R is finished, packages are not detached and namespaces are not unloaded, so

I want to create my own "[" function (for use on vectors, matrices, arrays, etc.), which calls the stock R "[", does some additional work, and then finally returns the modified result. But, how do I properly call the stock R "[" function? It takes a varying number of positional arguments, and its R-level closure is just: .Primitive("[") It's

Hi, forget about the below details. It is not related to the fact that the function is returned from a function. Sorry about that. I've been troubleshooting soo much I've been shoting over the target. Here is a much smaller reproducible example: x <- 1:10 y <- 1:10 + rnorm(length(x)) sp <- smooth.spline(x=x, y=y) ypred <- predict(sp$fit, x) # [1] 2.325181 2.756166 ...

Hi, I am trying to integrate a function which is approximately constant over the range of the integration. The function is as follows: > my.fcn = function(mu){ + m = 1000 + z = 0 + z.mse = 0 + for(i in 1:m){ + z[i] = rnorm(1, mu, 1) + z.mse = z.mse + (z[i] - mu)^2 + } + return(z.mse/m) + } > my.fcn(-10) [1] 1.021711 > my.fcn(10) [1] 0.9995235 > my.fcn(-5) [1] 1.012727 > my.fcn(5)

Dear R users, I have a data frame with a few thousand rows and several hundred numeric columns (plus a date column). For each row (day), I want to assign +/- 1 to the highest X absolute values, 0 to the other values, and save all that in a separate data frame. I have a working solution (below), however I find it rather slow. Is there something I could do to increase the speed? (The code is

Folks, I often build up R objects starting from NULL and then repeatedly using rbind() or cbind(). This yields code like: a <- NULL for () { onerow <- craft one more row a <- rbind(a, onerow) } This works because rbind() and cbind() are forgiving when presented with a NULL arg: they act like nothing happened, and you get all.equal(x,rbind(NULL,x)) or all.equal(x,cbind(NULL,x)).

Hi, All I'm trying to save the workspace as a Check Point for possible future come back. The "save.image" function works if the whole coding is in a main function: rm(list=ls()) t = 1 t = t+1 print(paste("before",t)) # [1] "before 2" save.image("tt.RData") t = t+1 print(paste("after",t)) # [1] "after 3"

Greetings, I love the R system and am sincerely grateful for the great effort the product and contributors are delivering. My question is as follows: I am trying to use S4 style classes but cannot write functions that modify an object because paramter passing is by value. For example I want to do this: setGeneric("setData", function(this,fcn,k){ standardGeneric("setData")

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 experts! I'd to fit data by 'nls' with me-supplied function 'fcn'. 1) I'd like 'fcn' to accept arbitrary arguments, i.e. I defined it as f(...) {<body>}. (Ok, that's not actually impotant). 2) Second, I would NOT like to supply every parameter in the formula. To illustrate this, let's look at the last example of 'nls' help

Here's a dummy example that I think illustrates the problem: toto <- function() { if (runif(1) < 0.5) function(a) a else function(a,b) a+b } > fcn <- toto() > fcn(1,2) [1] 3 > fcn <- toto() > fcn(1,2) [1] 3 > fcn <- toto() > fcn(1,2) Error in fcn(1, 2) : unused argument (2) How can you use the returned function, if you get different arguments?

When I run the following function HQ2 <- function(n) { nv <- 6 * sqrt(n) fcn <- function(z) { pchisq(z^2 / 36, n - 1) * dnorm(nv - z) } ## I want the integral from 0 to infinity: f.Inf <- integrate(fcn, 0, Inf) ## Doc: "Don't do this": f.100 <- integrate(fcn, 0, 100) cbind(f.Inf, f.100) } I get, for n = 9 and

I am having the following error in my function function(theta,reqdIRR) { theta1<-theta[1] theta2<-theta[2] n<-length(reqdIRR) constant<- n*(theta1+theta2) sum1<-lapply(reqdIRR*exp(theta1),FUN = sum) sum2<-lapply(exp(theta2 - reqdIRR*exp(theta1)),FUN = sum) sum = sum1 + sum2 log.fcn = constant - as.numeric(sum) result = - log.fcn return(result) } *error :

Hello: I am a bit confused by this problem. Can anyone give me some advice on this I would greatly appreciate it. Thank you for all your help. Need to create a for loop that saves the estimate of pi from each 0f 100 separate iterations and store it in a numeric vector (of length 100). The for loop should be placed in a function that allows the user to vary the sample size, the simulation size,

Hi, I'm currently in the process of writing an R-installation SOP for my company. As part of that process I'm using the recommendations from the 'R Installation and Administration' document, section 3.2, "Testing an installation". This is done on an XP machine, using the latest binary of 2.11.0. The binary is downloaded and then installed from the installer. I then

I have one equation, two unknowns, so I am trying to build the solution set by running through possible values for one unknown, and then using uniroot to solve for the accompanying second solution, then graphing the two vectors. p0 = .36 f = function(x) 0.29 * exp(5.66*(x - p0)) f.integral = integrate(f, p0, 1) p1 = p0 + .01 i = 1 n = (1 - p0)/.01 p1.vector = rep(0,n) p2.vector = rep(0,n) for (i

Hi, I'm having trouble using virtual register in the X86 backend. I implemented a new intrinsic and I use a custom inserter. The goal of the intrinsic is to set the content of the stack to zero at the end of each function. Here is my code: MachineBasicBlock * X86TargetLowering::EmitBURNSTACKWithCustomInserter( MachineInstr *MI, MachineBasicBlock