similar to: Function not working

Hi all, I had a weird problem with xyplot and I am wondering if anyone can help me figure out what's wrong here. Basically if I call xyplot() inside a function and I call quartz() after, then the previous xyplot() does not do anything. A short demo of the issue is like this: library(lattice) plot.trends <- function() { mat <- data.frame(cbind(rnorm(100), rnorm(100))) names(mat)

Why should it make an exception for cases where the about-to-be-assigned-to name is present in the global environment? I think it should warn or give an error if the altered variable is in any environment on the search list. -Bill On Sun, Mar 19, 2023 at 10:54?AM Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > I think that should be the default behaviour. It's pretty late to

I'd like to be able to prevent the <<- assignment operator ("super assignment") from assigning to the global environment unless the variable already exists and is not locked. If it does not exist or is locked, I'd like an error to be produced. This would allow me to evaluate expressions with this temporarily set to protect against mistakes. For example, I'd like to

I think that should be the default behaviour. It's pretty late to get that into R 4.3.0, but I think your proposal (with check.superassignment = FALSE being the default) could make it in, and 4.4.0 could change the default to TRUE. Duncan On 19/03/2023 12:08 p.m., Henrik Bengtsson wrote: > I'd like to be able to prevent the <<- assignment operator ("super >

Hi list, I want to find the total maximum resources I can spend given a set allocation proportion and some simple budget constraints. However, I get suboptimal results via lp and friends (i.e. lpSolve and simplex in the linprog and boot) . For example: library(lpSolve) proportions = c( 0.46, 0.28, 0.26) constraints = c( 352, 75, 171) lp(objective.in = proportions, const.mat =

I ran into a problem with "subset" while working at home that I am not sure is a bug or not. I defined a custom function to take a data frame of tree positions, sizes, and types (containing the columns TRT, COMP, PLOT, X, Y, DBH and CON) and wanted to make a stem map of the plot using different symbols and colors to represent the different types of tree stems. I copied a bit of the

Dear Helpers, in which way I have to use the assignment-operator evaluating left to right with R 2.0.0? With R 2.0.0 (w2k-installation) the "superassignment" operator '<<-' seems to have a different behaviour compared with R 1.9.1 : > x<<-4 > x [1] 4 > attr(x,'y')<<-5 Error: Object "x" not found > Using earlier

I would use replicate() to do an operation with random numbers repeatedly: ``` mysim <- replicate(10, { two.mat <- matrix(rnorm(4), 2, 2) four.mat <- matrix(rnorm(16), 4, 4) list(two.mat = two.mat, four.mat = four.mat) }) ``` which should give you a matrix-list. You can slice this matrix-list just like normal, then cbind it in one step: ``` two.mat <-

Dear community, I had been looking for an easy way to produce latex tables from R output. xtable() and the package apsrtable produce good outputs but they are not exactly what I was looking for. I wrote this code that generates regression tables from multiple R linear models. I want to share it because it might be useful for someone else, and because I would appreciate comments on how to

I have to say <<- is a core debugging tool when assigning into the global environment. I suppose I could use assign but that would be somewhat annoying. That said I'm still for this change, the vast overwhelming number of times that <<- is in my package code - already rare but it does happen - it would absolutely be a bug (typo most likely) for it to get to the global environment

Error in model.frame When I run the following nls model an error message appears and I dont know how to solve that. Could you help me?? mat = c(1,2,3,4,5,6,7,8,9,12,16,24,36,48,60) for (i in 1:length(j30)) { bliss = nls(c(j[i,1:length(mat)]) ~ b0 + b1*((1-exp(-k1*mat))/(k1*mat)) + b2*(((1-exp(-k2*mat))/(k2*mat))-exp(-k2*mat)), start = list(k1=0.1993, k2=0.1993, b0= 22.0046,

Hi!! I am trying to multiply 5 matrices and then using the inverse of that matrix for further computation. I read about precision problems from the archives and the suggestion was to use as.numeric while computing the products. I am still having problems with the results. Here is how I am using it #Mn.mat<-(T.mat %*% Rz.mat %*% Q.mat %*% Rz.mat %*% T.mat) # I was doing this in one step

I am trying to calculate the weighted mean for a of 10 deciles and I get an error: > decile <- tapply(X=mat$trt1m, INDEX=mat$Rank, FUN=weighted.mean, w=mat$mcap) Error in FUN(X[[1]], ...) : 'x' and 'w' must have the same length All three of my inputs have the same length, as shown below, and the weighted.mean calculation works by itself, just not in tapply() >

Hola tengo una tabla de tipo data.frame "datos" con la siguiente estructura, simplificada en número de niveles por variable, para crear un ejemplo más sencillo: id anio t_8a t_10a t_12a rankf8 rankf10 rankf12 1 1 100 220 220 NA NA NA 2 1 140 350 350 NA NA NA 3 2 55 165 165 NA NA NA 4 2 60 200 200 NA NA NA 5 2 100 NA NA NA NA NA 6 3 NA 350 350 NA NA NA También tengo una matriz,

> > I am looking for a more elegant way to write below code. > > #Simulation results have different dimensions > mysim <- lapply(1:10, function(y) { > two.mat <- matrix(rnorm(4), nrow = 2) > four.mat <- matrix(rnorm(16), nrow = 4) > list(two.mat = two.mat, four.mat = four.mat) #results with different dimensions > }) > > #Collect different

I have a matrix and am trying to write a code to 1. identify all neg values along each line 2. for each neg value I need to identify min(j+3) 3. end with this code: eq[i,j]<- ifelse(mat.r[i,j] < (0.5*mat.s[i,j]), mat.all[i,j], 0) This is the code I have so far. I have tried several different methods but I keep getting the same error message that the condition has length >1 and only

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Hi everyone, I have the following problem: I want to perform a LDA with the function lda(). My data object mat.data is a matrix with dimensions > dim(mat.data) [1] 1228 44 and my grouping vector grp has length 1228: > length(grp) [1] 1228 Every time I call lda(), the following error message occurs: > lda(mat.data,grp) Error in lda.default(x, grouping, ...) : length of dimnames [2]

Dear R-help, I have been trying really hard to generate the following vector given the data (x) and parameter (alpha) efficiently. Let y be the output list, the aim is to produce the the following vector(y) with at least half the time used by the loop example below. y[1] = alpha * x[1] y[2] = alpha^2 * x[1] + alpha * x[2] y[3] = alpha^3 * x[1] + alpha^2 * x[2] + alpha * x[3] ..... below are

Two friend reported me a problem, which I can't solve: (I run R-1.0.0, Debian Linux) They hava a function "corr.matrix" (see end of mail), and when they create a 173x173 matrix with this function V <- corr.matrix(0.3, n=173) V1 <- qr.solve(V) reports: Error in qr(a, tol = tol) : NA/NaN/Inf in foreign function call (arg 1) For n < 173, qr.solve returns the correct