similar to: Model object, when generated in a function, saves entire environment when saved

Another solution is to only save the parts of the model object that interest you. As long as they don't include the formula (which is what drags along the environment it was created in), you will save space. E.g., tfun2 <- function(subset) { junk <- 1:1e6 list(subset=subset, lm(Sepal.Length ~ Sepal.Width, data=iris, subset=subset)$coef) } saveSize(tfun2(1:4)) #[1] 152 Bill

One way around this problem is to make a new environment whose parent environment is .GlobalEnv and which contains only what the the call to lm() requires and to compute lm() in that environment. E.g., tfun1 <- function (subset) { junk <- 1:1e+06 env <- new.env(parent = globalenv()) env$subset <- subset with(env, lm(Sepal.Length ~ Sepal.Width, data = iris, subset =

Thanks so much for all this. The first solution is what I'm going with as I want the terms object to come along so that predict still works. On Wed, Jul 27, 2016 at 12:28 PM, William Dunlap via R-devel < r-devel at r-project.org> wrote: > Another solution is to only save the parts of the model object that > interest you. As long as they don't include the formula (which is

Reviving an old thread. I haven't noticed this be a problem for a while when saving RDS's which is great. However, I noticed the problem again when saving `qs` files (https://github.com/traversc/qs) which is an RDS replacement with a fast serialization / compression system. I'd like to get an idea of what change was made within R to address this issue for `saveRDS`. My thought is that

Greetings all, I am curious to know if either of these two sets of code is more efficient? Example1: ## t-test ## colA <- temp [ , j ] colB <- temp [ , k ] ttr <- t.test ( colA, colB, var.equal=TRUE) tt_pvalue [ i ] <- ttr$p.value or Example2: tt_pvalue [ i ] <- t.test ( temp[ , j ], temp[ , k ], var.equal=TRUE) ------------- I have three loops, i, j, k. One to test the all of

Hi, I have tried to follow the instructions in the ``save`` documentation and it doesn't seem to work (see below): mydata <- do.call(rbind, rep(iris, 10000)) con <- pipe("pigz -p8 > fname.gz", "wb"); save(mydata, file = con); close(con) # This runs R.utils::gunzip("fname.gz", "fname.RData", overwrite = TRUE) load("fname.RData") #

for example : I have files with the name "ma01.dat","ma02.dat","ma03.dat","ma04.dat",I want to read the data in these files into one data.frame flnm<-paste("obs",101:114,"_err.dat",sep="") newdata<-read.table(flnm,skip=2) data<-(flnm,skip=2) but the data only contains data from the flnm[1] I also tried as below : for

I have 100+ .csv files which have the basic format: > test X Substance1 Substance2 Substance3 Substance4 Substance5 1 Time1 10 0 0 0 0 2 Time2 9 5 0 0 0 3 Time3 8 10 1 0 0 4 Time4 7 20 2 1 0 5 Time5

ola, ive been looking at adding support for an -msave-args option for use on x86_64. the short explanation of it is that it makes x86_64 function prologues store their register arguments on the stack. the purpose of this is to make the arguments trivially accessible for things like stack traces with arguments. as per https://blogs.oracle.com/sherrym/entry/obtaining_function_arguments_on_amd64,

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

I have a data set where I want the correlations between 2 variables conditional on a students grade level. This code works just fine. by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete', method='pearson') However, this generates an error by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete',

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

Dear all This may be obvious, but I cannot get it working. I'm trying to subset & sort a data frame in one go. x <- iris x$Species1 <- as.character(x$Species) ##subsetting alone works fine with(x, x[Sepal.Length==6.7,]) ##sorting alone works fine with(x, x[order(Sepal.Length, rev(sort(Species1))),]) ##gets subsetted, but not sorted as expected with(x, x[(Sepal.Length==6.7) &

I am making a battery of levelplots and wireframes for several fitted models. I wrote a function that takes the fitted model object as the sole argument and produces these plots. Various strange behavior ensued, but I have identified one very concrete issue (illustrated below): when my figure-drawing function includes the addition of points/lines to trellis plots, some of the

Hello, I'll use part of the iris dataset for an example of what I want to do. > data(iris) > iris<-iris[1:10,1:4] > iris Sepal.Length Sepal.Width Petal.Length Petal.Width 1 5.1 3.5 1.4 0.2 2 4.9 3.0 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5

I would like to create an xyplot with varying y-axis limits and horizontal labels at the y-axis tickmarks. The following does not seem to work, although I think it should, going by the documentation for par. R version 2.5.1, Windows XP Prof. Thanks for a clue. Andreas Krause library(lattice) # axis labels for y-axis are horizontal xyplot(Sepal.Length ~ Sepal.Width | Species, data=iris) #

Dear all When using Sweave, I'm always hitting the same bump: I want to group repetitive calls in a function, but I want both the results and the function calls in the printed output. Let me explain myself. Consider the following computation in an Sweave document: summary(iris[,1:2]) cor(iris[,1:2]) When using these two calls directly, I obtain the following output: > summary(iris[,1:2])

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Hallo, I'd like to add grid lines to a lattice graph having 2 series of Y data. See these 2 examples: data(iris) [1] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales = "same",type="l", ) [2] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales =