Dear R-experts,
My reproducible example here below is not working because of an error message :
Erreur : vecteurs de mémoire épuisés (limite atteinte ?)
My code perfectly works when n=3000 or n=5000 but as soon as n=10000 my code
does not work anymore. By the way, my code takes a very long time to run.
How can I solve my 2 problems :
- Is there a way to make my code run much faster ?
- Is there a way to make my code work for n=10000 ?
Here is my sessionInfo
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS:
/Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK:
/Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] fr_CH.UTF-8/fr_CH.UTF-8/fr_CH.UTF-8/C/fr_CH.UTF-8/fr_CH.UTF-8
attached base packages:
[1] splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] remotes_2.1.0 RobStatTM_1.0.1 fit.models_0.5-14 hbrfit_0.02
Rfit_0.23.0 RobPer_1.2.2 rgenoud_5.8-3.0
[8] BB_2019.10-1 quantreg_5.51 SparseM_1.77 MASS_7.3-51.4
robustbase_0.93-5
loaded via a namespace (and not attached):
[1] quadprog_1.5-7 lattice_0.20-38 grid_3.6.1 MatrixModels_0.4-1
curl_4.0 Matrix_1.2-17 tools_3.6.1
[8] DEoptimR_1.0-8 compiler_3.6.1
# # # # # # # # # # # # #
install.packages( "robustbase",dependencies=TRUE )
install.packages( "MASS" ,dependencies=TRUE )
install.packages( "quantreg" ,dependencies=TRUE )
install.packages( "RobPer",dependencies=TRUE )
install.packages("remotes") remotes::install_github("kloke/hbrfit")
install.packages( "RobStatTM",dependencies=TRUE )
library(robustbase)
library(MASS)
library(quantreg)
library(RobPer)
library(hbrfit)
library(RobStatTM)
library("remotes")
my.experiment <- function() {
n<-10000
b<-runif(n, 0, 5)
z <- rnorm(n, 2, 3)
a <- runif(n, 0, 5)
y_model<- 0.1*b - 0.5 * z - a + 10
y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
HBR<-hbrfit(y_obs ~ b+z+a)
x<-model.matrix(~b+z+a)
y<-y_obs
fastTau <- FastTau(x=x, y=y)
w<-as.vector(x %*% fastTau$beta)
MSE_fastTau<-mean((w - y_model)^2)
MSE_HBR<-mean((HBR$fitted.values - y_model)^2)
return( c(MSE_fastTau,MSE_HBR) )
}
my.data = t(replicate( 10, my.experiment() ))
colnames(my.data) <- c("MSE_fastTau","MSE_HBR")
summary(my.data)
# # # # # # # # # # # # #
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