Hi,
I want to compare some models. As I have noticed, they generate 100 simulated data sets and run the models on each one of those data sets and average the results. As you know, running a model 100 times is way too much. I'm sure there must be some automatic methods for that. Do you have any idea? I read about Mont Carlo algorithm but I'mnot sure how to use it. Maybe I should define a loop?
Thank you
•
written
by
statfa
1 answer
You could save the model formulae in a list object in R, and then use mclapply() (https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/mclapply.html) to run the models in parallel.
For further information, take a look at my final section of code here: R functions edited for parallel processing
Log in to answer this question.
-
How to perform hypothesis testing on contingency tables and compare with the null distribution?
written by RK •
-
How to generate BED files with 3D coordinates?
written by jetson •
-
Benchmarking different DESeq2 design models
written by jdhindsa1999 •
-
Code for simulation of data sets
written by lorenzo •
-
How to tell the best model for ML analysis using RaxML?
written by MAPK
-
What GTF file should I use in htseq?
written by statfa
-
What's the criteria to simulate RNA-seq read counts table
written by statfa
-
How to use log2FoldChange in RNA-seq DE analysis
written by statfa
-
What does benchmark method exactly mean?
written by statfa
-
Forum: Regression simulation with varying parameters
written by ana.cd •
