Using harvestr for replicable simulations

library(harvestr)
library(plyr)
library(MCMCpack)

## Loading required package: coda

## Loading required package: MASS

## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)

## ## Copyright (C) 2003-2016 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park

## ##
## ## Support provided by the U.S. National Science Foundation

## ## (Grants SES-0350646 and SES-0350613)
## ##

library(dostats)
print.data.frame <- function(x, ...){
  print(ascii(df, include.rownames = FALSE), type = 'rest')
}

Introduction

The harvestr package is a new approach to simulation studies that facilitates parallel execution. It builds off the structures available in the plyr, foreach and rsprng packages. What harvestr brings to the picture is abstractions of the process of performing simulation.

Process

The theme of harvestr is that of gardening, stemming from the idea that the pseudo-random numbers generated (RNG) in replicable simulation come from initial states called seeds. Figure 1 shows the basic process for harvestr.

(workflow.png)[The basic harvestr process]

The ideas are simple.

• gather(n, {[seed{]})} takes an integer for the number of seeds to generate.\ Optionally, the seed can be set for replicable simulations. This uses the rsprng library to initialize independent parallel random number streams.
• The seeds that are returned from gather are then fed into the farm function along with an expression to be generate data. farm returns a list of data frames each independently generated under each of the rng streams.
• The final step is to apply a function to analyze the data. This is done with the harvest command, which takes the data from farm and applies an analysis function to the dataset. In the case that the analysis is deterministic harvest is equivalant to llply from the plyr package. The difference is with stochastic analysis, such as Markov Chain Monte Carlo (MCMC), where harvest resumes the RNG stream where farm left off when generating the data.

The effect is the results can be taken in any order and independently, and the final results are the same as if each analysis was taken from start to end with setting a single seed for each stream.

Example 1 - Basic Example

Some learn best by example. Here I will show a simple example for the basic process. Here we will perform simple linear regression for 100 data sets. First off we gather the seeds. This step is separate to facilitate storing the seeds to be distributed along with research if necessary.

library(harvestr)
library(plyr)
seeds <- gather(100, seed=12345)

Second, we generate the data.

datasets <- farm(seeds, {
  x <- rnorm(100)
  y <- rnorm(100, mean=x)
  data.frame(y,x)
})

Then we analyze the data.

analyses <-  harvest(datasets, lm)

So what do we have in analyses? We have whatever lm returned. In this case we have a list of lm objects containg the results of a linear regression. Ussually we will want to do more to summarize the results.

library(dostats)
coefs <- t(sapply(analyses, coef))
adply(coefs,2, dostats, mean, sd)

##            X1        mean        sd
## 1 (Intercept) 0.009484538 0.1030893
## 2           x 1.006747591 0.1007218

Example 2 - Stochastic Analysis

That is very nice, but rather simple as far ananalyses go. What might be more interesting is to perform an analysis with a stochastic component such as Markov Chain Monte Carlo.

library(MCMCpack)
library(plyr)
posteriors <- harvest(datasets, MCMCregress, formula=y~x)
dataframes <- harvest(posteriors, as.data.frame)
X.samples  <- harvest(dataframes, `[[`, "x")
densities  <- harvest(X.samples, density)

plot(densities[[1]])
l_ply(densities, lines)