# Parallel Simulator Function for R ## Tips for setting up a simulation study - Do not use too many conditions - Do not vary conditions that change too much - For example, number of nodes in network studies change both sparsity, edge strength and dimension. Too much! Keep it fixed to one case - Try first using only 2 repititons, with nCores = 1 - Use browser() for debugging (with nCores = 1) - Try local first before using a cluster ## Example (see also examples folder) ```{r} # Install the package: # library("devtools") # install_github("sachaepskamp/parSim") library("parSim") # Some function we might use: bias <- function(x,y){abs(x-y)} # Run the simulation: Results <- parSim( # Any number of conditions: sampleSize = c(50, 100, 250), beta = c(0, 0.5, 1), sigma = c(0.25, 0.5, 1), # Number of repititions? reps = 100, # Parallel? nCores = 1, # Write to file? write = FALSE, # Export objects (only needed when nCores > 1): export = c("bias"), # R expression: expression = { # Load all R packages in the expression if needed # library(...) # Want to debug? Enter browser() and run the function. Only works with nCores = 1! # browser() # Enter whatever codes you want. I can use the conditions as objects. X <- rnorm(sampleSize) Y <- beta * X + rnorm(sampleSize, sigma) fit <- lm(Y ~ X) betaEst <- coef(fit)[2] Rsquared <- summary(fit)$r.squared # Make a data frame with one row to return results (multple rows is also possible but not reccomended): data.frame( betaEst = betaEst, bias = bias(beta,betaEst), Rsquared = Rsquared ) } ) # Analyze the results: library("ggplot2") library("tidyr") # We want to plot bias and R-squared. Let's first make it long format: Long <- gather(Results,metric,value,bias:Rsquared) # Make factors with nice labels: Long$sigmaFac <- factor(Long$sigma,levels = c(0.25,0.5,1), labels = c("Sigma: 0.025", "Sigma: 0.5", "Sigma: 1")) # Now let's plot: g <- ggplot(Long, aes(x = factor(sampleSize), y = value, fill = factor(beta))) + facet_grid(metric ~ sigmaFac, scales = "free") + geom_boxplot() + theme_bw() + xlab("Sample size") + ylab("") + scale_fill_discrete("Beta") print(g) ```

parSim0.1.5 package

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parSim