Nothing

- 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

```
# 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)
```

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