Fixing set.seed for an entire session

Ensuring Reproducibility: Fixing set.seed() for an Entire R Session

Learn how to effectively manage random number generation in R by setting a global seed, crucial for reproducible research and simulations like Monte Carlo and agent-based models.

Reproducibility is a cornerstone of scientific research and robust simulations. In R, random number generation (RNG) is fundamental to many statistical analyses, Monte Carlo simulations, and agent-based models. However, without proper management, results involving randomness can vary between runs, making it difficult to verify findings or debug code. The set.seed() function is R's primary mechanism for controlling RNG, but its application can sometimes be misunderstood, leading to non-reproducible outcomes. This article will clarify how set.seed() works and demonstrate best practices for ensuring consistent results across an entire R session.

Understanding Random Number Generation in R

R's random number generation relies on a pseudo-random number generator (PRNG). A PRNG is an algorithm that produces a sequence of numbers that appear random but are actually determined by an initial value called a 'seed'. If you start the PRNG with the same seed, it will produce the exact same sequence of 'random' numbers every time. This deterministic nature is what allows for reproducibility.

flowchart TD
    A[Start R Session] --> B{Is `set.seed()` called?}
    B -->|No| C[Default Seed (System Time)]
    B -->|Yes| D[User-Defined Seed]
    C --> E[Generate Random Numbers]
    D --> E
    E --> F[Reproducible?]
    F -->|Yes| G[Consistent Results]
    F -->|No| H[Varying Results]

Flowchart illustrating the impact of set.seed() on random number generation.

By default, if set.seed() is not called, R initializes its PRNG using a seed derived from the current system time. This means that every new R session will start with a different seed, leading to different 'random' sequences. While this is fine for exploratory analysis where true randomness is desired, it's problematic for simulations or analyses that need to be exactly repeatable.

The Role of set.seed()

The set.seed() function takes an integer as its argument. This integer becomes the seed for R's internal random number generator. Once set.seed() is called, all subsequent calls to random number generation functions (e.g., runif(), rnorm(), sample(), rpois()) will produce the same sequence of numbers, provided the seed remains unchanged and the order of calls is identical.

# Example 1: Without set.seed()
print(runif(3))
print(runif(3))

# Restart R session and run again, results will differ

# Example 2: With set.seed()
set.seed(123)
print(runif(3))
set.seed(123)
print(runif(3))

Demonstrating the effect of set.seed() on reproducibility.

Ensuring Session-Wide Reproducibility

To ensure that your entire R session, or at least a significant block of code, is reproducible, the best practice is to call set.seed() once at the very beginning of your script or interactive session. This initializes the PRNG state, and all subsequent random operations will follow a predictable path.

# At the very beginning of your R script or session
set.seed(42) # A common choice, but any integer works

# --- Your Monte Carlo Simulation ---

# Generate random samples
sample1 <- rnorm(10)
sample2 <- runif(5)

# Perform agent-based modeling steps
# ... (which might involve more random calls)
agent_positions <- matrix(runif(100), ncol=2)

# Further analysis
mean(sample1)
median(sample2)

Setting a global seed for an entire R session to ensure reproducibility.

If you need to run multiple independent simulations, each requiring its own reproducible sequence, you can call set.seed() before each simulation block with a different seed value. However, for a single, continuous simulation or analysis, one call at the start is sufficient.

Advanced Considerations: Random Number Generator Types

R also allows you to specify the type of random number generator using the kind argument in set.seed(). While the default (Mersenne-Twister) is generally robust, for specific applications or compatibility with older code, you might need to change it. However, for most users, sticking with the default is perfectly fine.

# Setting seed with a specific RNG kind
set.seed(123, kind = "L'Ecuyer-CMRG")
print(runif(3))

# Resetting with default kind
set.seed(123, kind = "Mersenne-Twister")
print(runif(3))

Using different random number generator kinds with set.seed().

1. Identify Randomness

Review your R script or interactive session to identify all points where random numbers are generated (e.g., runif, rnorm, sample, rpois, or functions that internally use them).

2. Place set.seed()

Insert set.seed(your_chosen_integer) as the very first executable line of code in your R script or at the beginning of your interactive session, before any random number generation occurs.

3. Verify Reproducibility

Run your script or session multiple times. If set.seed() is correctly implemented, all random outputs should be identical across runs. If not, re-evaluate the placement of set.seed() and any external factors.

4. Document Your Seed

Always document the seed value you used, especially in research papers or shared code, to allow others to reproduce your exact results.