code_setseeds.qmd

# Setting Seeds for Reproducibility

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Any time you call a random varible or random sample (e.g. `rnorm` or `sample` in R), your code will not be reproductible unless you set a seed. sets the starting number used to generate a sequence of random numbers -- it ensures that you get the same result if you start with that same seed each time you run the same process.

The function `set.seed` can make your code reproducible, but you have to call it on the line before you ask R to do anything random.

Try:

    set.seed(98234)
    rnorm(100)
    set.seed(98234)
    rnorm(100)

And compare it to:

    set.seed(98234)
    rnorm(100)
    rnorm(100)

If you are running a large number of replicate simulations, it's important to make them reproducible. A good practice is to think about what parameters you are going to model and make a giant table. Here is an example for a simulation with three replicates of two different sets of parameters:

| **Seed** | **Rep** | **Param 1** | **Param 2** |
|:---------|:--------|:------------|:------------|
| 98234    | 1       | 0.5         | 1.2         |
| 98235    | 2       | 0.5         | 1.2         |
| 98236    | 3       | 0.5         | 1.2         |
| 98237    | 1       | 0.9         | 1.5         |
| 98238    | 2       | 0.9         | 1.5         |
| 98239    | 3       | 0.9         | 1.5         |

As you loop through this table, you can use the Seed as the `mySeed` for your simulations. However, there are bad and good ways to take the seed from this table and incorporate it into your simulations.

### **Bad Practice:** {.unnumbered}

    set.seed(mySeed)
    a<-rnorm(100)
    set.seed(mySeed)
    b<-rnorm(100)
    set.seed(mySeed)
    c<-rnorm(100)

Try this in R. *What values do you get for a, b, and c?*

### **Good practice:** {.unnumbered}

    set.seed(mySeed)
    sim_seeds <- round(runif(100)*10000000) 
      # may need more than 100 if there are more than 
      # 100 places in your sim where you set seeds
    set.seed(sim_seeds[1])
    a<-rnorm(100)
    set.seed(sim_seeds[2])
    b<-rnorm(100)
    set.seed(sim_seeds[3])
    c<-rnorm(100)