Get up to speed with the required R packages and data – see Part I, Part II and Part III.
First, we need an empty matrix with 51 columns, an initial value of $1 and intuitive column names.
We will use the rep() function to create 51 columns with a 1 as the value and set_names() to name each column with the appropriate simulation number.
sims <- 51
starts <-
rep(1, sims) %>%
set_names(paste(“sim”, 1:sims, sep = “”))
set_names(paste(“sim”, 1:sims, sep = “”))
Take a peek at starts to see what we just created and how it can house our simulations.
head(starts)
sim1 sim2 sim3 sim4 sim5 sim6
1 1 1 1 1 1
tail(starts)
sim46 sim47 sim48 sim49 sim50 sim51
1 1 1 1 1 1
51 columns, with a value of 1 in one row. This is where we will store the results of the 51 simulations.
Now we want to apply simulation_accum_1 to each of the 51 columns of the starts matrix and we will do that using the map_dfc() function from the purrr package.
map_dfc() takes a vector, in this case the columns of starts, and applies a function to it. By appending dfc() to the map_ function, we are asking the function to store each of its results as the column of a data frame (map_df() does the same thing, but stores results in the rows of a data frame). After running the code below, we will have a data frame with 51 columns, one for each of our simulations.
We also need to choose how many months to simulate (the N argument to our simulation function) and supply the distribution parameters as we did before. We do not supply the init_value argument because the init_value is 1, that same 1 which is in the 51 columns.
Stay tuned for the next installment, in which Jonathan will apply simulation_accum_1 to each of the 51 columns of the starts matrix and will do that using the map_dfc() function from the purrr package.
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