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Create data frame of simulation results

Source: R/ensemble.R
as.data.frame.ensemble_stockflow.Rd

Convert simulation results to a data.frame.

Usage

# S3 method for class 'ensemble_stockflow'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  which = c("summary", "sims")[1],
  direction = "long",
  sim = NULL,
  condition = NULL,
  vars = NULL,
  type = NULL,
  ...
)

Arguments

x

Output of simulate().

row.names

NULL or a character vector giving the row names for the data frame. Missing values are not allowed.

optional

Ignored parameter.

which

Type of data to return. Either "summary" for a summary statistics, or "sims" for individual simulation trajectories. Defaults to "summary".

direction

Format of data frame, either "long" (default) or "wide".

sim

Indices of the individual trajectories to include if which = "sims". Defaults to NULL, which includes all trajectories. Including a high number of trajectories will create a large object.

condition

Indices of the conditions to include. Defaults to NULL, which includes all conditions.

vars

Variables to plot. Defaults to NULL to plot all variables.

type

Variable types to retain in the data frame. Must be one or more of 'stock', 'flow', 'constant', 'aux', 'lookup', or 'func'. Defaults to NULL to include all types.

...

Optional parameters

Value

A data.frame with simulation results. For direction = "long" (default), the data frame has three columns: time, variable, and value. For direction = "wide", the data frame has columns time followed by one column per variable.

See also

ensemble(), stockflow()

Examples

sfm <- stockflow("sir") |>
  # Randomize initial values of all stocks to show variation in the ensemble
  update(c(susceptible, infected, recovered),
    eqn = runif(1, min = 20, max = 800)
  )

# Run ensemble simulation with 3 simulations,
# saving only 20 timepoints per simulation
sims <- ensemble(sfm, n = 3, save_length = 20, save_sims = TRUE)
#> Starting ensemble simulation in "R" with 3 simulations.
#> ✔ Ensemble simulation completed in 0.2007 seconds.

# Get summary statistics in long format
df <- as.data.frame(sims)
head(df, n = 1)
#>   condition variable time     mean   median missing_count   quant1   quant2
#> 1         1 infected    0 314.1899 246.0185             0 36.78391 649.5417

# Get summary statistics in wide format
df_wide <- as.data.frame(sims, direction = "wide")
head(df_wide, n = 1)
#>   condition time mean.infected median.infected missing_count.infected
#> 1         1    0      314.1899        246.0185                      0
#>   quant1.infected quant2.infected mean.recovered median.recovered
#> 1        36.78391        649.5417       488.0095         488.5935
#>   missing_count.recovered quant1.recovered quant2.recovered mean.susceptible
#> 1                       0         389.0273         586.4952         211.2914
#>   median.susceptible missing_count.susceptible quant1.susceptible
#> 1           142.6226                         0           85.96698
#>   quant2.susceptible
#> 1           394.9842

# Get individual simulations in wide format
df_wide_sims <- as.data.frame(sims, which = "sims", direction = "wide")
head(df_wide_sims, n = 1)
#>   time sim condition infected recovered susceptible
#> 1    0   1         1 670.7798  488.5935    82.98511