Generate sample data from all DGP objects that were added
to the Experiment, including their varied params. Primarily useful
for debugging. Note that results are not generated in parallel.
Arguments
- experiment
An
Experimentobject.- n_reps
The number of datasets to generate per
DGP.- ...
Not used.
Value
A list of length equal to the number of DGPs in the
Experiment. If the Experiment does not have a
vary_across component, then each element in the list is a list
of n_reps datasets generated by the given DGP. If the
Experiment does have a vary_across component, then each
element in the outermost list is a list of lists. The second layer of
lists corresponds to a specific parameter setting within the
vary_across scheme, and the innermost layer of lists is of
length n_reps with the dataset replicates, generated by the
DGP.
Examples
# create DGP to generate data from normal distribution with n samples
normal_dgp <- create_dgp(
.dgp_fun = function(n) rnorm(n), .name = "Normal DGP", n = 10
)
# create DGP to generate data from binomial distribution with n samples
bernoulli_dgp <- create_dgp(
.dgp_fun = function(n) rbinom(n, 1, 0.5), .name = "Bernoulli DGP", n = 10
)
# initialize experiment with toy DGPs only
experiment <- create_experiment(name = "Experiment Name") %>%
add_dgp(normal_dgp) %>%
add_dgp(bernoulli_dgp)
# generate data from all DGPs (and vary across components if applicable)
# in experiment
generate_data(experiment, n_reps = 2)
#> $`Normal DGP`
#> $`Normal DGP`[[1]]
#> $`Normal DGP`[[1]][[1]]
#> [1] -0.1955145 0.9566731 0.1811654 0.6868962 -0.3522124 -1.6073684
#> [7] 0.4971132 0.8414329 -0.1565263 0.2352087
#>
#>
#> $`Normal DGP`[[2]]
#> $`Normal DGP`[[2]][[1]]
#> [1] -0.06666797 -1.13211916 -0.24935832 -0.10377363 0.05421331 -1.85656591
#> [7] 2.04234475 0.62420312 -0.27977636 -0.57399593
#>
#>
#>
#> $`Bernoulli DGP`
#> $`Bernoulli DGP`[[1]]
#> $`Bernoulli DGP`[[1]][[1]]
#> [1] 0 1 1 0 0 1 0 1 1 1
#>
#>
#> $`Bernoulli DGP`[[2]]
#> $`Bernoulli DGP`[[2]][[1]]
#> [1] 1 0 0 1 1 1 0 0 0 1
#>
#>
#>