make_boot_datasets | R Documentation |
Creates bootstrap datasets and returns corresponding nm
objects. Requires
the necessary rsample
splitting objects to be present. See examples.
make_boot_datasets( m, samples = 10, data_folder = file.path(nm_dir("derived_data"), "bootstrap_datasets"), overwrite = FALSE, id_var = "ID", ... )
m |
An nm object. |
samples |
Number of samples. |
data_folder |
Folder (relative path) to store datasets. |
overwrite |
Logical (default = |
id_var |
Character (default = |
... |
Arguments passed to |
A tibble
with samples
rows and an nm object object column m
for
execution of the bootstrap.
## The following only works inside an NMproject directory structure and ## and requires NONMEM installed ## Not run: # create example object m1 from package demo files exdir <- system.file("extdata", "examples", "theopp", package = "NMproject") m1 <- new_nm(run_id = "m1", based_on = file.path(exdir, "Models", "ADVAN2.mod"), data_path = file.path(exdir, "SourceData", "THEOPP.csv")) d <- input_data(m1) ## in your dataset production script d <- d %>% mutate( WT_C = cut(WT, breaks = 2, labels = FALSE), STRATA = paste(SEX, WT_C, sep = "_") ) d_id <- d %>% distinct(ID, STRATA) set.seed(123) ## create large set of resamples (to enable simulation to grow ## without ruining seed) bootsplits <- rsample::bootstraps(d_id, 100, strata = "STRATA") dir.create("DerivedData", showWarnings = FALSE) bootsplits %>% saveRDS("DerivedData/bootsplit_data.csv.RData") ## In a model development script, the following, performs a ## 100 sample bootstrap of model m1 m1_boot <- m1 %>% make_boot_datasets(samples = 100, overwrite = TRUE) m1_boot$m %>% run_nm() ## the following bootstrap template will wait for results to complete m1_boot$m %>% nm_list_render("Scripts/basic_boot.Rmd") ## End(Not run)
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