data_helpers | R Documentation |
Use adm_rows()
, obs_rows()
and add_covariates()
to create or modify a dataset from scratch, from a pre-existing dataset, or from a dataset stored into a 'mrgsolve' model.
Instead of importing a '.csv' file, or painfully build a data set with a call to data.frame()
and mind how to format the data, you can pass information about:
administrations with adm_rows()
,
observations with obs_rows()
,
covariates with add_covariates()
,
all being called jointly with a pipe (%>%
or |>
).
These functions can be used to create or modify a dataset as a proper data.frame, or to create or modify a dataset within a 'mrgsolve' model (@args$data
slot).
The latter is particularly useful in order to:
automatically use default administration and observation compartments,
automatically duplicate rows if there are several depot compartments,
automatically set rate = -2
if model has zero-order absorption pathways,
automatically duplicate rows if concentrations of Parent drug and Metabolite are observed together,
automatically add "Amount Of Last Administration" and "Time Of Last Administration" variables if these are covariates,
subsequently call mrgsim()
or mapbayest()
.
library(magrittr)
# First option: work with a data.frame
adm_rows(amt = 1000, cmt = 1, addl = 4, ii = 12) %>%
obs_rows(time = c(12.3, 45.6), DV = c(.111, .222), cmt = 2) %>%
obs_rows(time = 48, cmt = 2) %>%
add_covariates(BW = 90, SEX = 0, TOLA = TRUE)
# You can even inform "time" using date and hours:
adm_rows(amt = 1000, cmt = 1, addl = 4, ii = 12, .datehour = "2022-01-01 11:11:11") %>%
obs_rows(.datehour = "2022-01-02 22:22:22", DV = 0.111, cmt = 2)
# Second option: work with a dataset within a 'mrgsolve' model
mod <- exmodel(add_exdata = FALSE)
# call `mrgsolve::see(mod)` to see how default compartment were coded
adm_cmt(mod)
obs_cmt(mod)
mod %>%
adm_rows(amt = 10000) %>%
obs_rows(time = c(1.5, 4.4, 7.5, 24.6), DV = c(91.2904, 110.826, 79.384, 20.6671)) %>%
# get_data() # for curiosity, you can extract the data set at this step
mapbayest()
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