Appends a new empty cohort to the CTS state object and makes this new cohort the active cohort.
Arguments
- state
CTS state from
CTS_fetch_state()
Value
CTS state object containing a new cohort and that
cohort is set as the current active cohort. See the help for
CTS_fetch_state() for ===ELEMENT== format.
Examples
# For more information see the Clinical Trial Simulation vignette:
# https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
# None of this will work if rxode2 isn't installed:
library(formods)
if( Sys.getenv("ruminate_rxfamily_found") == "TRUE"){
# This will populate the session variable with the model building (MB) module
sess_res = MB_test_mksession()
session = sess_res[["session"]]
id = "CTS"
id_ASM = "ASM"
id_MB = "MB"
input = list()
# Configuration files
FM_yaml_file = system.file(package = "formods", "templates", "formods.yaml")
MOD_yaml_file = system.file(package = "ruminate", "templates", "CTS.yaml")
state = CTS_fetch_state(id = id,
id_ASM = id_ASM,
id_MB = id_MB,
input = input,
session = session,
FM_yaml_file = FM_yaml_file,
MOD_yaml_file = MOD_yaml_file,
react_state = NULL)
# Fetch a list of the current element
current_ele = CTS_fetch_current_element(state)
# You can modify the element
current_ele[["element_name"]] = "A more descriptive name"
# Defining the source model
state[["CTS"]][["ui"]][["source_model"]] = "MB_obj_1_rx"
current_ele = CTS_change_source_model(state, current_ele)
# Single visit
current_ele[["ui"]][["visit_times"]] = "0"
current_ele[["ui"]][["cts_config_nsteps"]] = "5"
# Creating a dosing rule
state[["CTS"]][["ui"]][["rule_condition"]] = "time == 0"
state[["CTS"]][["ui"]][["rule_type"]] = "dose"
state[["CTS"]][["ui"]][["action_dosing_state"]] = "central"
state[["CTS"]][["ui"]][["action_dosing_values"]] = "c(1)"
state[["CTS"]][["ui"]][["action_dosing_times"]] = "c(0)"
state[["CTS"]][["ui"]][["action_dosing_durations"]] = "c(0)"
state[["CTS"]][["ui"]][["rule_name"]] = "Single_Dose"
# Adding the rule:
current_ele = CTS_add_rule(state, current_ele)
# Appending the plotting details as well
current_ele[["ui"]][["fpage"]] = "1"
current_ele[["ui"]][["dvcols"]] = "Cc"
# Reducing the number of subjects and steps to speed things up on CRAN
current_ele[["ui"]][["nsub"]] = "2"
current_ele[["ui"]][["cts_config_nsteps"]] = "5"
# Putting the element back in the state forcing code generation
state = CTS_set_current_element(
state = state,
element = current_ele)
# Now we pull out the current element, and simulate it
current_ele = CTS_fetch_current_element(state)
#current_ele = CTS_simulate_element(state, current_ele)
# Next we plot the element
current_ele = CTS_plot_element(state, current_ele)
# Now we save those results back into the state:
state = CTS_set_current_element(
state = state,
element = current_ele)
# This will extract the code for the current module
code = CTS_fetch_code(state)
code
# This will update the checksum of the module state
state = CTS_update_checksum(state)
# Access the datasets generated from simulations
ds = CTS_fetch_ds(state)
# CTS_add_covariate
state[["CTS"]][["ui"]][["covariate_value"]] = "70, .1"
state[["CTS"]][["ui"]][["covariate_type_selected"]] = "cont_lognormal"
state[["CTS"]][["ui"]][["selected_covariate"]] = "WT"
current_ele = CTS_add_covariate(state, current_ele)
# Creates a new empty element
state = CTS_new_element(state)
# Delete the current element
state = CTS_del_current_element(state)
}
#> → ASM: including file
#> → ASM: source: file.path(system.file(package="onbrand"), "templates", "report.docx")
#> → ASM: dest: file.path("config","report.docx")
#> → ASM: including file
#> → ASM: source: file.path(system.file(package="onbrand"), "templates", "report.pptx")
#> → ASM: dest: file.path("config","report.pptx")
#> → ASM: including file
#> → ASM: source: file.path(system.file(package="onbrand"), "templates", "report.yaml")
#> → ASM: dest: file.path("config","report.yaml")
#> → ASM: State initialized
#> → ASM: module isgood: TRUE
#> → MB: including file
#> → MB: source: file.path(system.file(package="onbrand"), "templates", "report.docx")
#> → MB: dest: file.path("config","report.docx")
#> → MB: including file
#> → MB: source: file.path(system.file(package="onbrand"), "templates", "report.pptx")
#> → MB: dest: file.path("config","report.pptx")
#> → MB: including file
#> → MB: source: file.path(system.file(package="onbrand"), "templates", "report.yaml")
#> → MB: dest: file.path("config","report.yaml")
#> ! MB: User-defined model: /Users/jmh/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)
#> ! MB: User-defined model: /Users/jmh/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)
#> → MB: module checksum updated:24933f86b657b9503f22440e8c4d3cac
#> → MB: State initialized
#> → MB: loading model idx: 1
#>
#>
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → MB: model checksum updated: 8b3f3b8af91627c0483413c0e0442669
#> → MB: module checksum updated:286a139c83f58035b4e9dc5acf49d038
#>
#>
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → MB: model checksum updated: 5f1b23b2d9a79cf5846ff0d31aa546e5
#> → MB: module checksum updated:0bdd4b6718625094549797c04d55d98e
#> → MB: -> setting name: One compartment model
#> → MB: -> setting time scale: hours
#> → MB: -> setting base from: user
#> → MB: -> setting catalog selection:
#> → MB: -> setting base model id: manual
#> → MB: -> setting base model name: manual
#> → MB: model checksum updated: dabcf0060488da635fd86bd7199eeaac
#> → MB: module checksum updated:d92ba98b47330355c06634c9d161c53e
#> → MB: added element idx: 1
#> → MB: loading model idx: 2
#>
#>
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → MB: model checksum updated: 581647073aa247db2cfb62a7f8824341
#> → MB: module checksum updated:76bf26c1c68ea7d0ba734efe07932ca9
#> → MB: -> setting name: PK Biomarker
#> → MB: -> setting time scale: days
#> → MB: -> setting base from: user
#> → MB: -> setting catalog selection:
#> → MB: -> setting base model id: manual
#> → MB: -> setting base model name: manual
#> → MB: model checksum updated: 285c2f14ea0a75939504345a76fa647f
#> → MB: module checksum updated:defa767de59af224ca683c7c6cf2983a
#> → MB: added element idx: 2
#> → MB: module isgood: TRUE
#> → CTS: including file
#> → CTS: source: file.path(system.file(package="onbrand"), "templates", "report.docx")
#> → CTS: dest: file.path("config","report.docx")
#> → CTS: including file
#> → CTS: source: file.path(system.file(package="onbrand"), "templates", "report.pptx")
#> → CTS: dest: file.path("config","report.pptx")
#> → CTS: including file
#> → CTS: source: file.path(system.file(package="onbrand"), "templates", "report.yaml")
#> → CTS: dest: file.path("config","report.yaml")
#> → CTS: source model change detected
#> → CTS: > covariates reset
#> → CTS: cohort checksum updated: 7662291cfda678368edef72abe61a255
#> → CTS: module checksum updated: 351269c95d7155b66deffb91bd3f52fc
#> → CTS: State initialized
#> → CTS: add rule success
#> → CTS: rule added
#> → CTS: cohort checksum updated: 74248f6eabcfb16860f02749a18b168e
#> → CTS: module checksum updated: fd6263cc4ded992f3bf2c1d5d19289cd
#> → CTS: CTS_plot_element()
#> → CTS: # Plotting timecourse
#> → CTS: CTS_obj_1_fgtc =
#> → CTS: plot_sr_tc(sro = CTS_obj_1_simres,
#> → CTS: xcol = "time",
#> → CTS: xlab_str = "Time",
#> → CTS: fncol = 4,
#> → CTS: fnrow = 2,
#> → CTS: dvcols = "Cc",
#> → CTS: fpage = 1)
#> → CTS:
#> → CTS: # Plotting events
#> → CTS: CTS_obj_1_fgev =
#> → CTS: plot_sr_ev(sro = CTS_obj_1_simres,
#> → CTS: xcol = "time",
#> → CTS: xlab_str = "Time",
#> → CTS: fncol = 4,
#> → CTS: fnrow = 2,
#> → CTS: evplot = 1,
#> → CTS: fpage = 1)
#> → CTS: No simulation available, you need to run the simulation first.
#> → CTS: cohort checksum updated: 05b72f93b911971f195d551ae9c1075a
#> → CTS: module checksum updated: ae803f1aee75d64433552081b34f7ced
#> → CTS: source model change detected
#> → CTS: > covariates reset
#> → CTS: cohort checksum updated: f2736e3fa1b53e235b00e78e2ee13b37
#> → CTS: module checksum updated: 91c41ca786f5e53ce2772cd97943fe30