Overview

Description

Generate replicates of the original data using random sampling with replacement.

Population parameters are then estimated from each replicate.

Usage

r <- bootmlx(project, nboot = 100, dataFolder = NULL, settings = NULL) 

Arguments

project
a Monolix project
nboot
number of bootstrat replicates (default= 100)
dataFolder
folder where the resampled datasets are stored (default = “bootstrap”)
settings
a list of optional settings


Examples

project <- "projects/warfarinPK1.mlxtran"
r <- bootmlx(project, nboot=10)
## [INFO] Results have been succesfully loaded
## [INFO] Results have been succesfully loaded
## Generating data sets...
## Generating projects with bootstrap data sets...
## [INFO] Results have been succesfully loaded
## Project 1/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 2/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 3/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 4/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 5/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 6/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 7/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 8/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 9/10 => already computed 
## [INFO] Results have been succesfully loaded
## Project 10/10 => already computed
print(r)
##       ka_pop    V_pop beta_V_lw70    Cl_pop beta_Cl_sex_1  omega_ka
## 1  0.5712162 7.733395   0.8347848 0.1266636    0.02254919 0.3262505
## 2  0.6088954 7.597509   0.9466322 0.1369339   -0.07309760 0.7546460
## 3  0.5656417 7.981152   1.2475913 0.1574498   -0.20440662 0.4040864
## 4  0.5179239 7.766416   0.8137808 0.1592522   -0.09850140 0.7678199
## 5  1.3633753 7.473373   0.7912810 0.1328755   -0.15438537 0.7417243
## 6  0.5496461 8.316222   1.1106699 0.1242055    0.04968830 0.4973725
## 7  0.5512048 7.928363   1.1300765 0.1154875    0.17133813 0.5492340
## 8  1.0695868 8.017304   0.9241754 0.1375020    0.02257928 1.1628917
## 9  0.5454635 7.817052   0.7225668 0.1590043   -0.11632045 0.7578875
## 10 0.4572127 8.033911   1.0927627 0.1152451    0.14723775 0.5369689
##       omega_V  omega_Cl         a1         b1
## 1  0.11594456 0.1678470 0.75917260 0.05423777
## 2  0.09857827 0.2892277 0.53703634 0.07931304
## 3  0.06238840 0.2430493 0.48166773 0.08622705
## 4  0.08194697 0.3105685 0.68181861 0.04285292
## 5  0.10386196 0.2864033 0.50838151 0.03901388
## 6  0.10625898 0.2184755 0.01508322 0.18708705
## 7  0.11086126 0.2753302 0.58239624 0.06453047
## 8  0.16166968 0.3394632 0.48543529 0.04753642
## 9  0.13748685 0.2704245 0.67359412 0.05737194
## 10 0.10654473 0.2639189 0.55739278 0.07739661

project <- './projects/warfarin_covariate1_project.mlxtran'

bootmlx(project)
bootmlx(project,  nboot = 5)
bootmlx(project,  nboot = 5, settings = list(covStrat = "sex"))
bootmlx(project,  nboot = 5, settings = list(newResampling = TRUE, covStrat = "sex"))
bootmlx(project,  nboot = 5,  settings = list(N = 50))
project <- './projects/iov2_project.mlxtran'
bootmlx(project,  settings = list(nboot = 110))
project <- './projects/40_gompertz_all.mlxtran'
bootmlx(project,  settings = list(nboot = 20, newResampling = TRUE))