Translate a monolix file to rxode2
Usage
monolix2rx(
mlxtran,
update = TRUE,
thetaMatType = c("sa", "lin"),
sd = 1,
cor = 1e-05,
theta = 0.5,
ci = 0.95,
sigdig = 3,
envir = parent.frame()
)Arguments
- mlxtran
file name for mlxtran to translate to rxode2
- update
is a boolean that represents if the final parameter estimates should be used for the translation (when present)
- thetaMatType
This lists the preferred source for
thetaMatcovariance matrix. By default it issafor simulated annealing, though you could uselinfor linearized covariance calculation. If only one is present, then use whatever is present- sd
Default standard deviation for between subject variability/inter-occasion variability that are missing.
- cor
Default correlation for missing correlations estimate
- theta
default population estimate
- ci
confidence interval for validation, by default 0.95
- sigdig
number of significant digits for validation, by default 3
- envir
represents the environment used for evaluating the corresponding rxode2 function
Examples
# First load in the model; in this case the theo model
# This is modified from the Monolix demos by saving the model
# File as a text file (hence you can access without model library)
# setup.
#
# This example is also included in the monolix2rx package, so
# you refer to the location with `system.file()`:
pkgTheo <- system.file("theo", package="monolix2rx")
rx <- monolix2rx(file.path(pkgTheo, "theophylline_project.mlxtran"))
#> ℹ integrated model file 'oral1_1cpt_kaVCl.txt' into mlxtran object
#> ℹ updating model values to final parameter estimates
#> ℹ done
#> ℹ reading run info (# obs, doses, Monolix Version, etc) from summary.txt
#> ℹ done
#> ℹ reading covariance from FisherInformation/covarianceEstimatesLin.txt
#> ℹ done
#> Warning: NAs introduced by coercion
#> ℹ imported monolix and translated to rxode2 compatible data ($monolixData)
#> ℹ imported monolix ETAS (_SAEM) imported to rxode2 compatible data ($etaData)
#> ℹ imported monolix pred/ipred data to compare ($predIpredData)
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> ℹ solving ipred problem
#> ℹ done
#> ℹ solving pred problem
#> ℹ done
pkgCov <- system.file("cov", package="monolix2rx")
rx <- monolix2rx(file.path(pkgCov, "warfarin_covariate3_project.mlxtran"))
#> ℹ integrated model file 'oral1_1cpt_TlagkaVCl.txt' into mlxtran object
#> ℹ updating model values to final parameter estimates
#> ℹ done
#> ℹ reading run info (# obs, doses, Monolix Version, etc) from summary.txt
#> ℹ done
#> ℹ reading covariance from FisherInformation/covarianceEstimatesSA.txt
#> ℹ done
#> ℹ imported monolix and translated to rxode2 compatible data ($monolixData)
#> ℹ imported monolix ETAS (_SAEM) imported to rxode2 compatible data ($etaData)
#> ℹ imported monolix pred/ipred data to compare ($predIpredData)
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> ℹ solving ipred problem
#> ℹ done
#> ℹ solving pred problem
#> ℹ done
rx
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> Tlag_pop ka_pop V_pop Cl_pop beta_V_tSex_F
#> -0.25949800 0.35610590 2.13606937 -2.00665359 -0.38227857
#> beta_Cl_tSex_F a b
#> -0.09383651 0.24818991 0.05086658
#>
#> Omega ($omega):
#> omega_Tlag omega_ka omega_V omega_Cl
#> omega_Tlag 0.3836648 0.0000000 0.00000000 0.00000000
#> omega_ka 0.0000000 0.9857194 0.00000000 0.00000000
#> omega_V 0.0000000 0.0000000 0.02782834 0.00000000
#> omega_Cl 0.0000000 0.0000000 0.00000000 0.08142194
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 central
#> ── μ-referencing ($muRefTable): ──
#> theta eta level covariates
#> 1 Tlag_pop omega_Tlag id
#> 2 ka_pop omega_ka id
#> 3 V_pop omega_V id (tSex == "F")*beta_V_tSex_F
#> 4 Cl_pop omega_Cl id (tSex == "F")*beta_Cl_tSex_F
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> description <- "The administration is extravascular with a first order absorption (rate constant ka) and a lag time (Tlag).\nThe PK model has one compartment (volume V) and a linear elimination (clearance Cl)."
#> dfObs <- 479
#> dfSub <- 32
#> thetaMat <- lotri({
#> Tlag_pop ~ c(Tlag_pop = 0.0695270111760315)
#> ka_pop ~ c(Tlag_pop = 0.00420093609313868, ka_pop = 0.195044017895198)
#> V_pop ~ c(Tlag_pop = -1.96027567180685e-05, ka_pop = -0.00923639238851991,
#> V_pop = 0.0894995626339569)
#> beta_V_tSex_F ~ c(Tlag_pop = 0.000672864307227817, ka_pop = 0.00146099695978716,
#> V_pop = -0.0105938787089785, beta_V_tSex_F = 0.00724034)
#> Cl_pop ~ c(Tlag_pop = 3.33517518294536e-05, ka_pop = -0.000198349948605509,
#> V_pop = 1.64480523051151e-05, beta_V_tSex_F = -2.52790128781238e-06,
#> Cl_pop = 5.67721912406063e-05)
#> beta_Cl_tSex_F ~ c(Tlag_pop = 4.97719167561125e-05, ka_pop = 0.00116960996255074,
#> V_pop = -0.000127036295934593, beta_V_tSex_F = -1.29385e-06,
#> Cl_pop = -0.000421700477175592, beta_Cl_tSex_F = 0.0199246)
#> omega_Tlag ~ c(Tlag_pop = -0.0670860109353223, ka_pop = 0.00570893614027221,
#> V_pop = 0.00299407684209903, beta_V_tSex_F = -0.00119581,
#> Cl_pop = 2.03533460988456e-06, beta_Cl_tSex_F = -0.00034514,
#> omega_Tlag = 0.116943)
#> omega_ka ~ c(Tlag_pop = -0.0133385617208073, ka_pop = 0.0172356179141179,
#> V_pop = 0.00171771988746332, beta_V_tSex_F = -0.000442385,
#> Cl_pop = 7.3966470999696e-05, beta_Cl_tSex_F = -0.000725679,
#> omega_Tlag = 0.0139898, omega_ka = 0.0694802)
#> omega_V ~ c(Tlag_pop = -0.000154212149163577, ka_pop = 0.000300664573505569,
#> V_pop = 4.67557030887738e-05, beta_V_tSex_F = -1.72411e-06,
#> Cl_pop = -2.66907442804329e-06, beta_Cl_tSex_F = 2.11319e-05,
#> omega_Tlag = 0.000296489, omega_ka = -0.000253742,
#> omega_V = 0.000656055)
#> omega_Cl ~ c(Tlag_pop = -0.00020876675474306, ka_pop = 0.000203991029463676,
#> V_pop = -4.25014903301396e-05, beta_V_tSex_F = 1.01086e-05,
#> Cl_pop = -4.27528356071897e-07, beta_Cl_tSex_F = -3.1184e-05,
#> omega_Tlag = 0.000190543, omega_ka = -0.000195656,
#> omega_V = 3.1147e-06, omega_Cl = 0.00133407)
#> a ~ c(Tlag_pop = 0.000459922525965396, ka_pop = -0.000402122537073151,
#> V_pop = -0.00035759769526248, beta_V_tSex_F = -5.67417e-07,
#> Cl_pop = 4.93306085861609e-06, beta_Cl_tSex_F = 5.18592e-05,
#> omega_Tlag = -0.000767474, omega_ka = -0.000402704,
#> omega_V = -3.56563e-05, omega_Cl = 5.77815e-05, a = 0.00146135)
#> b ~ c(Tlag_pop = -4.42123891106805e-05, ka_pop = 6.74134848256571e-05,
#> V_pop = 0.000102247569651447, beta_V_tSex_F = -4.84525e-06,
#> Cl_pop = -7.84308816660971e-07, beta_Cl_tSex_F = -4.64896e-06,
#> omega_Tlag = 5.67197e-05, omega_ka = 4.06959e-05,
#> omega_V = -4.39519e-06, omega_Cl = -1.31481e-05,
#> a = -0.000214637, b = 5.66332e-05)
#> })
#> validation <- c("ipred relative difference compared to Monolix ipred: 0.39%; 95% percentile: (0.02%,3.18%); rtol=0.00394",
#> "ipred absolute difference compared to Monolix ipred: 95% percentile: (8.49e-05, 0.166); atol=0.0175",
#> "pred relative difference compared to Monolix pred: 0%; 95% percentile: (0%,1.41%); rtol=1.25e-06",
#> "pred absolute difference compared to Monolix pred: 95% percentile: (1.87e-08, 5e-05); atol=6.09e-06",
#> "iwres relative difference compared to Monolix iwres: 0%; 95% percentile: (0.24%,204.01%); rtol=0.0794",
#> "iwres absolute difference compared to Monolix pred: 95% percentile: (0.0026, 0.33); atol=0.0362")
#> ini({
#> Tlag_pop <- -0.259498000083172
#> ka_pop <- 0.356105897545116
#> V_pop <- 2.1360693683596
#> Cl_pop <- -2.00665359474148
#> beta_V_tSex_F <- -0.382278567256413
#> beta_Cl_tSex_F <- -0.0938365059053137
#> a <- c(0, 0.24818990819019)
#> b <- c(0, 0.0508665778176092)
#> omega_Tlag ~ 0.383664766381593
#> omega_ka ~ 0.985719433448741
#> omega_V ~ 0.0278283386496708
#> omega_Cl ~ 0.0814219400052839
#> })
#> model({
#> cmt(depot)
#> cmt(central)
#> if (sex == 0) {
#> tSex <- "F"
#> }
#> else if (sex == 1) {
#> tSex <- "M"
#> }
#> else {
#> tSex <- "M"
#> }
#> Tlag <- exp(Tlag_pop + omega_Tlag)
#> ka <- exp(ka_pop + omega_ka)
#> V <- exp(V_pop + beta_V_tSex_F * (tSex == "F") + omega_V)
#> Cl <- exp(Cl_pop + beta_Cl_tSex_F * (tSex == "F") + omega_Cl)
#> d/dt(depot) <- -ka * depot
#> alag(depot) <- Tlag
#> d/dt(central) <- +ka * depot - Cl/V * central
#> Cc <- central/V
#> concentration <- Cc
#> concentration ~ add(a) + prop(b) + combined1()
#> })
#> }