Translate a monolix file to rxode2
monolix2rx(
mlxtran,
update = TRUE,
thetaMatType = c("sa", "lin"),
sd = 1,
cor = 1e-05,
theta = 0.5,
ci = 0.95,
sigdig = 3,
envir = parent.frame()
)file name for mlxtran to translate to rxode2
is a boolean that represents if the final parameter estimates should be used for the translation (when present)
This lists the preferred source for thetaMat
covariance matrix. By default it is sa for simulated
annealing, though you could use lin for linearized covariance
calculation. If only one is present, then use whatever is present
Default standard deviation for between subject variability/inter-occasion variability that are missing.
Default correlation for missing correlations estimate
default population estimate
confidence interval for validation, by default 0.95
number of significant digits for validation, by default 3
represents the environment used for evaluating the corresponding rxode2 function
rxode2 model
# 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%,0%); rtol=1.21e-06",
#> "pred absolute difference compared to Monolix pred: 95% percentile: (1.87e-08, 4.92e-05); atol=6.07e-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()
#> })
#> }