### Example: reading a .grd file with nmgrd Source: https://nlmixr2.github.io/nonmem2rx/reference/nmgrd.html Demonstrates reading a .grd file using nmgrd and shows the resulting gradient output. ```r nmgrd(system.file("mods/cpt/runODE032.grd", package="nonmem2rx")) #> ITERATION GRD(1) GRD(2) GRD(3) GRD(4) GRD(5) #> 25.0000000 560.0530000 1694.0100000 13.0952000 -115.0000000 -0.5077900 #> GRD(6) GRD(7) GRD(8) GRD(9) #> -0.2418040 0.0765655 -0.0282282 -0.1172670 ``` -------------------------------- ### Example: Read run001.ext and display output Source: https://nlmixr2.github.io/nonmem2rx/reference/nmext.html Demonstrates calling nmext on a sample .ext file included in the nonmem2rx package. The output shows the returned list with $theta, $omega, $sigma, and $objf components. ```r nmext(system.file("run001.ext", package="nonmem2rx")) #> $theta #> theta1 theta2 theta3 theta4 theta5 theta6 #> 26.29090000 1.34809000 4.20364000 0.20795800 0.20461000 0.01055270 #> theta7 #> 0.00717161 #> #> $omega #> eta1 eta2 eta3 #> eta1 0.0729525 0.0000000 0.00000 #> eta2 0.0000000 0.0380192 0.00000 #> eta3 0.0000000 0.0000000 1.90699 #> #> $sigma #> eps1 #> eps1 1 #> #> $objf #> [1] -1403.905 #> ``` -------------------------------- ### Example: Read runODE032.xml and display results Source: https://nlmixr2.github.io/nonmem2rx/reference/nmxml.html Calls nmxml on a sample NONMEM XML file included with the nonmem2rx package. The output shows the theta, omega, sigma, and covariance matrices extracted from the XML. ```R nmxml(system.file("mods/cpt/runODE032.xml", package="nonmem2rx")) #> $theta #> theta1 theta2 theta3 theta4 theta5 #> 1.3703404 4.1981491 1.3800349 3.8765734 0.1964461 #> #> $omega #> eta1 eta2 eta3 eta4 #> eta1 0.1012514 0.00000000 0.0000000 0.00000000 #> eta2 0.0000000 0.09938724 0.0000000 0.00000000 #> eta3 0.0000000 0.00000000 0.1013027 0.00000000 #> eta4 0.0000000 0.00000000 0.0000000 0.07304975 #> #> $sigma #> eps1 #> eps1 1 #> #> $cov #> theta1 theta2 theta3 theta4 theta5 #> theta1 8.876810e-04 -1.055098e-04 1.844162e-04 -1.202337e-04 5.278300e-08 #> theta2 -1.055098e-04 8.714095e-04 -1.061946e-04 -5.066632e-05 -1.565618e-05 #> theta3 1.844162e-04 -1.061946e-04 2.993363e-03 1.652516e-04 5.993313e-06 #> theta4 -1.202337e-04 -5.066632e-05 1.652516e-04 1.213465e-03 -2.539912e-05 #> theta5 5.278300e-08 -1.565618e-05 5.993313e-06 -2.539912e-05 9.942182e-06 #> eta1 -4.712728e-05 4.696667e-05 -3.642709e-05 2.547962e-05 -8.168847e-06 #> omega.1.2 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> omega.1.3 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> omega.1.4 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> eta2 -7.371560e-05 2.566338e-05 -8.083493e-05 1.369999e-05 -4.365635e-06 #> omega.2.3 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> omega.2.4 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> eta3 6.633832e-05 -8.190016e-05 5.489848e-04 1.683555e-04 1.591222e-06 #> omega.3.4 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> eta4 -9.496613e-06 1.101079e-04 -3.065372e-04 -9.128974e-05 3.187703e-06 #> eps1 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> eta1 omega.1.2 omega.1.3 omega.1.4 eta2 omega.2.3 #> theta1 -4.712728e-05 0 0 0 -7.371560e-05 0 #> theta2 4.696667e-05 0 0 0 2.566338e-05 0 #> theta3 -3.642709e-05 0 0 0 -8.083493e-05 0 #> theta4 2.547962e-05 0 0 0 1.369999e-05 0 #> theta5 -8.168847e-06 0 0 0 -4.365635e-06 0 #> eta1 1.692964e-04 0 0 0 8.751806e-06 0 #> omega.1.2 0.000000e+00 0 0 0 0.000000e+00 0 #> omega.1.3 0.000000e+00 0 0 0 0.000000e+00 0 #> omega.1.4 0.000000e+00 0 0 0 0.000000e+00 0 #> eta2 8.751806e-06 0 0 0 1.512503e-04 0 #> omega.2.3 0.000000e+00 0 0 0 0.000000e+00 0 #> omega.2.4 0.000000e+00 0 0 0 0.000000e+00 0 #> eta3 3.487139e-05 0 0 0 4.315929e-07 0 #> omega.3.4 0.000000e+00 0 0 0 0.000000e+00 0 #> eta4 1.366281e-05 0 0 0 -1.950959e-05 0 #> eps1 0.000000e+00 0 0 0 0.000000e+00 0 #> omega.2.4 eta3 omega.3.4 eta4 eps1 #> theta1 0 6.633832e-05 0 -9.496613e-06 0 #> theta2 0 -8.190016e-05 0 1.101079e-04 0 #> theta3 0 5.489848e-04 0 -3.065372e-04 0 #> theta4 0 1.683555e-04 0 -9.128974e-05 0 #> theta5 0 1.591222e-06 0 3.187703e-06 0 #> eta1 0 3.487139e-05 0 1.366281e-05 0 #> omega.1.2 0 0.000000e+00 0 0.000000e+00 0 #> omega.1.3 0 0.000000e+00 0 0.000000e+00 0 #> omega.1.4 0 0.000000e+00 0 0.000000e+00 0 #> eta2 0 4.315929e-07 0 -1.950959e-05 0 #> omega.2.3 0 0.000000e+00 0 0.000000e+00 0 #> omega.2.4 0 0.000000e+00 0 0.000000e+00 0 #> eta3 0 5.489848e-04 0 -3.065372e-04 0 #> omega.3.4 0 0.000000e+00 0 0.000000e+00 0 #> eta4 0 1.366281e-05 0 -1.950959e-05 0 #> eps1 0 0.000000e+00 0 0.000000e+00 0 ``` -------------------------------- ### Example: nmlst on built-in HCQ1CMT.lst Source: https://nlmixr2.github.io/nonmem2rx/reference/nmlst.html Calls nmlst on a built-in NONMEM .lst file and shows the returned list with theta, omega, sigma, cov, objf, nobs, nsub, nmtran, termInfo, nonmem, time, tere, and control stream contents. ```r nmlst(system.file("mods/DDMODEL00000322/HCQ1CMT.lst", package="nonmem2rx")) #> $theta #> theta1 theta2 theta3 theta4 theta5 #> 15.700 861.000 9.300 0.746 1.380 #> #> $omega #> eta1 eta2 #> eta1 0.149 0.000 #> eta2 0.000 0.272 #> #> $sigma #> eps1 #> eps1 0.0286 #> #> $cov #> NULL #> #> $objf #> [1] 865.901 #> #> $nobs #> [1] 76 #> #> $nsub #> [1] 48 #> #> $nmtran #> [1] "WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1\n\n(WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION." #> #> $termInfo #> [1] "0MINIMIZATION SUCCESSFUL\nNO. OF FUNCTION EVALUATIONS USED: 120\nNO. OF SIG. DIGITS IN FINAL EST.: 3.1" #> #> $nonmem #> [1] "7.3.0" #> #> $time #> [1] 68.17 #> #> $tere #> [1] "Elapsed estimation time in seconds: 26.44\n0R MATRIX ALGORITHMICALLY SINGULAR\nAND ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE\n0R MATRIX IS OUTPUT\n0COVARIANCE STEP ABORTED\nElapsed covariance time in seconds: 41.73" #> #> $control #> [1] ";; Description:" #> [2] ";; Author: user" #> [3] "$PROBLEM HCQ 1CMT ORAL PK MODEL (BASED ON CARMICHAEL ET AL (2003))" #> [4] "; ------------dataset------------" #> [5] "$INPUT ID SEX WT AGE TIME AMT ADDL II CMT DV MDV EVID" #> [6] "$DATA HCQdata5.csv IGNORE=@" #> [7] "" #> [8] "; ------------model------------" #> [9] "$SUBROUTINE ADVAN2 TRANS2" #> [10] "$PK" #> [11] "" #> [12] "TVCL=THETA(1)*((WT/80)**THETA(5));*((AGE/57)**THETA(5));" #> [13] "TVV=THETA(2)" #> [14] "TVKA=THETA(3)" #> [15] "TVF1=THETA(4)" #> [16] "; TVALAG1=THETA(4)" #> [17] "" #> [18] "" #> [19] "CL=TVCL*EXP(ETA(1))" #> [20] "V=TVV*EXP(ETA(2))" #> [21] "KA=TVKA;*EXP(ETA(3))" #> [22] "F1=TVF1;*EXP(ETA(4))" #> [23] "; ALAG1=TVALAG1;*EXP(ETA(3))" #> [24] "" #> [25] "" #> [26] "; scaling factor" #> [27] "KE=CL/V" #> [28] "S2=V/1000 ; dose [mg] and conc. [ng/mL]" #> [29] "" #> [30] "" #> [31] "$ERROR" #> [32] "Y=F*(1+EPS(1));+EPS(2)" #> [33] "W=F" #> [34] "" #> [35] "IPRED=F ; prediction individuelle" #> [36] "IRES=DV-IPRED ; (individual-specific residual)" #> [37] "IWRES=IRES/W ; (individual-specific weighted residual)" #> [38] "" ``` -------------------------------- ### Install nonmem2rx from r-universe Source: https://nlmixr2.github.io/nonmem2rx/ Installs the development version of nonmem2rx from the nlmixr2 r-universe repository. ```R install.packages('nonmem2rx', repos = c('https://nlmixr2.r-universe.dev', 'https://cloud.r-project.org')) ``` -------------------------------- ### Import NONMEM model with nonmem2rx Source: https://nlmixr2.github.io/nonmem2rx/articles/simulate-new-dosing.html Loads the nonmem2rx and rxode2 libraries, then imports a NONMEM control stream using the nonmem2rx() function. The example uses a built-in control stream file and specifies options to skip saving and error determination. The output shows the parsing and processing steps. ```R library(nonmem2rx) library(rxode2) library(nonmem2rx) # First we need the location of the nonmem control stream Since we are running an example, we will use one of the built-in examples in `nonmem2rx` ctlFile <- system.file("mods/cpt/runODE032.ctl", package="nonmem2rx") # You can use a control stream or other file. With the development # version of `babelmixr2`, you can simply point to the listing file mod <- nonmem2rx(ctlFile, lst=".res", save=FALSE, determineError=FALSE) ``` -------------------------------- ### Import NONMEM model into nonmem2rx Source: https://nlmixr2.github.io/nonmem2rx/articles/create-office.html Loads required libraries (nonmem2rx, babelmixr2, nlmixr2rpt, onbrand) and imports a NONMEM control stream using nonmem2rx(). The example uses a built-in control stream and saves the model without saving to disk. The output shows the parsing and processing steps. ```r library(nonmem2rx) library(babelmixr2) library(nlmixr2rpt) library(onbrand) library(nonmem2rx) # First we need the location of the nonmem control stream Since we are running an example, we will use one of the built-in examples in `nonmem2rx` ctlFile <- system.file("mods/cpt/runODE032.ctl", package="nonmem2rx") # You can use a control stream or other file. With the development # version of `babelmixr2`, you can simply point to the listing file mod <- nonmem2rx(ctlFile, lst=".res", save=FALSE) #> ℹ getting information from '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ reading in grd file #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $gTHETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@) #> ℹ applying names specified by $INPUT #> ℹ ignore filter code: .data$SD == 0 #> ℹ renaming 'ytype' to 'nmytype' #> ℹ done #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done ``` -------------------------------- ### Get covariance matrix with getVarCov(fit) Source: https://nlmixr2.github.io/nonmem2rx/articles/read-rounding.html Call getVarCov(fit) on an imported nlmixr2 fit object to compute and display the variance/covariance matrix. The output includes progress messages and a warning that 'r,s' not usable (sandwich doubtful); installed 'r'. The resulting matrix shows covariances for parameters like tktr, tka, tcl, tv, and error terms. ```r getVarCov(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> → finding duplicate expressions in EBE model... #> → compiling inner model... #> ✔ done #> → finding duplicate expressions in FD model... #> → compiling EBE model... #> ✔ done #> → compiling events FD model... #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:03:36 #> Warning: "r,s" not usable (sandwich doubtful); installed "r" #> Updated original fit object fit #> tktr tka tcl tv #> tktr 1.105361e-01 1.105580e-01 6.595295e-03 -1.378152e-02 #> tka 1.105580e-01 1.105818e-01 6.575920e-03 -1.378782e-02 #> tcl 6.595295e-03 6.575920e-03 3.355852e-03 -1.378506e-04 #> tv -1.378152e-02 -1.378782e-02 -1.378506e-04 3.395864e-03 #> prop.err 6.991459e-03 6.989051e-03 6.693899e-04 -9.794308e-04 #> pkadd.err -6.243180e-02 -6.248646e-02 -4.065125e-03 6.897479e-03 #> temax 3.791718e-02 3.794539e-02 -2.298042e-03 -8.276277e-03 #> tec50 -2.722903e-03 -2.651891e-03 -1.660533e-03 -6.385158e-04 #> tkout -2.880398e-03 -2.866592e-03 -1.843761e-04 2.160867e-04 #> te0 -5.673878e-04 -5.638154e-04 -7.962581e-05 1.302761e-04 #> pdadd.err 1.056755e-02 1.072430e-02 2.562323e-04 -2.446883e-04 #> om.eta.ktr -3.996192e-02 -4.003662e-02 -1.336302e-03 5.159895e-03 #> om.eta.ka -4.006710e-02 -4.013187e-02 -1.471516e-03 5.166575e-03 #> om.eta.cl 3.293154e-04 3.334785e-04 4.537361e-04 -1.678327e-04 #> om.eta.v -7.498831e-04 -7.493551e-04 -4.971795e-05 1.276597e-04 #> om.eta.emax 8.400322e-06 8.365553e-06 -1.093724e-07 -5.069930e-07 #> om.eta.ec50 3.753543e-03 3.765399e-03 3.341576e-04 -2.387801e-05 #> om.eta.kout 8.708897e-05 9.123636e-05 -6.744324e-06 -3.253798e-05 #> om.eta.e0 2.126127e-05 2.101492e-05 9.906537e-06 -1.797064e-05 #> prop.err pkadd.err temax tec50 #> tktr 6.991459e-03 -6.243180e-02 3.791718e-02 -2.722903e-03 #> tka 6.989051e-03 -6.248646e-02 3.794539e-02 -2.651891e-03 #> tcl 6.693899e-04 -4.065125e-03 -2.298042e-03 -1.660533e-03 #> tv -9.794308e-04 6.897479e-03 -8.276277e-03 -6.385158e-04 #> prop.err 6.138698e-04 -4.578322e-03 3.914700e-04 -2.845100e-04 #> pkadd.err -4.578322e-03 5.179030e-02 -1.128141e-02 1.014706e-03 #> temax 3.914700e-04 -1.128141e-02 2.140027e-01 9.125507e-03 #> tec50 -2.845100e-04 1.014706e-03 9.125507e-03 6.981158e-03 #> tkout -1.375440e-04 1.343440e-03 -2.291550e-03 1.077203e-03 #> te0 -7.538461e-05 -1.460555e-04 -9.611563e-04 -4.319198e-05 #> pdadd.err 7.240194e-04 -1.425947e-02 -1.143512e-02 1.593869e-02 #> om.eta.ktr -1.946047e-03 1.175427e-02 1.276037e-02 1.221867e-03 #> om.eta.ka -1.972896e-03 1.154218e-02 1.288658e-02 1.630999e-03 #> om.eta.cl -4.303421e-06 -5.968533e-04 -1.304120e-04 9.732639e-05 #> om.eta.v -7.318444e-05 8.318787e-04 -1.367561e-03 3.990646e-05 #> om.eta.emax -8.218347e-08 2.408679e-06 -7.106387e-06 8.465079e-07 #> om.eta.ec50 3.383662e-04 -4.249174e-03 -6.676435e-03 -9.958206e-04 ``` -------------------------------- ### Install nonmem2rx from CRAN Source: https://nlmixr2.github.io/nonmem2rx/ Installs the CRAN version of nonmem2rx once it is available on CRAN. ```R install.packages('nonmem2rx') ``` -------------------------------- ### Explore comparison datasets with head() Source: https://nlmixr2.github.io/nonmem2rx/articles/rxode2-validate.html Shows how to inspect the first rows of the iwresCompare, ipredCompare, and predCompare datasets. These datasets contain the NONMEM and rxode2 predictions side by side, allowing further exploration of differences. Note that comparisons are observation-only and dosing predictions are excluded. ```R head(mod$iwresCompare) #> ID TIME nonmemIWRES IWRES #> 1 1 0.25 -0.73154 -0.7315464 #> 2 1 0.50 1.86670 1.8666563 #> 3 1 0.75 -1.26860 -1.2685789 #> 4 1 1.00 0.44442 0.4444172 #> 5 1 1.50 0.55470 0.5546978 #> 6 1 2.00 0.35351 0.3535035 ``` ```R head(mod$ipredCompare) #> ID TIME nonmemIPRED IPRED #> 1 1 0.25 1215.4 1215.358 #> 2 1 0.50 1191.9 1191.924 #> 3 1 0.75 1169.2 1169.164 #> 4 1 1.00 1147.1 1147.057 #> 5 1 1.50 1104.7 1104.721 #> 6 1 2.00 1064.8 1064.759 ``` ```R head(mod$predCompare) #> ID TIME nonmemPRED PRED #> 1 1 0.25 1750.3 1750.290 #> 2 1 0.50 1699.8 1699.834 #> 3 1 0.75 1651.3 1651.349 #> 4 1 1.00 1604.8 1604.752 #> 5 1 1.50 1516.9 1516.913 #> 6 1 2.00 1435.7 1435.723 ``` -------------------------------- ### nminfo() Source: https://nlmixr2.github.io/nonmem2rx/reference/index.html Gets the most accurate information available from NONMEM. ```APIDOC ## nminfo() ### Description Get the most accurate information you can get from NONMEM. ### Usage `nminfo()` ### Arguments None documented on this page. ### Value An object containing NONMEM information. ### Details This function aggregates information from various NONMEM output files to provide the most accurate parameter estimates and diagnostics. ``` -------------------------------- ### Import NONMEM control stream with custom names using nonmem2rx() Source: https://nlmixr2.github.io/nonmem2rx/articles/import-nonmem.html Imports a NONMEM control stream with custom theta, eta, and compartment names. The example sets the output extension to '.res', disables saving, and uses thetaNames, etaNames, and cmtNames arguments. The output shows the resulting rxode2 model object with renamed parameters and compartments. ```R mod <- nonmem2rx(system.file("mods/cpt/runODE032.ctl", package="nonmem2rx"), lst=".res", save=FALSE, thetaNames=c("lcl", "lvc", "lq", "lvp", "prop.sd"), etaNames=c("eta.cl", "eta.vc", "eta.q","eta.vp"), cmtNames = c("central", "perip")) ``` ```R mod ``` -------------------------------- ### NONMEM control stream example for nmlst Source: https://nlmixr2.github.io/nonmem2rx/reference/nmlst.html Example NONMEM control stream showing the $PK block with covariate models, the $ERROR block with residual error definitions, and $THETA initial estimates. The output is shown as R console output with line numbers and #> prefixes. ```nonmem #> [23] "\tTVV2=THETA(6)*((WT/75)**THETA(9))" #> [24] "\tTVQ3=THETA(7) ; PERIPHERAL" #> [25] "\tTVV3=THETA(8)" #> [26] "" #> [27] "\t; interindividual variance model" #> [28] "\tCL=TVCL*EXP(ETA(1))" #> [29] "\tV1=TVV1*EXP(ETA(2))" #> [30] "\tQ2=TVQ2" #> [31] "\tV2=TVV2*EXP(ETA(3))" #> [32] "\tQ3=TVQ3*EXP(ETA(4))" #> [33] "\tV3=TVV3 " #> [34] "" #> [35] "\t; scaling factor" #> [36] "\tS1=V1/1000 ; dose [g] and conc. [mg/L]" #> [37] "\tS2=V2/THETA(10)" #> [38] "" #> [39] "" #> [40] "" #> [41] "" #> [42] "$ERROR " #> [43] "; calcultate de result (i.e. model prediction) " #> [44] "\tH1=0" #> [45] "\tH2=0" #> [46] "\tIF(CMT.EQ.1) H1=1" #> [47] "\tIF(CMT.EQ.2) H2=1" #> [48] "\tY=H1*(F*(1+EPS(1))+EPS(2))+H2*(F*(1+EPS(3))) ; +EPS(4)" #> [49] "\tW=F" #> [50] "\t" #> [51] "\tIPRED=F ; prediction individuelle " #> [52] "\tIRES=DV-IPRED ; (individual-specific residual) " #> [53] "\tIWRES=IRES/W ; (individual-specific weighted residual)" #> [54] "\t" #> [55] "" #> [56] "" #> [57] "; Initial estimates" #> [58] "$THETA (0,9.81355436018442) ; CL" #> [59] "$THETA 0.653109400662184 ; GFRCL" #> [60] "$THETA (0,4.53480721643241) ; V1" #> [61] "$THETA 1.12475365584077 ; WTV1" ```