mlxtran.md
In monolix2rx: Converts 'Monolix' Models to 'rxode2'

mlxtran initial list

Code
  print(v)
Output
  DESCRIPTION:
  model translated from `babelmixr2` and `nlmixr2` function pk.turnover.emax3 to pk.turnover.emax3-monolix.txt

  <DATAFILE>
  [FILEINFO]
  ; parsed: $DATAFILE$FILEINFO$FILEINFO
  file = 'pk.turnover.emax3-monolix.csv'
  delimiter = comma
  header = {ID, TIME, EVID, AMT, DV, ADM, YTYPE, nlmixrRowNums}

  [CONTENT]
  ; parsed: $DATAFILE$CONTENT$CONTENT
  ID = {use=identifier}
  TIME = {use=time}
  EVID = {use=eventidentifier}
  AMT = {use=amount}
  YTYPE = {use=observationtype}
  ADM = {use=administration}
  DV = {use=observation, name={y1, y2}, yname={'1', '2'}, type={continuous, continuous}}

  <MODEL>
  [INDIVIDUAL]
  ; parsed: $MODEL$INDIVIDUAL$INDIVIDUAL
  input = {ktr_pop, omega_ktr, ka_pop, omega_ka, cl_pop, omega_cl, v_pop, omega_v, emax_pop, omega_emax, ec50_pop, omega_ec50, kout_pop, omega_kout, e0_pop, omega_e0}

  DEFINITION:
  ; parsed: $MODEL$INDIVIDUAL$DEFINITION
  ktr = {distribution=lognormal, typical=ktr_pop, sd=omega_ktr}
  ka = {distribution=lognormal, typical=ka_pop, sd=omega_ka}
  cl = {distribution=lognormal, typical=cl_pop, sd=omega_cl}
  v = {distribution=lognormal, typical=v_pop, sd=omega_v}
  emax = {distribution=logitnormal, typical=emax_pop, sd=omega_emax, min=0, max=1}
  ec50 = {distribution=lognormal, typical=ec50_pop, sd=omega_ec50}
  kout = {distribution=lognormal, typical=kout_pop, sd=omega_kout}
  e0 = {distribution=lognormal, typical=e0_pop, sd=omega_e0}

  [LONGITUDINAL]
  ; parsed: $MODEL$LONGITUDINAL$LONGITUDINAL
  input = {pkadd__err, prop__err, pdadd__err, ktr, ka, cl, v, emax, ec50, kout, e0}

  DEFINITION:
  ; parsed: $MODEL$LONGITUDINAL$DEFINITION
  rx_prd_cp = {distribution=normal, prediction=rx_pred_cp, errorModel=combined2(pkadd__err, prop__err)}
  rx_prd_effect = {distribution=normal, prediction=rx_pred_effect, errorModel=constant(pdadd__err)}

  PK:
  ; parsed: $MODEL$LONGITUDINAL$PK
  compartment(cmt = 1, amount = depot, volume = 1.0)

  depot(adm = 1, target = depot, Tlag = 0, p = 1)

  EQUATION:
  ; parsed: $MODEL$LONGITUDINAL$EQUATION
  DCP = center/v
  PD = 1-emax*DCP/(ec50+DCP)
  effect_0 = e0
  kin = e0*kout
  ddt_depot = - ktr*depot
  ddt_gut = ktr*depot-ka*gut
  ddt_center = ka*gut-cl/v*center
  ddt_effect = kin*PD-kout*effect
  cp = center/v
  rx_pred_cp = cp
  rx_pred_effect = effect

  OUTPUT:
  ; parsed: $MODEL$LONGITUDINAL$OUTPUT
  output = {rx_pred_cp, rx_pred_effect}

  <FIT>
  ; parsed: $FIT$FIT
  data = {y1, y2}
  model = {rx_prd_cp, rx_prd_effect}

  <PARAMETER>
  ; parsed: $PARAMETER$PARAMETER
  ktr_pop = {value=1, method=MLE}
  ka_pop = {value=1, method=MLE}
  cl_pop = {value=0.1, method=MLE}
  v_pop = {value=10, method=MLE}
  prop__err = {value=0.1, method=MLE}
  pkadd__err = {value=0.1, method=MLE}
  emax_pop = {value=0.8, method=MLE}
  ec50_pop = {value=0.5, method=MLE}
  kout_pop = {value=0.05, method=MLE}
  e0_pop = {value=100, method=MLE}
  pdadd__err = {value=10, method=MLE}
  omega_ktr = {value=1, method=MLE}
  omega_ka = {value=1, method=MLE}
  omega_cl = {value=1.4142135623731, method=MLE}
  omega_v = {value=1, method=MLE}
  omega_emax = {value=0.707106781186548, method=MLE}
  omega_ec50 = {value=0.707106781186548, method=MLE}
  omega_kout = {value=0.707106781186548, method=MLE}
  omega_e0 = {value=0.707106781186548, method=MLE}

  <MONOLIX>
  [TASKS]
  ; parsed: $MONOLIX$TASKS$TASKS
  populationParameters()
  individualParameters(method = {conditionalMode})
  fim(method = Linearization)
  logLikelihood(method = Linearization)
  plotResult(method = {outputplot, indfits, obspred, residualsscatter, residualsdistribution, parameterdistribution, covariatemodeldiagnosis, randomeffects, covariancemodeldiagnosis, saemresults})

  [SETTINGS]
  GLOBAL:
  ; parsed: $MONOLIX$SETTINGS$GLOBAL
  exportpath = 'pk.turnover.emax3-monolix'

  POPULATION:
  ; parsed: $MONOLIX$SETTINGS$POPULATION
  exploratoryautostop = no
  smoothingautostop = no
  burniniterations = 5
  exploratoryiterations = 250
  simulatedannealingiterations = 250
  smoothingiterations = 200
  exploratoryalpha = 0
  exploratoryinterval = 200
  omegatau = 0.95
  errormodeltau = 0.95