; MONOLIX project generated with IQRtools

; ==PROJECT HEADER START===================================================
; COMMENT             = 'First covariate model'
; TOOL                = 'MONOLIX'
; TOOLVERSION         = 'MLX2023R1'
; DATA                = '../../Ex8/data.csv'
; DOSINGTYPES         = 'BOLUS'
; TK0NAMES            = 'NA'
; COVNAMES            = 'WT0,AGE0,BMI0,HT0'
; CATNAMES            = 'SEX'
; CATCATEGORIES       = '[1 2]'
; REGRESSIONNAMES     = ''
; OUTPUTS             = 'Cc'
; ERRORMODELS         = 'absrel'
; PARAMNAMES          = 'ka,CL,Vc,Q1,Vp1'
; PARAMTRANS          = 'exp(phi),exp(phi),exp(phi),exp(phi),exp(phi)'
; PARAMINVTRANS       = 'log(psi),log(psi),log(psi),log(psi),log(psi)'
; COVARIATENAMES      = 'WT0,AGE0,BMI0,HT0,SEX'
; COVARIATESUSED      = 'SEX,WT0,AGE0'
; BETACOVNAMES        = 'beta_CL(WT0),beta_CL(AGE0),beta_Vc(AGE0)'
; BETACOVTRANS        = 'log(cov/77),log(cov/31.5),log(cov/31.5)'
; BETACATNAMES        = 'beta_ka(SEX)'
; BETACATREFERENCE    = '1'
; NROBSERVATIONS      = '1266'
; ==PROJECT HEADER END=====================================================

; =============================================
<DATAFILE>
; =============================================

[FILEINFO]
file = '../../Ex8/data.csv'
delimiter = comma
header = {IXGDF, USUBJID, ID, TIME, TIMEPOS, TAD, TIMEUNIT, YTYPE, NAME, DV, UNIT, CENS, MDV, EVID, AMT, ADM, TINF, RATE, DOSE, WT0, AGE0, BMI0, HT0, SEX}

[CONTENT]
ID = {use=identifier}
TIME = {use=time}
YTYPE = {use=observationtype}
DV = {use=observation, name={y}, ytype={1}, type={continuous}}
CENS = {use=censored}
MDV = {use=missingdependentvariable}
EVID = {use=eventidentifier}
AMT = {use=amount}
ADM = {use=administration}
TINF = {use=infusiontime}
WT0 = {use=covariate, type=continuous}
AGE0 = {use=covariate, type=continuous}
BMI0 = {use=covariate, type=continuous}
HT0 = {use=covariate, type=continuous}
SEX = {use=covariate, type=categorical}

; =============================================
<MODEL>
; =============================================

[COVARIATE]
input = {WT0,AGE0,BMI0,HT0,SEX}

SEX = {type=categorical, categories={1,2}}

EQUATION:
tWT0 = log(WT0/77)
tAGE0 = log(AGE0/31.5)
tBMI0 = log(BMI0/24.65)
tHT0 = log(HT0/176.5)

DEFINITION:
tSEX =
{
  transform = SEX,
  categories = {
    1 = 1,
    2 = 2
  },
  reference = 1
}

[INDIVIDUAL]
input = {ka_pop, CL_pop, Vc_pop, Q1_pop, Vp1_pop, omega_ka, omega_CL, omega_Vc, omega_Q1, omega_Vp1, beta_ka_tSEX_2, beta_CL_tWT0, beta_CL_tAGE0, beta_Vc_tAGE0, tSEX, tWT0, tAGE0}
tSEX = {type=categorical, categories={1,2}}

DEFINITION:
ka = {distribution=logNormal, typical=ka_pop, sd=omega_ka, covariate={tSEX}, coefficient={{0, beta_ka_tSEX_2}}}
CL = {distribution=logNormal, typical=CL_pop, sd=omega_CL, covariate={tWT0,tAGE0}, coefficient={beta_CL_tWT0, beta_CL_tAGE0}}
Vc = {distribution=logNormal, typical=Vc_pop, sd=omega_Vc, covariate={tAGE0}, coefficient={beta_Vc_tAGE0}}
Q1 = {distribution=logNormal, typical=Q1_pop, sd=omega_Q1}
Vp1 = {distribution=logNormal, typical=Vp1_pop, sd=omega_Vp1}

[LONGITUDINAL]
input = {a, b}

file = './project_model.txt'

DEFINITION:
y = {distribution=normal, prediction=Cc ,errorModel=combined1(a, b)}

; =============================================
<FIT>
; =============================================
data = {y}
model = {y}

; =============================================
<PARAMETER>
; =============================================
ka_pop = {value=0.2, method=MLE}
CL_pop = {value=20, method=MLE}
Vc_pop = {value=20, method=MLE}
Q1_pop = {value=20, method=MLE}
Vp1_pop = {value=2000, method=MLE}
omega_ka = {value=0.5, method=MLE}
omega_CL = {value=0.5, method=MLE}
omega_Vc = {value=0.5, method=MLE}
omega_Q1 = {value=0.5, method=MLE}
omega_Vp1 = {value=0.1, method=MLE}
beta_ka_tSEX_2 = {value=0.1, method=MLE}
beta_CL_tWT0 = {value=0.1, method=MLE}
beta_CL_tAGE0 = {value=0.1, method=MLE}
beta_Vc_tAGE0 = {value=0.1, method=MLE}
a = {value=1, method=MLE}
b = {value=0.3, method=MLE}

; =============================================
<MONOLIX>
; =============================================

[TASKS]
populationParameters()
individualParameters(method = conditionalMode)
fim(method = Linearization)
logLikelihood(method = Linearization)
plotResult()

[SETTINGS]
GLOBAL:
seed = 123456
exportpath = 'RESULTSORIG'
nbchains = 1
autochains = no

POPULATION:
exploratoryautostop = no
smoothingautostop = no
smoothingiterations = 20
exploratoryiterations = 50
variability = FirstStage