<DATAFILE>

[FILEINFO]
file='data/parent_metabolite_data.csv'
delimiter = comma
header = {ID, TIME, OBS, OBSID, AMOUNT, CENS, LIMIT}

[CONTENT]
ID = {use=identifier}
TIME = {use=time}
OBS = {use=observation, name={y1_Cp, y2_Cm}, yname={'1_Cp', '2_Cm'}, type={continuous, continuous}}
OBSID = {use=observationtype}
AMOUNT = {use=amount}
CENS = {use=censored}
LIMIT = {use=limit}

<MODEL>

[INDIVIDUAL]
input = {Kpm_pop, omega_Kpm, V_pop, omega_V, k_pop, omega_k, k12_pop, omega_k12, k21_pop, omega_k21, ka_pop, omega_ka, km_pop, omega_km}

DEFINITION:
Kpm = {distribution=logNormal, typical=Kpm_pop, sd=omega_Kpm}
V = {distribution=logNormal, typical=V_pop, sd=omega_V}
k = {distribution=logNormal, typical=k_pop, sd=omega_k}
k12 = {distribution=logNormal, typical=k12_pop, sd=omega_k12}
k21 = {distribution=logNormal, typical=k21_pop, sd=omega_k21}
ka = {distribution=logNormal, typical=ka_pop, sd=omega_ka}
km = {distribution=logNormal, typical=km_pop, sd=omega_km}

[LONGITUDINAL]
input = {a1_Cp, b1_Cp, a2_Cm, b2_Cm}

file = 'oral1_noFPE_2cptP1cptM_uni_kaVkk12k21kmKpm.txt'

DEFINITION:
y1_Cp = {distribution=normal, prediction=Cp, errorModel=combined1(a1_Cp, b1_Cp)}
y2_Cm = {distribution=normal, prediction=Cm, errorModel=combined1(a2_Cm, b2_Cm)}

<FIT>
data = {y1_Cp, y2_Cm}
model = {y1_Cp, y2_Cm}

<PARAMETER>
Kpm_pop = {value=0.1, method=MLE}
V_pop = {value=9.664, method=MLE}
a1_Cp = {value=1, method=MLE}
a2_Cm = {value=1, method=MLE}
b1_Cp = {value=0.3, method=MLE}
b2_Cm = {value=0.3, method=MLE}
c1_Cp = {value=1, method=FIXED}
c2_Cm = {value=1, method=FIXED}
k12_pop = {value=0.0402, method=MLE}
k21_pop = {value=0.0437, method=MLE}
k_pop = {value=0.139, method=MLE}
ka_pop = {value=0.158, method=MLE}
km_pop = {value=0.0302, method=MLE}
omega_Kpm = {value=1, method=MLE}
omega_V = {value=1, method=MLE}
omega_k = {value=1, method=MLE}
omega_k12 = {value=1, method=MLE}
omega_k21 = {value=1, method=MLE}
omega_ka = {value=1, method=MLE}
omega_km = {value=1, method=MLE}

<MONOLIX>

[TASKS]
populationParameters()
individualParameters(method = {conditionalMean, conditionalMode })
fim(method = StochasticApproximation)
logLikelihood(method = ImportanceSampling)
plotResult(method = {indfits, parameterdistribution, covariancemodeldiagnosis, covariatemodeldiagnosis, obspred, vpc, residualsscatter, residualsdistribution, randomeffects, saemresults })

[SETTINGS]
GLOBAL:
exportpath = 'pm'
