[COVARIATE]
input = race

race = {type=categorical, categories={hispanic, caucasian, asian}}

[INDIVIDUAL]
input = {ka_pop, omega_ka, Q_pop, omega_Q, V1_pop, omega_V1, V2_pop, omega_V2, Cl_pop, omega_Cl, F_pop, omega_F, race, beta_F_race_caucasian, beta_F_race_asian}

race = {type=categorical, categories={hispanic, caucasian, asian}}

DEFINITION:
ka = {distribution=logNormal, typical=ka_pop, sd=omega_ka}
Q = {distribution=logNormal,  typical=Q_pop, sd=omega_Q}
V1 = {distribution=logNormal, typical=V1_pop, sd=omega_V1}
V2 = {distribution=logNormal, typical=V2_pop, sd=omega_V2}
Cl = {distribution=logNormal, typical=Cl_pop, sd=omega_Cl}
F = {distribution=logNormal,  typical=F_pop, covariate=race, coefficient={0, beta_F_race_caucasian, beta_F_race_asian}, sd=omega_F}

[LONGITUDINAL]

input = {a, b, ka, Cl, V1, Q, V2, F}

EQUATION:

; Parameter transformations 
V = V1 
k = Cl/V1 
k12 = Q/V1 
k21 = Q/V2

; PK model definition
Cc = pkmodel(ka, V, k, k12, k21, p=F)

OUTPUT:
output = Cc

DEFINITION:
y1 = {distribution=normal, prediction=Cc, errorModel=combined1(a, b)}