inst/models/uiModels.R
In nlmixrdevelopment/nlmixr.examples: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics
options(RxODE.cache.directory="~/.rxCache")
## runModel <- "U003"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U014"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U025"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U034"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U048"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U062"; runEst <- "focei"; source("uiModels.R")
## runEst <- "saem"; source("uiModels.R")
## Use runModel to select one model to run. ie
## runModel <- "U014" and then source the file
## Use runEst to select one estimation type ie
## runEst <- "focei"
library(nlmixr)
library(testthat)
source("helper-prep_fit.R")
one.compartment.IV.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1 #IIV V
eta.Cl ~ 0.1 #IIV Cl
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
# RxODE-style differential equations are supported
d / dt(centr) = -(Cl / Vc) * centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.IV.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1 #IIV V
eta.Cl ~ 0.1 #IIV Cl
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
linCmt() ~ prop(prop.err)
})
}
one.compartment.IV.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
# RxODE-style differential equations are supported
d/dt(centr) = -(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.IV.MM.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 6.9 #log Vmax (mg/hr)
lKM <- 5.8 #log KM (mg/L)
lVc <- 4.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.2, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1
eta.VM ~ 0.14
eta.KM ~ 0.1
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
# RxODE-style differential equations are supported
d/dt(centr) = -(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.8 #log Cl (L/hr)
lVc <- 4.7 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(Cl/Vc)*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.8 #log Cl (L/hr)
lVc <- 4.7 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
linCmt() ~ prop(prop.err)
})
}
one.compartment.oral.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(Cl/Vc)*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model2.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
one.compartment.oral.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
# RxODE-style differential equations are supported
K10<- Cl/Vc
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-K10*centr;
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
two.compartment.IV.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7.1 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.3 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.15
eta.KM ~ 0.1
eta.Vc ~ 0.15
eta.Q ~ 0.15
eta.Vp ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.MM.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.Vc ~ 0.15
eta.Q ~ 0.15
eta.Vp ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.MM.model3 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.1
eta.KM ~ 0.1
eta.Vc ~ 0.1
eta.Q ~ 0.1
eta.Vp ~ 0.1
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.oral.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
K10<- Cl/Vc
K12<- Q/Vc
K21<- Q/Vp
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot+K21*periph-K12*centr-K10*centr;
d/dt(periph) = -K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.oral.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
KA <- exp(lKA + eta.KA)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
two.compartment.oral.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 7.1 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4.1 #log Vp (L)
lKA <- 0.22 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Vp ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot+K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) = -K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
require(dplyr);
nmModels <- (ls(pattern="[.]compartment[.]"))
getModel <- function(cmt=1,oral=FALSE,mm=FALSE,extra="",solve=FALSE){
m <- nmModels %>%
stringr::str_subset(ifelse(cmt==1,"one","two")) %>%
stringr::str_subset(ifelse(oral,"oral","IV"))
if (mm){
m <- m %>% stringr::str_subset("MM")
} else {
m <- m[!stringr::str_detect(m,"MM")];
}
if (extra==""){
m <- m[!stringr::str_detect(m,"[23]")];
} else {
m <- m %>% stringr::str_subset(extra);
}
if (solve){
m <- m[stringr::str_detect(m,"solve")];
} else {
m <- m[!stringr::str_detect(m,"solve")];
}
if (length(m)!=1){
return(NA_character_)
} else {
return(m)
}
}
expandSolve <- function(x){
.x1 <- as.data.frame(x)
.x2 <- .x1
.x1$solve <- FALSE
.x2$solve <- TRUE
return(rbind(.x1,.x2));
}
# model cmt oral, mm, infusion,subset
mod2 <- matrix(c(1, 1,FALSE, FALSE, FALSE, "SD",
2, 1,FALSE, FALSE, FALSE, "MD",
4, 1,FALSE, FALSE, FALSE, "full",
3, 1,FALSE, FALSE, FALSE, "SS",
12,1,FALSE, FALSE, TRUE, "SD",
13,1,FALSE, FALSE, TRUE, "MD",
15,1,FALSE, FALSE, TRUE, "full",
14,1,FALSE, FALSE, TRUE, "SS",
# model cmt oral, mm, infusion,subset
9, 1,FALSE, TRUE, FALSE, "SD",
10,1,FALSE, TRUE, FALSE, "MD",#2
11,1,FALSE, TRUE, FALSE, "full", #2
20,1,FALSE, TRUE, TRUE, "SD",
21,1,FALSE, TRUE, TRUE, "MD",
22,1,FALSE, TRUE, TRUE, "full",
# model cmt oral, mm, infusion,subset
23,1,TRUE, FALSE, FALSE, "SD",
24,1,TRUE, FALSE, FALSE, "MD",
26,1,TRUE, FALSE, FALSE, "full",
25,1,TRUE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
29,1,TRUE, TRUE, FALSE, "SD",
30,1,TRUE, TRUE, FALSE, "MD",
31,1,TRUE, TRUE, FALSE, "full",
# model cmt oral, mm, infusion,subset
32,2,FALSE, FALSE, FALSE, "SD",
33,2,FALSE, FALSE, FALSE, "MD",
35,2,FALSE, FALSE, FALSE, "full",
34,2,FALSE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
46,2,FALSE, FALSE, TRUE, "SD",
47,2,FALSE, FALSE, TRUE, "MD",
49,2,FALSE, FALSE, TRUE, "full",
48,2,FALSE, FALSE, TRUE, "SS",
# model cmt oral, mm, infusion,subset
40,2,FALSE, TRUE, FALSE, "SD",
41,2,FALSE, TRUE, FALSE, "MD",
42,2,FALSE, TRUE, FALSE, "full",
# model cmt oral, mm, infusion,subset
54,2,FALSE, TRUE, TRUE, "SD",
55,2,FALSE, TRUE, TRUE, "MD", #3
56,2,FALSE, TRUE, TRUE, "full", #2
# model cmt oral, mm, infusion,subset
60,2,TRUE, FALSE, FALSE, "SD",
61,2,TRUE, FALSE, FALSE, "MD",
63,2,TRUE, FALSE, FALSE, "full",
62,2,TRUE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
68,2,TRUE, TRUE, FALSE, "SD",
69,2,TRUE, TRUE, FALSE, "MD",
70,2,TRUE, TRUE, FALSE, "full"
),ncol=6,byrow=TRUE,
dimnames=list(NULL,c("model", "cmt", "oral", "mm", "infusion", "subset"))) %>%
expandSolve()%>%
mutate(id=as.numeric(paste(model))) %>%
mutate(model=sprintf("U%03d",id)) %>%
mutate(oral=as.logical(paste(oral)),mm=as.logical(paste(mm)),
infusion=as.logical(paste(infusion)),solve=as.logical(paste(solve))) %>%
mutate(extra=ifelse(id %in% c(10,11,56, 24, 25, 26, 41, 42, 54),"2",
ifelse(id==55,"3",""))) %>%
group_by(id,solve) %>%
mutate(fn=getModel(cmt=cmt,oral=oral,mm=mm,extra=extra,solve=solve)) %>%
ungroup() %>% filter(!is.na(fn)) %>% mutate(model=paste0(model,ifelse(solve,"_solve",""))) %>%
mutate(data=paste0(ifelse(oral,"Oral",ifelse(infusion, "Infusion","Bolus")),"_",
cmt,"CPT",ifelse(mm,"MM","")))
if (exists("runModel", globalenv())){
mod2 <- mod2 %>% filter(model==runModel)
}
## opts <- c("focei", "saem", "nlme", "fo", "foi", "foce")
opts <- c("focei", "nlme", "saem")
if (exists("runEst", globalenv())){
opts <- runEst
}
env <- environment()
ns <- loadNamespace("nlmixr")
os <- .Platform$OS.type ## On mac this is "unix"
if (Sys.info()["sysname"]=="Darwin") os <- "mac"
for (opt in opts){
for (i in seq_along(mod2$model)){
with(mod2[i,],{
if ((solve & opt %in% c("nlme","saem")) | !solve){
.msg <- sprintf("%s: %s %s-compartment %s, %s%s",model,
opt,
ifelse(cmt==1,"one","two"),
ifelse(infusion,"infusion",ifelse(oral,"oral","bolus")),
ifelse(subset=="SD","single dose",
ifelse(subset=="MD","multiple dose",
ifelse(subset=="SS","steady state", "full data"))),
ifelse(mm,", Michelis-Menton elimination",""),
ifelse(solve," (solved)",""))
message(.msg)
context(.msg)
runno <- paste0(model,"_",opt);
assign("runno",runno,globalenv())
rds <- paste0(runno,"-", os, ".rds")
.rfile <- genIfNeeded(FALSE)
.success <- TRUE
if (file.exists(rds)){
fit <- get("fit",envir=env);
fit[[runno]] <- readRDS(rds);
assign("fit",fit, envir=env)
} else if (file.exists(.rfile)){
source(.rfile)
} else {
nlmixrFn <- get(fn,envir=env);
print(nlmixrFn)
if (exists(data,env$ns)){
datr <- get(data,env$ns)
} else {
datr <- read.csv(paste0("../../vignettes/",data,".csv"),header=TRUE,stringsAsFactors=FALSE)
}
if (subset=="SD"){
dat <- datr[datr$SD == 1, ]
dat <- dat[, names(dat) != "SS"];
} else if (subset=="MD") {
dat <- datr[datr$SD == 0, ]
dat <- dat[, names(dat) != "SS"];
} else if (subset=="full"){
dat <- datr;
dat <- dat[, names(dat) != "SS"];
} else {
dat <- datr[datr$SS != 99, ];
}
print(head(dat))
fit <- get("fit",envir=env);
if (Sys.getenv("RUN")!="false"){
.fit <- try(nlmixr(nlmixrFn, dat, opt, control=defaultControl(opt), table=tableControl(cwres=TRUE)))
} else {
.fit <- try({stop("dumb")},silent=TRUE)
}
if (!inherits(.fit, "try-error")){
fit[[runno]] <- .fit
saveRDS(fit[[runno]],rds);
assign("fit",fit, envir=env)
} else {
.success <- FALSE
}
}
if (.success){
source(genIfNeeded())
}
}})
}
## context(sprintf("%s-UI-003ode: one-compartment bolus, steady-state", opt))
## runno <- paste0(opt, "U003ode")
## fit[[runno]] <-
## source(genIfNeeded())
}
## U014
nlmixrdevelopment/nlmixr.examples documentation built on Nov. 4, 2019, 10:08 p.m.
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options(RxODE.cache.directory="~/.rxCache")
## runModel <- "U003"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U014"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U025"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U034"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U048"; runEst <- "focei"; source("uiModels.R")
## runModel <- "U062"; runEst <- "focei"; source("uiModels.R")
## runEst <- "saem"; source("uiModels.R")
## Use runModel to select one model to run. ie
## runModel <- "U014" and then source the file
## Use runEst to select one estimation type ie
## runEst <- "focei"
library(nlmixr)
library(testthat)
source("helper-prep_fit.R")
one.compartment.IV.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1 #IIV V
eta.Cl ~ 0.1 #IIV Cl
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
# RxODE-style differential equations are supported
d / dt(centr) = -(Cl / Vc) * centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.IV.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1 #IIV V
eta.Cl ~ 0.1 #IIV Cl
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
linCmt() ~ prop(prop.err)
})
}
one.compartment.IV.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
# RxODE-style differential equations are supported
d/dt(centr) = -(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.IV.MM.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 6.9 #log Vmax (mg/hr)
lKM <- 5.8 #log KM (mg/L)
lVc <- 4.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.2, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.1
eta.VM ~ 0.14
eta.KM ~ 0.1
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
# RxODE-style differential equations are supported
d/dt(centr) = -(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.8 #log Cl (L/hr)
lVc <- 4.7 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(Cl/Vc)*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.8 #log Cl (L/hr)
lVc <- 4.7 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
linCmt() ~ prop(prop.err)
})
}
one.compartment.oral.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(Cl/Vc)*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
one.compartment.oral.model2.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Cl ~ 0.15
eta.Vc ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
one.compartment.oral.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log V (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-(VM*centr/Vc)/(KM+centr/Vc);
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
# RxODE-style differential equations are supported
K10<- Cl/Vc
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-K10*centr;
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
two.compartment.IV.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7.1 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.3 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.15
eta.KM ~ 0.1
eta.Vc ~ 0.15
eta.Q ~ 0.15
eta.Vp ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.MM.model2 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.Vc ~ 0.15
eta.Q ~ 0.15
eta.Vp ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.IV.MM.model3 <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
# Simple numeric expressions are supported
lVM <- 7 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.5 #log Q (L/hr)
lVp <- 4 #log Vp (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.VM ~ 0.1
eta.KM ~ 0.1
eta.Vc ~ 0.1
eta.Q ~ 0.1
eta.Vp ~ 0.1
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
Vc <- exp(lVc + eta.Vc)
Q <- exp(lQ + eta.Q)
Vp <- exp(lVp + eta.Vp)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(centr) = K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) =-K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.oral.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
K10<- Cl/Vc
K12<- Q/Vc
K21<- Q/Vp
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot+K21*periph-K12*centr-K10*centr;
d/dt(periph) = -K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
two.compartment.oral.model.solve <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lCl <- 1.6 #log Cl (L/hr)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4 #log Vp (L)
lKA <- 0.2 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Cl ~ 0.15
eta.Vp ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
KA <- exp(lKA + eta.KA)
# And is assumed to follow proportional error estimated by prop.err
linCmt() ~ prop(prop.err)
})
}
two.compartment.oral.MM.model <- function(){
ini({ # Where initial conditions/variables are specified
# '<-' or '=' defines population parameters
lVM <- 7.1 #log Vmax (mg/hr)
lKM <- 5.7 #log KM (mg/L)
lVc <- 4.5 #log Vc (L)
lQ <- 1.6 #log Q (L/hr)
lVp <- 4.1 #log Vp (L)
lKA <- 0.22 #log V (L)
# Bounds may be specified by c(lower, est, upper), like NONMEM:
# Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.3, 1)
# Between subject variability estimates are specified by '~'
# Semicolons are optional
eta.Vc ~ 0.15
eta.Vp ~ 0.15
eta.VM ~ 0.15
eta.KM ~ 0.15
eta.Q ~ 0.15
eta.KA ~ 0.15
})
model({ # Where the model is specified
# The model uses the ini-defined variable names
Vc <- exp(lVc + eta.Vc)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
VM <- exp(lVM + eta.VM)
KM <- exp(lKM + eta.KM)
KA <- exp(lKA + eta.KA)
# RxODE-style differential equations are supported
K12<- Q/Vc
K21<- Q/Vp
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot+K21*periph-K12*centr-(VM*centr/Vc)/(KM+centr/Vc);
d/dt(periph) = -K21*periph+K12*centr;
## Concentration is calculated
cp = centr / Vc;
# And is assumed to follow proportional error estimated by prop.err
cp ~ prop(prop.err)
})
}
require(dplyr);
nmModels <- (ls(pattern="[.]compartment[.]"))
getModel <- function(cmt=1,oral=FALSE,mm=FALSE,extra="",solve=FALSE){
m <- nmModels %>%
stringr::str_subset(ifelse(cmt==1,"one","two")) %>%
stringr::str_subset(ifelse(oral,"oral","IV"))
if (mm){
m <- m %>% stringr::str_subset("MM")
} else {
m <- m[!stringr::str_detect(m,"MM")];
}
if (extra==""){
m <- m[!stringr::str_detect(m,"[23]")];
} else {
m <- m %>% stringr::str_subset(extra);
}
if (solve){
m <- m[stringr::str_detect(m,"solve")];
} else {
m <- m[!stringr::str_detect(m,"solve")];
}
if (length(m)!=1){
return(NA_character_)
} else {
return(m)
}
}
expandSolve <- function(x){
.x1 <- as.data.frame(x)
.x2 <- .x1
.x1$solve <- FALSE
.x2$solve <- TRUE
return(rbind(.x1,.x2));
}
# model cmt oral, mm, infusion,subset
mod2 <- matrix(c(1, 1,FALSE, FALSE, FALSE, "SD",
2, 1,FALSE, FALSE, FALSE, "MD",
4, 1,FALSE, FALSE, FALSE, "full",
3, 1,FALSE, FALSE, FALSE, "SS",
12,1,FALSE, FALSE, TRUE, "SD",
13,1,FALSE, FALSE, TRUE, "MD",
15,1,FALSE, FALSE, TRUE, "full",
14,1,FALSE, FALSE, TRUE, "SS",
# model cmt oral, mm, infusion,subset
9, 1,FALSE, TRUE, FALSE, "SD",
10,1,FALSE, TRUE, FALSE, "MD",#2
11,1,FALSE, TRUE, FALSE, "full", #2
20,1,FALSE, TRUE, TRUE, "SD",
21,1,FALSE, TRUE, TRUE, "MD",
22,1,FALSE, TRUE, TRUE, "full",
# model cmt oral, mm, infusion,subset
23,1,TRUE, FALSE, FALSE, "SD",
24,1,TRUE, FALSE, FALSE, "MD",
26,1,TRUE, FALSE, FALSE, "full",
25,1,TRUE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
29,1,TRUE, TRUE, FALSE, "SD",
30,1,TRUE, TRUE, FALSE, "MD",
31,1,TRUE, TRUE, FALSE, "full",
# model cmt oral, mm, infusion,subset
32,2,FALSE, FALSE, FALSE, "SD",
33,2,FALSE, FALSE, FALSE, "MD",
35,2,FALSE, FALSE, FALSE, "full",
34,2,FALSE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
46,2,FALSE, FALSE, TRUE, "SD",
47,2,FALSE, FALSE, TRUE, "MD",
49,2,FALSE, FALSE, TRUE, "full",
48,2,FALSE, FALSE, TRUE, "SS",
# model cmt oral, mm, infusion,subset
40,2,FALSE, TRUE, FALSE, "SD",
41,2,FALSE, TRUE, FALSE, "MD",
42,2,FALSE, TRUE, FALSE, "full",
# model cmt oral, mm, infusion,subset
54,2,FALSE, TRUE, TRUE, "SD",
55,2,FALSE, TRUE, TRUE, "MD", #3
56,2,FALSE, TRUE, TRUE, "full", #2
# model cmt oral, mm, infusion,subset
60,2,TRUE, FALSE, FALSE, "SD",
61,2,TRUE, FALSE, FALSE, "MD",
63,2,TRUE, FALSE, FALSE, "full",
62,2,TRUE, FALSE, FALSE, "SS",
# model cmt oral, mm, infusion,subset
68,2,TRUE, TRUE, FALSE, "SD",
69,2,TRUE, TRUE, FALSE, "MD",
70,2,TRUE, TRUE, FALSE, "full"
),ncol=6,byrow=TRUE,
dimnames=list(NULL,c("model", "cmt", "oral", "mm", "infusion", "subset"))) %>%
expandSolve()%>%
mutate(id=as.numeric(paste(model))) %>%
mutate(model=sprintf("U%03d",id)) %>%
mutate(oral=as.logical(paste(oral)),mm=as.logical(paste(mm)),
infusion=as.logical(paste(infusion)),solve=as.logical(paste(solve))) %>%
mutate(extra=ifelse(id %in% c(10,11,56, 24, 25, 26, 41, 42, 54),"2",
ifelse(id==55,"3",""))) %>%
group_by(id,solve) %>%
mutate(fn=getModel(cmt=cmt,oral=oral,mm=mm,extra=extra,solve=solve)) %>%
ungroup() %>% filter(!is.na(fn)) %>% mutate(model=paste0(model,ifelse(solve,"_solve",""))) %>%
mutate(data=paste0(ifelse(oral,"Oral",ifelse(infusion, "Infusion","Bolus")),"_",
cmt,"CPT",ifelse(mm,"MM","")))
if (exists("runModel", globalenv())){
mod2 <- mod2 %>% filter(model==runModel)
}
## opts <- c("focei", "saem", "nlme", "fo", "foi", "foce")
opts <- c("focei", "nlme", "saem")
if (exists("runEst", globalenv())){
opts <- runEst
}
env <- environment()
ns <- loadNamespace("nlmixr")
os <- .Platform$OS.type ## On mac this is "unix"
if (Sys.info()["sysname"]=="Darwin") os <- "mac"
for (opt in opts){
for (i in seq_along(mod2$model)){
with(mod2[i,],{
if ((solve & opt %in% c("nlme","saem")) | !solve){
.msg <- sprintf("%s: %s %s-compartment %s, %s%s",model,
opt,
ifelse(cmt==1,"one","two"),
ifelse(infusion,"infusion",ifelse(oral,"oral","bolus")),
ifelse(subset=="SD","single dose",
ifelse(subset=="MD","multiple dose",
ifelse(subset=="SS","steady state", "full data"))),
ifelse(mm,", Michelis-Menton elimination",""),
ifelse(solve," (solved)",""))
message(.msg)
context(.msg)
runno <- paste0(model,"_",opt);
assign("runno",runno,globalenv())
rds <- paste0(runno,"-", os, ".rds")
.rfile <- genIfNeeded(FALSE)
.success <- TRUE
if (file.exists(rds)){
fit <- get("fit",envir=env);
fit[[runno]] <- readRDS(rds);
assign("fit",fit, envir=env)
} else if (file.exists(.rfile)){
source(.rfile)
} else {
nlmixrFn <- get(fn,envir=env);
print(nlmixrFn)
if (exists(data,env$ns)){
datr <- get(data,env$ns)
} else {
datr <- read.csv(paste0("../../vignettes/",data,".csv"),header=TRUE,stringsAsFactors=FALSE)
}
if (subset=="SD"){
dat <- datr[datr$SD == 1, ]
dat <- dat[, names(dat) != "SS"];
} else if (subset=="MD") {
dat <- datr[datr$SD == 0, ]
dat <- dat[, names(dat) != "SS"];
} else if (subset=="full"){
dat <- datr;
dat <- dat[, names(dat) != "SS"];
} else {
dat <- datr[datr$SS != 99, ];
}
print(head(dat))
fit <- get("fit",envir=env);
if (Sys.getenv("RUN")!="false"){
.fit <- try(nlmixr(nlmixrFn, dat, opt, control=defaultControl(opt), table=tableControl(cwres=TRUE)))
} else {
.fit <- try({stop("dumb")},silent=TRUE)
}
if (!inherits(.fit, "try-error")){
fit[[runno]] <- .fit
saveRDS(fit[[runno]],rds);
assign("fit",fit, envir=env)
} else {
.success <- FALSE
}
}
if (.success){
source(genIfNeeded())
}
}})
}
## context(sprintf("%s-UI-003ode: one-compartment bolus, steady-state", opt))
## runno <- paste0(opt, "U003ode")
## fit[[runno]] <-
## source(genIfNeeded())
}
## U014
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