library(rxode2)
#> rxode2 2.1.2.9000 using 2 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`There are two fundamental operations that you may wish to do in
rxode2/nlmixr2. First you might want to modify
your model (ie add covariate effects, add between subject variability,
etc). The second thing you may wish to do is change initial estimates,
change the boundaries of the problem, fix/unfix the initial estimates,
etc.
Modifying model
There are a few tasks you might want to do with the overall model:
Change a line in the model
Add a line to the model
Rename parameters in the model
Combine different models
Create functions to add certain model features to the model
We will go over the model piping and other functions that you can use to modify models and even add your own functions that modify models.
We will not cover any of the model modification functions in
nlmixr2lib
Modifying a model line
In my opinion, modifying lines in a model is likely the most common task in modifying a model. We may wish to modify the model to have a between subject variability or add a covariate effects.
To begin of course you need a base model to modify. Let’s start with a very simple PK example, using the single-dose theophylline dataset generously provided by Dr. Robert A. Upton of the University of California, San Francisco:
one.compartment <- function() {
ini({
tka <- 0.45; label("Ka")
tcl <- 1; label("Cl")
tv <- 3.45; label("V")
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
d/dt(depot) = -ka * depot
d/dt(center) = ka * depot - cl / v * center
cp = center / v
cp ~ add(add.sd)
})
}If we believed we did not have enough absorption to support between subject variability you can change the line to drop the between subject by modifying a single line. To do this simply type the line you want in the model piping expression:
mod <- one.compartment |>
model(ka <- exp(tka))
#> ! remove between subject variability `eta.ka`
print(mod)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.cl eta.v
#> eta.cl 0.3 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }As expected, the line is modified. Also you can notice that the initial estimate for the between subject variability is dropped since it is no longer part of the model.
If for some reason you wanted to add it back to the model you can modify the model and add it back:
mod2 <- mod |>
model(ka <- tka * exp(eta.ka))
#> ℹ add between subject variability `eta.ka` and set estimate to 1
print(mod2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.cl eta.v eta.ka
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.ka 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.ka ~ 1
#> })
#> model({
#> ka <- tka * exp(eta.ka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }In this modification, the eta.ka is automatically
assumed to be a between subject variability parameter. Also since
eta.ka is not mu-referenced rxode2 points this
out.
The automatic detection of eta.ka is because the name
follows a convention. Parameters starting or ending with the following
names are assumed to be between subject variability parameters:
- eta (from NONMEM convention)
- ppv (per patient variability)
- psv (per subject variability)
- iiv (inter-individual variability)
- bsv (between subject variability)
- bpv (between patient variability)
If this is not functioning correctly you can change it to a covariate which you can add a type of initial estimate to later:
mod2 <- mod |>
model(ka <- tka * exp(eta.ka) + WT * covWt, cov="eta.ka")
#> ℹ add covariate `eta.ka` (as requested by cov option)
#> ℹ add covariate `WT`
#> ℹ add population parameter `covWt` and set estimate to 1
print(mod2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd covWt
#> 0.45 1.00 3.45 0.70 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v
#> eta.cl 0.3 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level covariates
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> covWt <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- tka * exp(eta.ka) + WT * covWt
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }As seen above, the eta.ka in the above model is assumed
to be a data-input parameter or covariate instead of an estimated
parameter.
You can also note that WT is automatically recognized as
a covariate and covWt is automatically recognized as a
covariate parameter.
In general covariates and typical/population parameters are automatically converted to estimated parameters based on the parameter name starting with (or ending with):
- tv (for typical value)
- t (also for typical value)
- pop (for population parameter)
- err (for error parameter)
- eff (for effect parameter)
- cov (for covariate parameters)
This has a few notable exceptions for parameters like
(wt, sex and crcl) which are
assumed to be covariates.
If you don’t want any automatic variable conversion, you can also use
auto=FALSE:
mod3 <- mod |>
model(ka <- tka * exp(eta.ka) + WT * covWt, auto=FALSE)
#> ℹ add covariate `eta.ka`
#> ℹ add covariate `WT`
#> ℹ add covariate `covWt`
print(mod3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.cl eta.v
#> eta.cl 0.3 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level covariates
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- tka * exp(eta.ka) + WT * covWt
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }In this case all additional parameters (eta.ka,
WT, and covWt) are assumed to be parameters in
the dataset.
Note on automatic detection of variables
The automatic detection of variables is convenient for many models
but may not suit your style; If you do not like it you can always change
it by using options():
options(rxode2.autoVarPiping=FALSE)With this option disabled, all variables will be assumed to be
covariates and you will have to promote them to population parameters
with the ini block
In the last example with this option enabled none of the variables
starting with t will be added to the model
mod7 <- mod3 |>
model({
emax <- exp(temax)
e0 <- exp(te0 + eta.e0)
ec50 <- exp(tec50)
kin <- exp(tkin)
kout <- exp(tkout)
}, append=FALSE)
print(mod7)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.cl eta.v
#> eta.cl 0.3 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level covariates
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- tka * exp(eta.ka) + WT * covWt
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }Of course you could use it and then turn it back on:
options(rxode2.autoVarPiping=TRUE)
mod8 <- mod |>
model({
emax <- exp(temax)
e0 <- exp(te0 + eta.e0)
ec50 <- exp(tec50)
kin <- exp(tkin)
kout <- exp(tkout)
}, append=FALSE)
#> ℹ promote `temax` to population parameter with initial estimate 1
#> ℹ promote `te0` to population parameter with initial estimate 1
#> ℹ promote `eta.e0` to between subject variability with initial estimate 1
#> ℹ promote `tec50` to population parameter with initial estimate 1
#> ℹ promote `tkin` to population parameter with initial estimate 1
#> ℹ promote `tkout` to population parameter with initial estimate 1
print(mod8)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd temax te0 tec50 tkin tkout
#> 0.45 1.00 3.45 0.70 1.00 1.00 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> temax <- 1
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tkout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }Or you can use the
withr::with_options(list(rxode2.autoVarPiping=FALSE), ...)
to turn the option on temporarily.
If you don’t like the defaults for changing variables you could
change them as well with rxSetPipingAuto()
For example if you only wanted variables starting or ending with
te you can change this with:
rxSetPipingAuto(thetamodelVars = rex::rex("te"))
mod9 <- mod |>
model({
emax <- exp(temax)
e0 <- exp(te0 + eta.e0)
ec50 <- exp(tec50)
kin <- exp(tkin)
kout <- exp(tkout)
}, append=FALSE)
#> ℹ promote `temax` to population parameter with initial estimate 1
#> ℹ promote `te0` to population parameter with initial estimate 1
#> ℹ promote `eta.e0` to between subject variability with initial estimate 1
#> ℹ promote `tec50` to population parameter with initial estimate 1
print(mod9)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd temax te0 tec50
#> 0.45 1.00 3.45 0.70 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> temax <- 1
#> te0 <- 1
#> tec50 <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }And as requested only the population parameters starting with
te are added to the ini block.
If you want to reset the defaults you simply call
rxSetPipingAuto() without any arguments:
rxSetPipingAuto()
mod10 <- mod |>
model({
emax <- exp(temax)
e0 <- exp(te0 + eta.e0)
ec50 <- exp(tec50)
kin <- exp(tkin)
kout <- exp(tkout)
}, append=FALSE)
#> ℹ promote `temax` to population parameter with initial estimate 1
#> ℹ promote `te0` to population parameter with initial estimate 1
#> ℹ promote `eta.e0` to between subject variability with initial estimate 1
#> ℹ promote `tec50` to population parameter with initial estimate 1
#> ℹ promote `tkin` to population parameter with initial estimate 1
#> ℹ promote `tkout` to population parameter with initial estimate 1Adding model lines
There are three ways to insert lines in a
rxode2/nlmixr2 model. You can add lines to the
end of the model, after an expression or to the beginning of the model
all controlled by the append option.
Let’s assume that there are two different assays that were run with the same compound and you have noticed that they both have different variability.
You can modify the model above by adding some lines to the end of the
model by using append=TRUE:
mod4 <- mod |>
model({
cp2 <- cp
cp2 ~ lnorm(lnorm.sd)
}, append=TRUE)
#> ℹ add residual parameter `lnorm.sd` and set estimate to 1
print(mod4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd lnorm.sd
#> 0.45 1.00 3.45 0.70 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v
#> eta.cl 0.3 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── Multiple Endpoint Model ($multipleEndpoint): ──
#> variable cmt dvid*
#> 1 cp ~ … cmt='cp' or cmt=3 dvid='cp' or dvid=1
#> 2 cp2 ~ … cmt='cp2' or cmt=4 dvid='cp2' or dvid=2
#> * If dvids are outside this range, all dvids are re-numered sequentially, ie 1,7, 10 becomes 1,2,3 etc
#>
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tcl eta.cl id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> lnorm.sd <- c(0, 1)
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> cp2 <- cp
#> cp2 ~ lnorm(lnorm.sd)
#> })
#> }Perhaps instead you may want to add an indirect response model in
addition to the concentrations, you can choose where to add this: with
append=lhsVar where lhsVar is the left handed
variable above where you want to insert the new lines:
mod5 <- mod |>
model({
PD <- 1-emax*cp/(ec50+cp)
##
effect(0) <- e0
kin <- e0*kout
d/dt(effect) <- kin*PD -kout*effect
}, append=d/dt(center))The last type of insertion you may wish to do is to add lines to the
beginning of the model by using append=FALSE:
mod6 <- mod5 |>
model({
emax <- exp(temax)
e0 <- exp(te0 + eta.e0)
ec50 <- exp(tec50)
kin <- exp(tkin)
kout <- exp(tkout)
}, append=FALSE)
#> ℹ promote `temax` to population parameter with initial estimate 1
#> ℹ promote `te0` to population parameter with initial estimate 1
#> ℹ promote `eta.e0` to between subject variability with initial estimate 1
#> ℹ promote `tec50` to population parameter with initial estimate 1
#> ℹ promote `tkin` to population parameter with initial estimate 1
#> ℹ promote `tkout` to population parameter with initial estimate 1
print(mod6)
#> ── rxode2-based free-form 3-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd temax te0 tec50 tkin tkout
#> 0.45 1.00 3.45 0.70 1.00 1.00 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> 3 3 effect
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> temax <- 1
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tkout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> PD <- 1 - emax * cp/(ec50 + cp)
#> effect(0) <- e0
#> kin <- e0 * kout
#> d/dt(effect) <- kin * PD - kout * effect
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }Remove lines in the model
The lines in a model can be removed in one of 2 ways either use
-param or param <- NULL in model
piping:
mod7 <- mod6 |>
model(-emax)
#> ! remove population parameter `temax`
print(mod7)
#> ── rxode2-based free-form 3-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd te0 tec50 tkin tkout
#> 0.45 1.00 3.45 0.70 1.00 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> 3 3 effect
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tkout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> PD <- 1 - emax * cp/(ec50 + cp)
#> effect(0) <- e0
#> kin <- e0 * kout
#> d/dt(effect) <- kin * PD - kout * effect
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }
# Equivalently
mod8 <- mod6 |>
model(emax <- NULL)
#> ! remove population parameter `temax`
print(mod8)
#> ── rxode2-based free-form 3-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd te0 tec50 tkin tkout
#> 0.45 1.00 3.45 0.70 1.00 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> 3 3 effect
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tkout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> PD <- 1 - emax * cp/(ec50 + cp)
#> effect(0) <- e0
#> kin <- e0 * kout
#> d/dt(effect) <- kin * PD - kout * effect
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }Rename parameters in a model
You may want to rename parameters in a model, which is easy to do
with rxRename(). When dplyr is loaded you can
even replace it with rename(). The semantics are similar
between the two functions, that is you assigning
newVar=oldVar. For example:
mod11 <- mod10 |>
rxRename(drug1kout=kout, tv.drug1kout=tkout)
print(mod11)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd temax te0
#> 0.45 1.00 3.45 0.70 1.00 1.00
#> tec50 tkin tv.drug1kout
#> 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> temax <- 1
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tv.drug1kout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> drug1kout <- exp(tv.drug1kout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }You can see every instance of the variable is named in the model is
renamed inside the model and ini block.
For completeness you can see this with the dplyr verb
(since it is a S3 method):
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
mod12 <- mod10 |>
rename(drug1kout=kout, tv.drug1kout=tkout)
print(mod12)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd temax te0
#> 0.45 1.00 3.45 0.70 1.00 1.00
#> tec50 tkin tv.drug1kout
#> 1.00 1.00 1.00
#>
#> Omega ($omega):
#> eta.cl eta.v eta.e0
#> eta.cl 0.3 0.0 0
#> eta.v 0.0 0.1 0
#> eta.e0 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 te0 eta.e0 id
#> 2 tcl eta.cl id
#> 3 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Ka")
#> tcl <- 1
#> label("Cl")
#> tv <- 3.45
#> label("V")
#> add.sd <- c(0, 0.7)
#> temax <- 1
#> te0 <- 1
#> tec50 <- 1
#> tkin <- 1
#> tv.drug1kout <- 1
#> eta.cl ~ 0.3
#> eta.v ~ 0.1
#> eta.e0 ~ 1
#> })
#> model({
#> emax <- exp(temax)
#> e0 <- exp(te0 + eta.e0)
#> ec50 <- exp(tec50)
#> kin <- exp(tkin)
#> drug1kout <- exp(tv.drug1kout)
#> ka <- exp(tka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) = -ka * depot
#> d/dt(center) = ka * depot - cl/v * center
#> cp = center/v
#> cp ~ add(add.sd)
#> })
#> }Combine different models
You can also combine different models with
rxAppendModel(). In general they need variables in common
to combine. This is because you generally want the models to link
between each other. In the below example a pk and pd model this is done
by renaming cp in the first model to ceff in
the second model:
ocmt <- function() {
ini({
tka <- exp(0.45) # Ka
tcl <- exp(1) # Cl
tv <- exp(3.45); # log V
## the label("Label name") works with all models
add.sd <- 0.7
})
model({
ka <- tka
cl <- tcl
v <- tv
d/dt(depot) <- -ka * depot
d/dt(center) <- ka * depot - cl / v * center
cp <- center / v
cp ~ add(add.sd)
})
}
idr <- function() {
ini({
tkin <- log(1)
tkout <- log(1)
tic50 <- log(10)
gamma <- fix(1)
idr.sd <- 1
})
model({
kin <- exp(tkin)
kout <- exp(tkout)
ic50 <- exp(tic50)
d/dt(eff) <- kin - kout*(1-ceff^gamma/(ic50^gamma+ceff^gamma))
eff ~ add(idr.sd)
})
}
rxAppendModel(ocmt %>% rxRename(ceff=cp), idr)
#> ℹ parameter labels from comments will be replaced by 'label()'
#> ── rxode2-based free-form 3-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd tkin tkout tic50 gamma
#> 1.568312 2.718282 31.500392 0.700000 0.000000 0.000000 2.302585 1.000000
#> idr.sd
#> 1.000000
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> 3 3 eff
#> ── Multiple Endpoint Model ($multipleEndpoint): ──
#> variable cmt dvid*
#> 1 ceff ~ … cmt='ceff' or cmt=4 dvid='ceff' or dvid=1
#> 2 eff ~ … cmt='eff' or cmt=3 dvid='eff' or dvid=2
#> * If dvids are outside this range, all dvids are re-numered sequentially, ie 1,7, 10 becomes 1,2,3 etc
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 1.56831218549017
#> label("Ka")
#> tcl <- 2.71828182845905
#> label("Cl")
#> tv <- 31.5003923087479
#> label("log V")
#> add.sd <- c(0, 0.7)
#> tkin <- 0
#> tkout <- 0
#> tic50 <- 2.30258509299405
#> gamma <- fix(1)
#> idr.sd <- c(0, 1)
#> })
#> model({
#> ka <- tka
#> cl <- tcl
#> v <- tv
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> ceff <- center/v
#> ceff ~ add(add.sd)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ic50 <- exp(tic50)
#> d/dt(eff) <- kin - kout * (1 - ceff^gamma/(ic50^gamma +
#> ceff^gamma))
#> eff ~ add(idr.sd)
#> })
#> }You will get an error if you try to combine models without variables in common:
try(rxAppendModel(ocmt, idr))
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Error : not all the models have variables in common (use `common=FALSE` to allow this)If you want to combine the models without respecting the having the
variables in common, you can use common=FALSE:
mod2 <- rxAppendModel(ocmt, idr, common=FALSE) |>
model(ceff=cp, append=ic50) # here we add the translation after the
#> ℹ parameter labels from comments will be replaced by 'label()'
# ic50 line to make it reasonable
print(mod2)
#> ── rxode2-based free-form 3-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd tkin tkout tic50 gamma
#> 1.568312 2.718282 31.500392 0.700000 0.000000 0.000000 2.302585 1.000000
#> idr.sd
#> 1.000000
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> 3 3 eff
#> ── Multiple Endpoint Model ($multipleEndpoint): ──
#> variable cmt dvid*
#> 1 cp ~ … cmt='cp' or cmt=4 dvid='cp' or dvid=1
#> 2 eff ~ … cmt='eff' or cmt=3 dvid='eff' or dvid=2
#> * If dvids are outside this range, all dvids are re-numered sequentially, ie 1,7, 10 becomes 1,2,3 etc
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 1.56831218549017
#> label("Ka")
#> tcl <- 2.71828182845905
#> label("Cl")
#> tv <- 31.5003923087479
#> label("log V")
#> add.sd <- c(0, 0.7)
#> tkin <- 0
#> tkout <- 0
#> tic50 <- 2.30258509299405
#> gamma <- fix(1)
#> idr.sd <- c(0, 1)
#> })
#> model({
#> ka <- tka
#> cl <- tcl
#> v <- tv
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> kin <- exp(tkin)
#> kout <- exp(tkout)
#> ic50 <- exp(tic50)
#> ceff <- cp
#> d/dt(eff) <- kin - kout * (1 - ceff^gamma/(ic50^gamma +
#> ceff^gamma))
#> eff ~ add(idr.sd)
#> })
#> }Creating more complex model modification functions
These are pretty flexible, but you may want to do even more, so there are some helper functions to help you create functions to do more. We will discuss how to extract the model from the function and how to update it.
Lets start with a model:
f <- function() {
ini({
tka <- 0.45
tcl <- 1
tv <- 3.45
eta.ka ~ 0.6
eta.v ~ 0.1
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl)
v <- exp(tv + eta.v)
d/dt(depot) <- -ka * depot
d/dt(center) <- ka * depot - cl/v * center
cp <- center/v
})
}Lets assume for a moment you want to remove an eta to
cl. First you probably want to get all the model lines. You
can do that with modelExtract():
totLines <- modelExtract(f, endpoint=NA) # endpoints should be included
print(totLines)
#> [1] "ka <- exp(tka + eta.ka)"
#> [2] "cl <- exp(tcl)"
#> [3] "v <- exp(tv + eta.v)"
#> [4] "d/dt(depot) <- -ka * depot"
#> [5] "d/dt(center) <- ka * depot - cl/v * center"
#> [6] "cp <- center/v"Now you want to only worry about the cl line, you can
subset here:
clLine <- modelExtract(f, cl, lines=TRUE)
line <- attr(clLine, "lines")Now I wish to change the line to “cl <- exp(tcl+eta.cl)”
totLines[line] <- "cl <- exp(tcl+eta.cl)"
# For now lets remove the entire `ini` block (so you don't have to
# worry about syncing parameters).
#
ini(f) <- NULL
model(f) <- totLines
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
print(f)
#> function ()
#> {
#> ini({
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl + eta.cl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> })
#> }
#> <environment: 0x55d01b583010>Note that these functions do not modify the ini({})
block. You may have to modify the ini block first to make it a valid
rxode2/nlmixr2 model.
In this particular case, using model piping would be easier, but it simply demonstrates two different way to extract model information and a way to add information to the final model.
These methods can be tricky because when using them you have to have model that is parsed correctly. This means you have to make sure the parameters and endpoints follow the correct rules
Modifying initial estimates
The common items you want to do with initial estimates are:
Fix/Unfix a parameter
Change the initial condition values and bounds
Change the initial condition type
Change labels and transformations
Reorder parameters
You may wish to create your own functions; we will discuss this too.
Fixing or unfixing a parameter
You can fix model estimates in two ways. The first is to fix the
value to whatever is in the model function, this is done by piping the
model parameter name (like tka) and setting it equal to
fix (%>% ini(tka=fix)). Below is a full
example:
f <- function() {
ini({
tka <- 0.45
tcl <- 1
tv <- 3.45
add.sd <- c(0, 0.7)
eta.ka ~ 0.6
eta.v ~ 0.1
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl)
v <- exp(tv + eta.v)
d/dt(depot) <- -ka * depot
d/dt(center) <- ka * depot - cl/v * center
cp <- center/v
cp ~ add(add.sd)
})
}
f2 <- f |>
ini(tka=fix)
#> ℹ fix `tka` to `0.45`
print(f2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- fix(0.45)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }You can also fix the parameter to a different value if you wish; This
is very similar you can specify the value to fix inside of a
fix pseudo-function as follows:
%>% ini(tka=fix(0.1)). A fully worked example is
below:
f <- function() {
ini({
tka <- 0.45
tcl <- 1
tv <- 3.45
add.sd <- c(0, 0.7)
eta.ka ~ 0.6
eta.v ~ 0.1
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl)
v <- exp(tv + eta.v)
d/dt(depot) <- -ka * depot
d/dt(center) <- ka * depot - cl/v * center
cp <- center/v
cp ~ add(add.sd)
})
}
f2 <- f |>
ini(tka=fix(0.1))
#> ℹ fix `tka` to `0.45`
#> ℹ change initial estimate of `tka` to `0.1`
print(f2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- fix(0.1)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Unfixing parameters
You an unfix parameters very similarly to fixing. Instead of using
the fix keyword, you use the unfix keyword. So
to unfix a parameter (keeping its value) you would pipe the model using
(|> ini(tka=unfix)). Starting with the fixed model above
a fully worked example is:
print(f2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- fix(0.1)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
f3 <- f2 |> ini(tka=unfix)
#> ℹ unfix `tka` keeping initial estimate `0.1`
print(f3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }You can also unfix and change the initial estimate with
ini(parameter=unfix(newEst)):
print(f2)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- fix(0.1)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
f3 <- f2 |>
ini(tka=unfix(10))
#> ℹ unfix `tka` keeping initial estimate `0.1`
#> ℹ change initial estimate of `tka` to `10`
print(f3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 10.00 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 10
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Changing the parameter values and possibly bounds
Multiple parameter assignment
You can also assign multiple parameters by providing them:
As a vector/list
As multiple lines in a piped
ini()blockUsing a covariance matrix
In the case of a vector you can specify them and then pipe the model.
For example:
ini1 <- c(tka=0.1, tcl=1, tv=3)
f4 <- f |> ini(ini1)
#> ℹ change initial estimate of `tka` to `0.1`
#> ℹ change initial estimate of `tcl` to `1`
#> ℹ change initial estimate of `tv` to `3`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.1 1.0 3.0 0.7
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# or equivalently
ini1 <- list(tka=0.1, tcl=1, tv=3)
f4a <- f |> ini(ini1)
#> ℹ change initial estimate of `tka` to `0.1`
#> ℹ change initial estimate of `tcl` to `1`
#> ℹ change initial estimate of `tv` to `3`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.1 1.0 3.0 0.7
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }This can also be added with multiple lines or commas separating estimates:
# commas separating values:
f4 <- f |> ini(tka=0.1, tcl=1, tv=3)
#> ℹ change initial estimate of `tka` to `0.1`
#> ℹ change initial estimate of `tcl` to `1`
#> ℹ change initial estimate of `tv` to `3`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.1 1.0 3.0 0.7
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# multiple lines in {}
f4 <- f |>
ini({
tka <- 0.2
tcl <- 2
tv <- 6
})
#> ℹ change initial estimate of `tka` to `0.2`
#> ℹ change initial estimate of `tcl` to `2`
#> ℹ change initial estimate of `tv` to `6`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.2 2.0 6.0 0.7
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.2
#> tcl <- 2
#> tv <- 6
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }You could also use a matrix to specify the covariance:
ome <- lotri(eta.ka + eta.v ~ c(0.6,
0.01, 10.1))
f4 <- f |> ini(ome)
#> ℹ change initial estimate of `eta.ka` to `0.6`
#> ℹ add covariance between `eta.v` and `eta.ka` with initial estimate `0.01`
#> ℹ change initial estimate of `eta.v` to `10.1`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.60 0.01
#> eta.v 0.01 10.10
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka + eta.v ~ c(0.6, 0.01, 10.1)
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# or equavialtly use the lotri-type syntax for the omega:
f4 <- f |> ini(eta.ka + eta.v ~ c(0.6,
0.01, 0.2))
#> ℹ change initial estimate of `eta.ka` to `0.6`
#> ℹ add covariance between `eta.v` and `eta.ka` with initial estimate `0.01`
#> ℹ change initial estimate of `eta.v` to `0.2`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.60 0.01
#> eta.v 0.01 0.20
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka + eta.v ~ c(0.6, 0.01, 0.2)
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Single parameter assignment
The simplest way to change the initial parameter estimates is to
simply use ini(parameter=newValue). You can also use
<- or ~ to change the value:
A fully worked example showing all three types of initial value modification is:
f3 <- f |>
ini(tka <- 0.1)
#> ℹ change initial estimate of `tka` to `0.1`
f4 <- f |>
ini(tka=0.1)
#> ℹ change initial estimate of `tka` to `0.1`
f5 <- f |>
ini(tka ~ 0.1)
#> ℹ change initial estimate of `tka` to `0.1`
print(f5)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }You can change the bounds like you do in the model specification by
using a numeric vector of c(low, estimate) or
c(low, estimate, hi). Here is a worked example:
f3 <- f |>
ini(tka <- c(0, 0.1, 0.2))
#> ℹ change initial estimate (0.1) and upper/lower bound (0 to 0.2) of `tka`
print(f3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- c(0, 0.1, 0.2)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
f3 <- f |>
ini(tka <- c(0, 0.1))
#> ℹ change initial estimate (0.1) and lower bound (0) of `tka`
print(f3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- c(0, 0.1)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Note by changing the parameters to their default values they might not show up in the parameter printout:
f3 <- f |>
ini(tka <- c(0, 0.1, 0.2))
#> ℹ change initial estimate (0.1) and upper/lower bound (0 to 0.2) of `tka`
print(f3)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- c(0, 0.1, 0.2)
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# Now reassign
f4 <- f3 |>
ini(tka <- c(-Inf, 0.1, Inf))
#> ℹ change initial estimate (0.1) and upper/lower bound (-Inf to Inf) of `tka`
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.10 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.1
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Changing parameter types
You can change the parameter type by two operators either by using
-par to convert the parameter to a covariate or
~par to toggle between population and individual
parameters.
Here is an example that does all 3:
# Switch population parameter to between subject variability parameter:
f4 <- f |>
ini( ~ tcl)
#> ℹ convert 'tcl' from population parameter to between subject variability
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd
#> 0.45 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v tcl
#> eta.ka 0.6 0.0 0
#> eta.v 0.0 0.1 0
#> tcl 0.0 0.0 1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> tcl ~ 1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# Switch back to population parameter
f5 <- f4 |>
ini( ~ tcl)
#> ℹ convert 'tcl' from between subject variability to population parameter
print(f5)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd tcl
#> 0.45 3.45 0.70 1.00
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> tcl <- 1
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# Change the variable to a covariate parameter (ie it doesn't have an
# initial estimate so remove it with the `-` operator):
f6 <- f4 |>
ini(-tcl)
#> ℹ changing between subject variability parameter 'tcl' to covariate parameter
print(f6)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd
#> 0.45 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# You can change the covariate or remove the parameter estimate by
# `tcl <- NULL`:
f6 <- f4 |>
ini(tcl <- NULL)
#> ℹ changing between subject variability parameter 'tcl' to covariate parameter
print(f6)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd
#> 0.45 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
# to add it back as a between subject variability or population
# parameter you can pipe it as follows:
f7 <- f6 |>
ini(tcl=4)
#> ℹ promote `tcl` to population parameter with initial estimate 4
#> ℹ change initial estimate of `tcl` to `4`
print(f7)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd tcl
#> 0.45 3.45 0.70 4.00
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> tcl <- 4
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }
f8 <- f6 |>
ini(tcl ~ 0.1)
#> ℹ promote `tcl` to between subject variability with initial estimate 0.1
#> ℹ change initial estimate of `tcl` to `0.1`
print(f8)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tv add.sd
#> 0.45 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v tcl
#> eta.ka 0.6 0.0 0.0
#> eta.v 0.0 0.1 0.0
#> tcl 0.0 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> tcl ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Changing parameter labels
If you want to change/add a parameter label you assign the parameter
to label("label to add"). For example:
f4 <- f |>
ini(tka=label("Typical Ka (1/hr)"))
print(f4)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> label("Typical Ka (1/hr)")
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }You can also change the order while performing operations:
f5 <- f |>
ini(tka=label("Typical Ka (1/hr)"), append=tcl)
print(f5)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tcl tka tv add.sd
#> 1.00 0.45 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tcl <- 1
#> tka <- 0.45
#> label("Typical Ka (1/hr)")
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }If you want to remove the labels you can remove them with
ini(par=label(NULL)); For example:
f6 <- f |>
ini(tka=label(NULL))
print(f6)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }Changing parameter transformations
Back-transformations over-ride the back transformations in
nlmixr2 models. They are very similar to the modification
of the labels.
Here you use |> ini(tka=backTransform(exp)) to add an
exponential back-transformation for data:
f7 <- f |>
ini(tka=backTransform(exp))
print(f7)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> backTransform("exp")
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }If you wish to remove them you can also do that with
|> ini(tka=backTransform(NULL)):
f8 <- f |>
ini(tka=backTransform(NULL))
print(f8)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 0.45 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 0.45
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }More granular access of initial conditions
Just like with model() you can modify the underlying
data frame that represents the ini() block. In this case I
will simply change the initial estimate of the first parameter
(tka):
f <- rxode2(f)
ini <- f$iniDf
print(ini)
#> ntheta neta1 neta2 name lower est upper fix label backTransform
#> 1 1 NA NA tka -Inf 0.45 Inf FALSE <NA> <NA>
#> 2 2 NA NA tcl -Inf 1.00 Inf FALSE <NA> <NA>
#> 3 3 NA NA tv -Inf 3.45 Inf FALSE <NA> <NA>
#> 4 4 NA NA add.sd 0 0.70 Inf FALSE <NA> <NA>
#> 5 NA 1 1 eta.ka -Inf 0.60 Inf FALSE <NA> <NA>
#> 6 NA 2 2 eta.v -Inf 0.10 Inf FALSE <NA> <NA>
#> condition err
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 <NA> <NA>
#> 4 cp add
#> 5 id <NA>
#> 6 id <NA>
ini$est[1] <- 7
ini(f) <- ini
print(f)
#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> tka tcl tv add.sd
#> 7.00 1.00 3.45 0.70
#>
#> Omega ($omega):
#> eta.ka eta.v
#> eta.ka 0.6 0.0
#> eta.v 0.0 0.1
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 center
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 tka eta.ka id
#> 2 tv eta.v id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> ini({
#> tka <- 7
#> tcl <- 1
#> tv <- 3.45
#> add.sd <- c(0, 0.7)
#> eta.ka ~ 0.6
#> eta.v ~ 0.1
#> })
#> model({
#> ka <- exp(tka + eta.ka)
#> cl <- exp(tcl)
#> v <- exp(tv + eta.v)
#> d/dt(depot) <- -ka * depot
#> d/dt(center) <- ka * depot - cl/v * center
#> cp <- center/v
#> cp ~ add(add.sd)
#> })
#> }