Type: Package
Title: Use 'nlmixr2' to Interact with Open Source and Commercial Software
Version: 0.1.8
Description: Run other estimation and simulation software via the 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including 'PKNCA', 'NONMEM' and 'Monolix'. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization. When 'lixoftConnectors' is available, 'Monolix' can be run directly instead of setting up command line usage.
License: GPL (≥ 3)
URL: https://nlmixr2.github.io/babelmixr2/, https://github.com/nlmixr2/babelmixr2/
NeedsCompilation: yes
Encoding: UTF-8
Suggests: testthat, withr, lixoftConnectors, PKNCA (≥ 0.10.0), rmarkdown, spelling, PopED, units (≥ 0.8-6), vdiffr, nlme, dplyr, devtools, memoise
Depends: R (≥ 3.5)
Imports: checkmate, cli, digest, lotri, nlmixr2data, nlmixr2extra, nlmixr2plot, magrittr, nlmixr2est (≥ 3.0.1), nonmem2rx (≥ 0.1.5), monolix2rx (≥ 0.0.3), methods, qs, rex, rxode2 (≥ 3.0.2)
RoxygenNote: 7.3.2
Config/testthat/edition: 3
LinkingTo: Rcpp, rxode2, RcppArmadillo, RcppEigen
Language: en-US
Packaged: 2025-07-15 04:07:53 UTC; matt
Author: Matthew Fidler ORCID iD [aut, cre], Bill Denney ORCID iD [aut], Theodoros Papathanasiou [ctb], Nook Fulloption [ctb] (goldfish art)
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>
Repository: CRAN
Date/Publication: 2025-07-15 08:10:07 UTC

Internal function to use with PopED to run PopED in parallel on Windows

Description

Internal function to use with PopED to run PopED in parallel on Windows

Usage

.popedCluster(babelmixr2)

Arguments

babelmixr2

environment in poped environment

Value

nothing, called for side effects

Author(s)

Matthew L. Fidler

Get the function value from the rxode2 solve

Description

This shouldn't be called directly

Usage

.popedF(popedDb)

Arguments

popedDb

poped DB with babelmixr2 issue

Value

rxode2 weights for the poped error function

Author(s)

Matthew L. Fidler

Free Poped memory (if any is allocated)

Description

This should not be called directly but is used in babelmixr2's poped interface

Usage

.popedFree()

Value

nothing, called for side effects

Author(s)

Matthew L. Fidler

Setup poped if needed

Description

Should not be called by user

Usage

.popedRxRunSetup(popedDb)

Value

nothing, called for side effects

Author(s)

Matthew L. Fidler

Setup the PopED environment

Description

This should not typically be called directly

Usage

.popedSetup(e, eglobal, full = FALSE)

Arguments

e

environment with setup information for popEd

eglobal

global environment for poped info

full

setup the full model

Value

nothing, called for side effects

Author(s)

Matthew L. Fidler

Solve poped problem for appropriate times with single/multiple endpoint models

Description

This really should not be called directly (if not setup correctly can crash R)

Usage

.popedSolveIdME(theta, umt, mt, ms, nend, id, totn)

.popedSolveIdME2(theta, umt, mt, ms, nend, id, totn)

Arguments

theta

parameters (includes covariates and modeling times)

umt

unique times sampled

mt

original unsorted time (to match the f/w against)

ms

model switch parameter integer starting with 1 (related to dvid in rxode2)

nend

specifies the number of endpoints in this model

id

this is the design identifier

totn

This is the total number of design points tested

Value

a data frame with $f and $w corresponding to the function value and standard deviation at the sampling point

Author(s)

Matthew L. Fidler

Get the weight from the rxode2 solve

Description

This shouldn't be called directly

Usage

.popedW(popedDb)

Arguments

popedDb

poped DB with babelmixr2 issue

Value

rxode2 weights for the poped error function

Author(s)

Matthew L. Fidler

Setup the poped database

Description

Setup the poped database

Usage

.setupPopEDdatabase(ui, data, control)

Arguments

ui

rxode2 ui function

data

babelmixr2 design data

control

PopED control

Value

PopED database

Author(s)

Matthew L. Fidler

Convert an object to a nlmixr2 fit object

Description

Convert an object to a nlmixr2 fit object

Usage

as.nlmixr2(
  x,
  ...,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  ci = 0.95
)

as.nlmixr(
  x,
  ...,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  ci = 0.95
)

Arguments

x

Object to convert

...

Other arguments

table

is the nlmixr2est::tableControl() options

rxControl

is the rxode2::rxControl() options, which is generally needed for how addl doses are handled in the translation

ci

is the confidence interval of the residual differences calculated (by default 0.95)

Value

nlmixr2 fit object

Author(s)

Matthew L. Fidler

Examples




# First read in the model (but without residuals)
mod <- nonmem2rx(system.file("mods/cpt/runODE032.ctl", package="nonmem2rx"),
                 determineError=FALSE, lst=".res", save=FALSE)

# define the model with residuals (and change the name of the
# parameters) In this step you need to be careful to not change the
# estimates and make sure the residual estimates are correct (could
# have to change var to sd).

 mod2 <-function() {
   ini({
     lcl <- 1.37034036528946
     lvc <- 4.19814911033061
     lq <- 1.38003493562413
     lvp <- 3.87657341967489
     RSV <- c(0, 0.196446108190896, 1)
     eta.cl ~ 0.101251418415006
     eta.v ~ 0.0993872449483344
     eta.q ~ 0.101302674763154
     eta.v2 ~ 0.0730497519364148
   })
   model({
     cmt(CENTRAL)
     cmt(PERI)
     cl <- exp(lcl + eta.cl)
     v <- exp(lvc + eta.v)
     q <- exp(lq + eta.q)
     v2 <- exp(lvp + eta.v2)
     v1 <- v
     scale1 <- v
     k21 <- q/v2
     k12 <- q/v
     d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1
     d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL
     f <- CENTRAL/scale1
     f ~ prop(RSV)
   })
 }

# now we create another nonmem2rx object that validates the model above:

new <- as.nonmem2rx(mod2, mod)

# once that is done, you can translate to a full nlmixr2 fit (if you wish)

fit <- as.nlmixr2(new)

print(fit)

Expand a babelmixr2 PopED database

Description

Expand a babelmixr2 PopED database

Usage

babel.poped.database(popedInput, ..., optTime = NA)

Arguments

popedInput

The babelmixr2 generated PopED database

...

other parameters sent to PopED::create.poped.database()

optTime

boolean to indicate if the global time indexer inside of babelmixr2 is reset if the times are different. By default this is TRUE. If FALSE you can get slightly better run times and possibly slightly different results. When optTime is FALSE the global indexer is reset every time the PopED rxode2 is setup for a problem or when a poped dataset is created. You can manually reset with popedMultipleEndpointResetTimeIndex()

Value

babelmixr2 PopED database (with $babelmixr2 in database)

Author(s)

Matthew L. Fidler

Get the bpop_idx by variable name for a poped database created by babelmixr2

Description

This may work for other poped databases if the population parameters are named.

Usage

babelBpopIdx(popedInput, var)

Arguments

popedInput

The babelmixr2 created database

var

variable to query

Value

index of the variable

Author(s)

Matthew L. Fidler

Examples


if (requireNamespace("PopED", quietly=TRUE)) {

f <- function() {
  ini({
    tV <- 72.8
    tKa <- 0.25
    tCl <- 3.75
    tF <- fix(0.9)
    pedCL <- 0.8

    eta.v ~ 0.09
    eta.ka ~ 0.09
    eta.cl ~0.25^2

    prop.sd <- fix(sqrt(0.04))
    add.sd <- fix(sqrt(5e-6))

  })
  model({
    V<-tV*exp(eta.v)
    KA<-tKa*exp(eta.ka) * (pedCL**isPediatric) # add covariate for pediatrics
    CL<-tCl*exp(eta.cl)
    Favail <- tF

    N <-  floor(t/TAU)+1
    y <- (DOSE*Favail/V)*(KA/(KA - CL/V)) *
      (exp(-CL/V * (t - (N - 1) * TAU)) *
         (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) -
         exp(-KA * (t - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))

    y ~ prop(prop.sd) + add(add.sd)
  })
}

e <- et(c( 1,8,10,240,245))

babel.db <- nlmixr2(f, e, "poped",
                    popedControl(m = 2,
                                 groupsize=20,
                                 bUseGrouped_xt=TRUE,
                                 a=list(c(DOSE=20,TAU=24,isPediatric = 0),
                                        c(DOSE=40, TAU=24,isPediatric = 0))))

babelBpopIdx(babel.db, "pedCL")

}

Convert nlmixr2-compatible data to other formats (if possible)

Description

Convert nlmixr2-compatible data to other formats (if possible)

Usage

bblDatToMonolix(
  model,
  data,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  env = NULL
)

bblDatToNonmem(
  model,
  data,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  env = NULL
)

bblDatToRxode(
  model,
  data,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  env = NULL
)

bblDatToMrgsolve(
  model,
  data,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  env = NULL
)

bblDatToPknca(
  model,
  data,
  table = nlmixr2est::tableControl(),
  rxControl = rxode2::rxControl(),
  env = NULL
)

Arguments

model

rxode2 model for conversion

data

Input dataset.

table

is the table control; this is mostly to figure out if there are additional columns to keep.

rxControl

is the rxode2 control options; This is to figure out how to handle the addl dosing information.

env

When NULL (default) nothing is done. When an environment, the function nlmixr2est::.foceiPreProcessData(data, env, model, rxControl) is called on the provided environment.

Value

With the function bblDatToMonolix() return a list with:

With the function nlmixrDataToNonmem() return a dataset that is compatible with NONMEM.

With the function nlmixrDataToMrgsolve() return a dataset that is compatible with mrgsolve. Unlike NONMEM, it supports replacement events with evid=8 (note with rxode2 replacement evid is 5).

With the function nlmixrDataToRxode() this will normalize the dataset to use newer evid definitions that are closer to NONMEM instead of any classic definitions that are used at a lower level

Author(s)

Matthew L. Fidler

Examples


pk.turnover.emax3 <- function() {
  ini({
    tktr <- log(1)
    tka <- log(1)
    tcl <- log(0.1)
    tv <- log(10)
    ##
    eta.ktr ~ 1
    eta.ka ~ 1
    eta.cl ~ 2
    eta.v ~ 1
    prop.err <- 0.1
    pkadd.err <- 0.1
    ##
    temax <- logit(0.8)
    tec50 <- log(0.5)
    tkout <- log(0.05)
    te0 <- log(100)
    ##
    eta.emax ~ .5
    eta.ec50  ~ .5
    eta.kout ~ .5
    eta.e0 ~ .5
    ##
    pdadd.err <- 10
  })
  model({
    ktr <- exp(tktr + eta.ktr)
    ka <- exp(tka + eta.ka)
    cl <- exp(tcl + eta.cl)
    v <- exp(tv + eta.v)
    emax = expit(temax+eta.emax)
    ec50 =  exp(tec50 + eta.ec50)
    kout = exp(tkout + eta.kout)
    e0 = exp(te0 + eta.e0)
    ##
    DCP = center/v
    PD=1-emax*DCP/(ec50+DCP)
    ##
    effect(0) = e0
    kin = e0*kout
    ##
    d/dt(depot) = -ktr * depot
    d/dt(gut) =  ktr * depot -ka * gut
    d/dt(center) =  ka * gut - cl / v * center
    d/dt(effect) = kin*PD -kout*effect
    ##
    cp = center / v
    cp ~ prop(prop.err) + add(pkadd.err)
    effect ~ add(pdadd.err) | pca
  })
}

bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin)

bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin)

bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin)

bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin)

Determine standardized rxode2 column names from data

Description

Determine standardized rxode2 column names from data

Usage

getStandardColNames(data)

Arguments

data

A data.frame as the source for column names

Value

A named character vector where the names are the standardized names and the values are either the name of the column from the data or NA if the column is not present in the data.

Examples

getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4))

Unit conversion for pharmacokinetic models

Description

Unit conversion for pharmacokinetic models

Usage

modelUnitConversion(
  dvu = NA_character_,
  amtu = NA_character_,
  timeu = NA_character_,
  volumeu = NA_character_
)

Arguments

dvu, amtu, timeu

The units for the DV, AMT, and TIME columns in the data

volumeu

The units for the volume parameters in the model

Value

A list with names for the units associated with each parameter ("amtu", "clearanceu", "volumeu", "timeu", "dvu") and the numeric value to multiply the modeled estimate (for example, cp) so that the model is consistent with the data units.

See Also

Other Unit conversion: simplifyUnit()

Examples

modelUnitConversion(dvu = "ng/mL", amtu = "mg", timeu = "hr", volumeu = "L")

Monolix Controller for nlmixr2

Description

Monolix Controller for nlmixr2

Usage

monolixControl(
  nbSSDoses = 7,
  useLinearization = FALSE,
  stiff = FALSE,
  addProp = c("combined2", "combined1"),
  exploratoryAutoStop = FALSE,
  smoothingAutoStop = FALSE,
  burnInIterations = 5,
  smoothingIterations = 200,
  exploratoryIterations = 250,
  simulatedAnnealingIterations = 250,
  exploratoryInterval = 200,
  exploratoryAlpha = 0,
  omegaTau = 0.95,
  errorModelTau = 0.95,
  variability = c("none", "firstStage", "decreasing"),
  runCommand = getOption("babelmixr2.monolix", ""),
  rxControl = NULL,
  sumProd = FALSE,
  optExpression = TRUE,
  calcTables = TRUE,
  compress = TRUE,
  ci = 0.95,
  sigdigTable = NULL,
  absolutePath = FALSE,
  modelName = NULL,
  muRefCovAlg = TRUE,
  run = TRUE,
  ...
)

Arguments

nbSSDoses

Number of steady state doses (default 7)

useLinearization

Use linearization for log likelihood and fim.

stiff

boolean for using the stiff ODE solver

addProp

specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2).

The combined1 error type can be described by the following equation:

y = f + (a + b\times f^c) \times \varepsilon

The combined2 error model can be described by the following equation:

y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon

Where:

- y represents the observed value

- f represents the predicted value

- a is the additive standard deviation

- b is the proportional/power standard deviation

- c is the power exponent (in the proportional case c=1)

exploratoryAutoStop

logical to turn on or off exploratory phase auto-stop of SAEM (default 250)

smoothingAutoStop

Boolean indicating if the smoothing should automatically stop (default FALSE)

burnInIterations

Number of burn in iterations

smoothingIterations

Number of smoothing iterations

exploratoryIterations

Number of iterations for exploratory phase (default 250)

simulatedAnnealingIterations

Number of simulating annealing iterations

exploratoryInterval

Minimum number of iterations in the exploratory phase (default 200)

exploratoryAlpha

Convergence memory in the exploratory phase (only used when exploratoryAutoStop is TRUE)

omegaTau

Proportional rate on variance for simulated annealing

errorModelTau

Proportional rate on error model for simulated annealing

variability

This describes the methodology for parameters without variability. It could be: - Fixed throughout (none) - Variability in the first stage (firstStage) - Decreasing until it reaches the fixed value (decreasing)

runCommand

is a shell command or function to run monolix; You can specify the default by options("babelmixr2.monolix"="runMonolix"). If it is empty and 'lixoftConnectors' is available, use lixoftConnectors to run monolix. See details for function usage.

rxControl

'rxode2' ODE solving options during fitting, created with 'rxControl()'

sumProd

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

optExpression

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

calcTables

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

compress

Should the object have compressed items

ci

Confidence level for some tables. By default this is 0.95 or 95% confidence.

sigdigTable

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

absolutePath

Boolean indicating if the absolute path should be used for the monolix runs

modelName

Model name used to generate the NONMEM output. If NULL try to infer from the model name (could be x if not clear). Otherwise use this character for outputs.

muRefCovAlg

This controls if algebraic expressions that can be mu-referenced are treated as mu-referenced covariates by:

1. Creating a internal data-variable 'nlmixrMuDerCov#' for each algebraic mu-referenced expression

2. Change the algebraic expression to 'nlmixrMuDerCov# * mu_cov_theta'

3. Use the internal mu-referenced covariate for saem

4. After optimization is completed, replace 'model()' with old 'model()' expression

5. Remove 'nlmixrMuDerCov#' from nlmix2 output

In general, these covariates should be more accurate since it changes the system to a linear compartment model. Therefore, by default this is 'TRUE'.

run

Should monolix be run and the results be imported to nlmixr2? (Default is TRUE)

...

Ignored parameters

Details

If runCommand is given as a string, it will be called with the system() command like:

runCommand mlxtran.

For example, if runCommand="'/path/to/monolix/mlxbsub2021' -p " then the command line used would look like the following:

'/path/to/monolix/mlxbsub2021' monolix.mlxtran

If runCommand is given as a function, it will be called as FUN(mlxtran, directory, ui) to run Monolix. This allows you to run Monolix in any way that you may need, as long as you can write it in R. babelmixr2 will wait for the function to return before proceeding.

If runCommand is NA, nlmixr() will stop after writing the model files and without starting Monolix.

Note that you can get the translated monolix components from a parsed/compiled rxode2 ui object with ui$monolixModel and ui$mlxtran

Value

A monolix control object

Author(s)

Matthew Fidler

Estimate starting parameters using PKNCA

Description

Estimate starting parameters using PKNCA

Usage

## S3 method for class 'pknca'
nlmixr2Est(env, ...)

Arguments

env

Environment for the nlmixr2 estimation routines.

This needs to have:

- rxode2 ui object in '$ui'

- data to fit in the estimation routine in '$data'

- control for the estimation routine's control options in '$ui'

...

Other arguments provided to 'nlmixr2Est()' provided for flexibility but not currently used inside nlmixr

Details

Parameters are estimated as follows:

The bounds for the parameter estimates are set to 10% of the first percentile and 10 times the 99th percentile. (For ka, the lower bound is set to the lower of 10% of the first percentile or 0.03 and the upper bound is not modified from 10 times the 99th percentile.)

Parameter estimation methods may be changed in a future version.

Value

A model with updated starting parameters. In the model a new element named "nca" will be available which includes the PKNCA results used for the calculation.

This is a S3 method for getting the distribution lines for a base rxode2 saem problem

Description

This is a S3 method for getting the distribution lines for a base rxode2 saem problem

Usage

nmGetDistributionMonolixLines(line)

## S3 method for class 'rxUi'
nmGetDistributionMonolixLines(line)

## S3 method for class 'norm'
nmGetDistributionMonolixLines(line)

Arguments

line

Parsed rxode2 model environment

Value

Lines for the estimation of monolix

Author(s)

Matthew Fidler

This is a S3 method for getting the distribution lines for a base rxode2 saem problem

Description

This is a S3 method for getting the distribution lines for a base rxode2 saem problem

Usage

nmGetDistributionNonmemLines(line)

## S3 method for class 'rxUi'
nmGetDistributionNonmemLines(line)

## S3 method for class 'norm'
nmGetDistributionNonmemLines(line)

Arguments

line

Parsed rxode2 model environment

Value

Lines for the estimation of nonmem

Author(s)

Matthew Fidler

NONMEM estimation control

Description

NONMEM estimation control

Usage

nonmemControl(
  est = c("focei", "imp", "its", "posthoc"),
  advanOde = c("advan13", "advan8", "advan6"),
  cov = c("r,s", "r", "s", ""),
  maxeval = 1e+05,
  tol = 6,
  atol = 12,
  sstol = 6,
  ssatol = 12,
  sigl = 12,
  sigdig = 3,
  print = 1,
  extension = getOption("babelmixr2.nmModelExtension", ".nmctl"),
  outputExtension = getOption("babelmixr2.nmOutputExtension", ".lst"),
  runCommand = getOption("babelmixr2.nonmem", ""),
  iniSigDig = 5,
  protectZeros = FALSE,
  muRef = TRUE,
  addProp = c("combined2", "combined1"),
  rxControl = NULL,
  sumProd = FALSE,
  optExpression = TRUE,
  calcTables = TRUE,
  compress = TRUE,
  ci = 0.95,
  sigdigTable = NULL,
  readRounding = FALSE,
  readBadOpt = FALSE,
  niter = 100L,
  isample = 1000L,
  iaccept = 0.4,
  iscaleMin = 0.1,
  iscaleMax = 10,
  df = 4,
  seed = 14456,
  mapiter = 1,
  mapinter = 0,
  noabort = TRUE,
  modelName = NULL,
  muRefCovAlg = TRUE,
  run = TRUE,
  ...
)

Arguments

est

NONMEM estimation method

advanOde

The ODE solving method for NONMEM

cov

The NONMEM covariance method

maxeval

NONMEM's maxeval (for non posthoc methods)

tol

NONMEM tolerance for ODE solving advan

atol

NONMEM absolute tolerance for ODE solving

sstol

NONMEM tolerance for steady state ODE solving

ssatol

NONMEM absolute tolerance for steady state ODE solving

sigl

NONMEM sigl estimation option

sigdig

the significant digits for NONMEM

print

The print number for NONMEM

extension

NONMEM file extensions

outputExtension

Extension to use for the NONMEM output listing

runCommand

Command to run NONMEM (typically the path to "nmfe75") or a function. See the details for more information.

iniSigDig

How many significant digits are printed in $THETA and $OMEGA when the estimate is zero. Also controls the zero protection numbers

protectZeros

Add methods to protect divide by zero

muRef

Automatically mu-reference the control stream

addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable

Passed to nlmixr2est::foceiControl

rxControl

Options to pass to rxode2::rxControl for simulations

readRounding

Try to read NONMEM output when NONMEM terminated due to rounding errors

readBadOpt

Try to read NONMEM output when NONMEM terminated due to an apparent failed optimization

niter

number of iterations in NONMEM estimation methods

isample

Isample argument for NONMEM ITS estimation method

iaccept

Iaccept for NONMEM ITS estimation methods

iscaleMin

parameter for IMP NONMEM method (ISCALE_MIN)

iscaleMax

parameter for IMP NONMEM method (ISCALE_MAX)

df

degrees of freedom for IMP method

seed

is the seed for NONMEM methods

mapiter

the number of map iterations for IMP method

mapinter

is the MAPINTER parameter for the IMP method

noabort

Add the NOABORT option for ⁠$EST⁠

modelName

Model name used to generate the NONMEM output. If NULL try to infer from the model name (could be x if not clear). Otherwise use this character for outputs.

muRefCovAlg

This controls if algebraic expressions that can be mu-referenced are treated as mu-referenced covariates by:

1. Creating a internal data-variable 'nlmixrMuDerCov#' for each algebraic mu-referenced expression

2. Change the algebraic expression to 'nlmixrMuDerCov# * mu_cov_theta'

3. Use the internal mu-referenced covariate for saem

4. After optimization is completed, replace 'model()' with old 'model()' expression

5. Remove 'nlmixrMuDerCov#' from nlmix2 output

In general, these covariates should be more accurate since it changes the system to a linear compartment model. Therefore, by default this is 'TRUE'.

run

Should NONMEM be run (and the files imported to nlmixr2); default is TRUE, but FALSE will simply create the NONMEM control stream and data file.

...

optional genRxControl argument controlling automatic rxControl generation.

Details

If runCommand is given as a string, it will be called with the system() command like:

runCommand controlFile outputFile.

For example, if runCommand="'/path/to/nmfe75'" then the command line used would look like the following:

'/path/to/nmfe75' one.cmt.nmctl one.cmt.lst

If runCommand is given as a function, it will be called as FUN(ctl, directory, ui) to run NONMEM. This allows you to run NONMEM in any way that you may need, as long as you can write it in R. babelmixr2 will wait for the function to return before proceeding.

If runCommand is NA, nlmixr() will stop after writing the model files and without starting NONMEM.

Value

babelmixr2 control option for generating NONMEM control stream and reading it back into babelmixr2/nlmixr2

Author(s)

Matthew L. Fidler

Examples


nonmemControl()

PKNCA estimation control

Description

PKNCA estimation control

Usage

pkncaControl(
  concu = NA_character_,
  doseu = NA_character_,
  timeu = NA_character_,
  volumeu = NA_character_,
  vpMult = 2,
  qMult = 1/2,
  vp2Mult = 4,
  q2Mult = 1/4,
  dvParam = "cp",
  groups = character(),
  sparse = FALSE,
  ncaData = NULL,
  ncaResults = NULL,
  rxControl = rxode2::rxControl()
)

Arguments

concu, doseu, timeu

concentration, dose, and time units from the source data (passed to PKNCA::pknca_units_table()).

volumeu

compartment volume for the model (if NULL, simplified units from source data will be used)

vpMult, qMult, vp2Mult, q2Mult

Multipliers for vc and cl to provide initial estimates for vp, q, vp2, and q2

dvParam

The parameter name in the model that should be modified for concentration unit conversions. It must be assigned on a line by itself, separate from the residual error model line.

groups

Grouping columns for NCA summaries by group (required if sparse = TRUE)

sparse

Are the concentration-time data sparse PK (commonly used in small nonclinical species or with terminal or difficult sampling) or dense PK (commonly used in clinical studies or larger nonclinical species)?

ncaData

Data to use for calculating NCA parameters. Typical use is when a subset of the original data are informative for NCA.

ncaResults

Already computed NCA results (a PKNCAresults object) to bypass automatic calculations. At least the following parameters must be calculated in the NCA: tmax, cmax.dn, cl.last

rxControl

Control options sent to rxode2::rxControl()

Value

A list of parameters

Control for a PopED design task

Description

Control for a PopED design task

Usage

popedControl(
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  maxn = NULL,
  rxControl = NULL,
  sigdig = 4,
  important = NULL,
  unimportant = NULL,
  iFIMCalculationType = c("reduced", "full", "weighted", "loc", "reducedPFIM", "fullABC",
    "largeMat", "reducedFIMABC"),
  iApproximationMethod = c("fo", "foce", "focei", "foi"),
  iFOCENumInd = 1000,
  prior_fim = matrix(0, 0, 1),
  d_switch = c("d", "ed"),
  ofv_calc_type = c("lnD", "d", "a", "Ds", "inverse"),
  strEDPenaltyFile = "",
  ofv_fun = NULL,
  iEDCalculationType = c("mc", "laplace", "bfgs-laplace"),
  ED_samp_size = 45,
  bLHS = c("hypercube", "random"),
  bUseRandomSearch = TRUE,
  bUseStochasticGradient = TRUE,
  bUseLineSearch = TRUE,
  bUseExchangeAlgorithm = FALSE,
  bUseBFGSMinimizer = FALSE,
  bUseGrouped_xt = FALSE,
  EACriteria = c("modified", "fedorov"),
  strRunFile = "",
  poped_version = NULL,
  modtit = "PopED babelmixr2 model",
  output_file = "PopED_output_summary",
  output_function_file = "PopED_output_",
  strIterationFileName = "PopED_current.R",
  user_data = NULL,
  ourzero = 1e-05,
  dSeed = NULL,
  line_opta = NULL,
  line_optx = NULL,
  bShowGraphs = FALSE,
  use_logfile = FALSE,
  m1_switch = c("central", "complex", "analytic", "ad"),
  m2_switch = c("central", "complex", "analytic", "ad"),
  hle_switch = c("central", "complex", "ad"),
  gradff_switch = c("central", "complex", "analytic", "ad"),
  gradfg_switch = c("central", "complex", "analytic", "ad"),
  grad_all_switch = c("central", "complex"),
  rsit_output = 5,
  sgit_output = 1,
  hm1 = 1e-05,
  hlf = 1e-05,
  hlg = 1e-05,
  hm2 = 1e-05,
  hgd = 1e-05,
  hle = 1e-05,
  AbsTol = 1e-06,
  RelTol = 1e-06,
  iDiffSolverMethod = NULL,
  bUseMemorySolver = FALSE,
  rsit = 300,
  sgit = 150,
  intrsit = 250,
  intsgit = 50,
  maxrsnullit = 50,
  convergence_eps = 1e-08,
  rslxt = 10,
  rsla = 10,
  cfaxt = 0.001,
  cfaa = 0.001,
  bGreedyGroupOpt = FALSE,
  EAStepSize = 0.01,
  EANumPoints = FALSE,
  EAConvergenceCriteria = 1e-20,
  bEANoReplicates = FALSE,
  BFGSProjectedGradientTol = 1e-04,
  BFGSTolerancef = 0.001,
  BFGSToleranceg = 0.9,
  BFGSTolerancex = 0.1,
  ED_diff_it = 30,
  ED_diff_percent = 10,
  line_search_it = 50,
  Doptim_iter = 1,
  iCompileOption = c("none", "full", "mcc", "mpi"),
  compileOnly = FALSE,
  iUseParallelMethod = c("mpi", "matlab"),
  MCC_Dep = NULL,
  strExecuteName = "calc_fim.exe",
  iNumProcesses = 2,
  iNumChunkDesignEvals = -2,
  Mat_Out_Pre = "parallel_output",
  strExtraRunOptions = "",
  dPollResultTime = 0.1,
  strFunctionInputName = "function_input",
  bParallelRS = FALSE,
  bParallelSG = FALSE,
  bParallelMFEA = FALSE,
  bParallelLS = FALSE,
  groupsize = NULL,
  time = "time",
  timeLow = "low",
  timeHi = "high",
  id = "id",
  m = NULL,
  x = NULL,
  ni = NULL,
  maxni = NULL,
  minni = NULL,
  maxtotni = NULL,
  mintotni = NULL,
  maxgroupsize = NULL,
  mingroupsize = NULL,
  maxtotgroupsize = NULL,
  mintotgroupsize = NULL,
  xt_space = NULL,
  a = NULL,
  maxa = NULL,
  mina = NULL,
  a_space = NULL,
  x_space = NULL,
  use_grouped_xt = FALSE,
  grouped_xt = NULL,
  use_grouped_a = FALSE,
  grouped_a = NULL,
  use_grouped_x = FALSE,
  grouped_x = NULL,
  our_zero = NULL,
  auto_pointer = "",
  user_distribution_pointer = "",
  minxt = NULL,
  maxxt = NULL,
  discrete_xt = NULL,
  discrete_a = NULL,
  fixRes = FALSE,
  script = NULL,
  overwrite = TRUE,
  literalFix = TRUE,
  opt_xt = FALSE,
  opt_a = FALSE,
  opt_x = FALSE,
  opt_samps = FALSE,
  optTime = TRUE,
  literalFixRes = FALSE,
  ...
)

Arguments

stickyRecalcN

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

maxOdeRecalc

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

odeRecalcFactor

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

maxn

Maximum number of design points for optimization; By default this is declared by the maximum number of design points in the babelmixr2 dataset (when NULL)

rxControl

'rxode2' ODE solving options during fitting, created with 'rxControl()'

sigdig

Optimization significant digits. This controls:

  • The tolerance of the inner and outer optimization is 10^-sigdig

  • The tolerance of the ODE solvers is 0.5*10^(-sigdig-2); For the sensitivity equations and steady-state solutions the default is 0.5*10^(-sigdig-1.5) (sensitivity changes only applicable for liblsoda)

  • The tolerance of the boundary check is 5 * 10 ^ (-sigdig + 1)

important

character vector of important parameters or NULL for default. This is used with Ds-optimality

unimportant

character vector of unimportant parameters or NULL for default. This is used with Ds-optimality

iFIMCalculationType

can be either an integer or a named value of the Fisher Information Matrix type:

  • 0/"full" = Full FIM

  • 1/"reduced" = Reduced FIM

  • 2/"weighted" = weighted models

  • 3/"loc" = Loc models

  • 4/"reducedPFIM" = reduced FIM with derivative of SD of sigma as in PFIM

  • 5/"fullABC" = FULL FIM parameterized with A,B,C matrices & derivative of variance

  • 6/"largeMat" = Calculate one model switch at a time, good for large matrices

  • 7/"reducedFIMABC" = =Reduced FIM parameterized with A,B,C matrices & derivative of variance

iApproximationMethod

Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI

iFOCENumInd

integer; number of individuals in focei solve

prior_fim

matrix; prior FIM

d_switch

integer or character option:

  • 0/"ed" = ED design

  • 1/"d" = D design

ofv_calc_type

objective calculation type:

  • 1/"d" = D-optimality". Determinant of the FIM: det(FIM)

  • 2/"a" = "A-optimality". Inverse of the sum of the expected parameter variances: 1/trace_matrix(inv(FIM))

  • 4/"lnD" = "lnD-optimality". Natural logarithm of the determinant of the FIM: log(det(FIM))

  • 6/"Ds" = "Ds-optimality". Ratio of the Determinant of the FIM and the Determinant of the uninteresting rows and columns of the FIM: det(FIM)/det(FIM_u)

  • 7/"inverse" = Inverse of the sum of the expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))

strEDPenaltyFile

Penalty function name or path and filename, empty string means no penalty. User defined criterion can be defined this way.

ofv_fun

User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it.

iEDCalculationType

ED Integral Calculation type:

  • 0/"mc" = Monte-Carlo-Integration

  • 1/"laplace" = Laplace Approximation

  • 2/"bfgs-laplace" = BFGS Laplace Approximation

ED_samp_size

Sample size for E-family sampling

bLHS

How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube –

bUseRandomSearch

  • ******START OF Optimization algorithm SPECIFICATION OPTIONS**********

Use random search (1=TRUE, 0=FALSE)

bUseStochasticGradient

Use Stochastic Gradient search (1=TRUE, 0=FALSE)

bUseLineSearch

Use Line search (1=TRUE, 0=FALSE)

bUseExchangeAlgorithm

Use Exchange algorithm (1=TRUE, 0=FALSE)

bUseBFGSMinimizer

Use BFGS Minimizer (1=TRUE, 0=FALSE)

bUseGrouped_xt

Use grouped time points (1=TRUE, 0=FALSE).

EACriteria

Exchange Algorithm Criteria:

  • 1/"modified" = Modified

  • 2/"fedorov" = Fedorov

strRunFile

Filename and path, or function name, for a run file that is used instead of the regular PopED call.

poped_version

  • ******START OF Labeling and file names SPECIFICATION OPTIONS**********

The current PopED version

modtit

The model title

output_file

Filename and path of the output file during search

output_function_file

Filename suffix of the result function file

strIterationFileName

Filename and path for storage of current optimal design

user_data

  • ******START OF Miscellaneous SPECIFICATION OPTIONS**********

User defined data structure that, for example could be used to send in data to the model

ourzero

Value to interpret as zero in design

dSeed

The seed number used for optimization and sampling – integer or -1 which creates a random seed as.integer(Sys.time()) or NULL.

line_opta

Vector for line search on continuous design variables (1=TRUE,0=FALSE)

line_optx

Vector for line search on discrete design variables (1=TRUE,0=FALSE)

bShowGraphs

Use graph output during search

use_logfile

If a log file should be used (0=FALSE, 1=TRUE)

m1_switch

Method used to calculate M1:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

m2_switch

Method used to calculate M2:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

hle_switch

Method used to calculate linearization of residual error:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 30/"ad" = Automatic differentiation

gradff_switch

Method used to calculate the gradient of the model:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

gradfg_switch

Method used to calculate the gradient of the parameter vector g:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

grad_all_switch

Method used to calculate all the gradients:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

rsit_output

Number of iterations in random search between screen output

sgit_output

Number of iterations in stochastic gradient search between screen output

hm1

Step length of derivative of linearized model w.r.t. typical values

hlf

Step length of derivative of model w.r.t. g

hlg

Step length of derivative of g w.r.t. b

hm2

Step length of derivative of variance w.r.t. typical values

hgd

Step length of derivative of OFV w.r.t. time

hle

Step length of derivative of model w.r.t. sigma

AbsTol

The absolute tolerance for the diff equation solver

RelTol

The relative tolerance for the diff equation solver

iDiffSolverMethod

The diff equation solver method, NULL as default.

bUseMemorySolver

If the differential equation results should be stored in memory (1) or not (0)

rsit

Number of Random search iterations

sgit

Number of stochastic gradient iterations

intrsit

Number of Random search iterations with discrete optimization.

intsgit

Number of Stochastic Gradient search iterations with discrete optimization

maxrsnullit

Iterations until adaptive narrowing in random search

convergence_eps

Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal)

rslxt

Random search locality factor for sample times

rsla

Random search locality factor for covariates

cfaxt

Stochastic Gradient search first step factor for sample times

cfaa

Stochastic Gradient search first step factor for covariates

bGreedyGroupOpt

Use greedy algorithm for group assignment optimization

EAStepSize

Exchange Algorithm StepSize

EANumPoints

Exchange Algorithm NumPoints

EAConvergenceCriteria

Exchange Algorithm Convergence Limit/Criteria

bEANoReplicates

Avoid replicate samples when using Exchange Algorithm

BFGSProjectedGradientTol

BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance

BFGSTolerancef

BFGS Minimizer Line Search Tolerance f

BFGSToleranceg

BFGS Minimizer Line Search Tolerance g

BFGSTolerancex

BFGS Minimizer Line Search Tolerance x

ED_diff_it

Number of iterations in ED-optimal design to calculate convergence criteria

ED_diff_percent

ED-optimal design convergence criteria in percent

line_search_it

Number of grid points in the line search

Doptim_iter

Number of iterations of full Random search and full Stochastic Gradient if line search is not used

iCompileOption

Compile options for PopED

  • "none"/-1 = No compilation

  • "full/0 or 3 = Full compilation

  • "mcc"/1 or 4 = Only using MCC (shared lib)

  • "mpi"/2 or 5 = Only MPI,

When using numbers, option 0,1,2 runs PopED and option 3,4,5 stops after compilation.

When using characters, the option compileOnly determines if the model is only compiled (and PopED is not run).

compileOnly

logical; only compile the model, do not run PopED (in conjunction with iCompileOption)

iUseParallelMethod

Parallel method to use

  • 0/"matlab"= Matlab PCT

  • 1/"mpi" = MPI

MCC_Dep

Additional dependencies used in MCC compilation (mat-files), if several space separated

strExecuteName

Compilation output executable name

iNumProcesses

Number of processes to use when running in parallel (e.g. 3 = 2 workers, 1 job manager)

iNumChunkDesignEvals

Number of design evaluations that should be evaluated in each process before getting new work from job manager

Mat_Out_Pre

The prefix of the output mat file to communicate with the executable

strExtraRunOptions

Extra options send to e$g. the MPI executable or a batch script, see execute_parallel$m for more information and options

dPollResultTime

Polling time to check if the parallel execution is finished

strFunctionInputName

The file containing the popedInput structure that should be used to evaluate the designs

bParallelRS

If the random search is going to be executed in parallel

bParallelSG

If the stochastic gradient search is going to be executed in parallel

bParallelMFEA

If the modified exchange algorithm is going to be executed in parallel

bParallelLS

If the line search is going to be executed in parallel

groupsize

Vector defining the size of the different groups (num individuals in each group). If only one number then the number will be the same in every group.

time

string that represents the time in the dataset (ie xt)

timeLow

string that represents the lower design time (ie minxt)

timeHi

string that represents the upper design time (ie maxmt)

id

The id variable

m

Number of groups in the study. Each individual in a group will have the same design.

x

A matrix defining the initial discrete values for the model Each row is a group/individual.

ni

Vector defining the number of samples for each group.

maxni

  • ******START OF DESIGN SPACE OPTIONS**********

Max number of samples per group/individual

minni

Min number of samples per group/individual

maxtotni

Number defining the maximum number of samples allowed in the experiment.

mintotni

Number defining the minimum number of samples allowed in the experiment.

maxgroupsize

Vector defining the max size of the different groups (max number of individuals in each group)

mingroupsize

Vector defining the min size of the different groups (min num individuals in each group) –

maxtotgroupsize

The total maximal groupsize over all groups

mintotgroupsize

The total minimal groupsize over all groups

xt_space

Cell array cell defining the discrete variables allowed for each xt value. Can also be a vector of values c(1:10) (same values allowed for all xt), or a list of lists list(1:10, 2:23, 4:6) (one for each value in xt in row major order or just for one row in xt, and all other rows will be duplicated).

a

Matrix defining the initial continuous covariate values. n_rows=number of groups, n_cols=number of covariates. If the number of rows is one and the number of groups > 1 then all groups are assigned the same values.

maxa

Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value

mina

Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value

a_space

Cell array cell defining the discrete variables allowed for each a value. Can also be a list of values list(1:10) (same values allowed for all a), or a list of lists list(1:10, 2:23, 4:6) (one for each value in a).

x_space

Cell array cell defining the discrete variables for each x value.

use_grouped_xt

Group sampling times between groups so that each group has the same values (TRUE or FALSE).

grouped_xt

Matrix defining the grouping of sample points. Matching integers mean that the points are matched. Allows for finer control than use_grouped_xt

use_grouped_a

Group continuous design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_a

Matrix defining the grouping of continuous design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_a.

use_grouped_x

Group discrete design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_x

Matrix defining the grouping of discrete design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_x.

our_zero

Value to interpret as zero in design.

auto_pointer

Filename and path, or function name, for the Autocorrelation function, empty string means no autocorrelation

user_distribution_pointer

Filename and path, or function name, for user defined distributions for E-family designs

minxt

Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value

maxxt

Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.

discrete_xt

Cell array cell defining the discrete variables allowed for each xt value. Can also be a list of values list(1:10) (same values allowed for all xt), or a list of lists list(1:10, 2:23, 4:6) (one for each value in xt). See examples in create_design_space.

discrete_a

Cell array cell defining the discrete variables allowed for each a value. Can also be a list of values list(1:10) (same values allowed for all a), or a list of lists list(1:10, 2:23, 4:6) (one for each value in a). See examples in create_design_space.

fixRes

boolean; Fix the residuals to what is specified by the model

script

write a PopED/rxode2 script that can be modified for more fine control. The default is NULL.

When script is TRUE, the script is returned as a lines that would be written to a file and with the class babelmixr2popedScript. This allows it to be printed as the script on screen.

When script is a file name (with an R extension), the script is written to that file.

overwrite

[logical(1)]
If TRUE, an existing file in place is allowed if it it is both readable and writable. Default is FALSE.

literalFix

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

opt_xt

boolean to indicate if this is meant for optimizing times

opt_a

boolean to indicate if this is meant for optimizing covariates

opt_x

boolean to indicate if the discrete design variables be optimized

opt_samps

boolean to indicate if the sample optimizer is used (not implemented yet in PopED)

optTime

boolean to indicate if the global time indexer inside of babelmixr2 is reset if the times are different. By default this is TRUE. If FALSE you can get slightly better run times and possibly slightly different results. When optTime is FALSE the global indexer is reset every time the PopED rxode2 is setup for a problem or when a poped dataset is created. You can manually reset with popedMultipleEndpointResetTimeIndex()

literalFixRes

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

...

other parameters for PopED control

Value

popedControl object

Author(s)

Matthew L. Fidler

Get Multiple Endpoint Modeling Times

Description

This function takes a vector of times and a corresponding vector of IDs, groups the times by their IDs, initializes an internal C++ global TimeIndexer, that is used to efficiently lookup the final output from the rxode2 solve and then returns the sorted unique times.

The popedMultipleEndpointIndexDataFrame() function can be used to visualize the internal data structure inside R, but it does not show all the indexes in the case of time ties for a given ID. Rather it shows one of the indexs and the total number of indexes in the data.frame

Usage

popedGetMultipleEndpointModelingTimes(times, modelSwitch, sorted = FALSE)

popedMultipleEndpointIndexDataFrame(print = FALSE)

Arguments

times

A numeric vector of times.

modelSwitch

An integer vector of model switch indicator corresponding to the times

sorted

A boolean indicating if the returned times should be sorted

print

boolean for popedMultipleEndpointIndexDataFrame() when TRUE show each id/index per time even though it may not reflect in the returned data.frame

Value

A numeric vector of unique times.

Examples





times <- c(1.1, 1.2, 1.3, 2.1, 2.2, 3.1)
modelSwitch <- c(1, 1, 1, 2, 2, 3)
sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE)
print(sortedTimes)

# now show the output of the data frame representing the model
# switch to endpoint index

popedMultipleEndpointIndexDataFrame()

# now show a more complex example with overlaps etc.

times <- c(1.1, 1.2, 1.3, 0.5, 2.2, 1.1, 0.75,0.75)
modelSwitch <- c(1, 1, 1, 2, 2, 2, 3, 3)
sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE)
print(sortedTimes)

popedMultipleEndpointIndexDataFrame(TRUE) # Print to show individual matching

Populates Multiple Endpoint Parameters for internal solving

Description

This function populates a numeric vector with parameters and unique times and also populates the internal C++ global index

Usage

popedMultipleEndpointParam(p, times, modelSwitch, maxMT, optTime = TRUE)

Arguments

p

A numeric vector of parameters

times

A numeric vector of times

modelSwitch

An integer vector indicating model switches from PopED

maxMT

An integer specifying the maximum number of time points in the mtimes model

Details

Value

A numeric vector containing the parameters followed by unique times, if the maximum number of times is greater than the input this will append the maximum observed times in the input. This assumes the first parameter is the id and is dropped fro the output.

Author(s)

Matthew L. Fidler

Examples




p <- c(1.0, 2.0, 3.0)
times <- c(0.5, 1.5, 2.5)
modelSwitch <- c(1, 2, 3)
maxMT <- 5
popedMultipleEndpointParam(p, times, modelSwitch, maxMT)

Reset the Global Time Indexer for Multiple Endpoint Modeling

Description

This clears the memory and resets the global time indexer used for multiple endpoint modeling.

Usage

popedMultipleEndpointResetTimeIndex()

Value

NULL, called for side effects

Examples




popedMultipleEndpointResetTimeIndex()

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

lotri

lotri

magrittr

%>%

monolix2rx

mlxtran, mlxtran, monolix2rx, monolix2rx, monolix2rx

nlmixr2est

.nlmixrNlmeFun, ACF, addCwres, addNpde, addTable, augPred, bobyqaControl, fixed.effects, fixef, foceiControl, getData, getValidNlmixrCtl, getValidNlmixrCtl, getVarCov, groupedData, lbfgsb3cControl, n1qn1Control, newuoaControl, nlmControl, nlme, nlmeControl, nlminbControl, nlmixr, nlmixr2, nlmixr2, nlmixr2AllEst, nlmixr2Est, nlmixr2Est, nlmixr2NlmeControl, nlsControl, nmObjGetControl, nmObjGetControl, nmObjGetFoceiControl, nmObjGetFoceiControl, nmObjHandleControlObject, nmObjHandleControlObject, optimControl, pdBlocked, pdCompSymm, pdConstruct, pdDiag, pdFactor, pdIdent, pdLogChol, pdMat, pdMatrix, pdNatural, pdSymm, random.effects, ranef, reStruct, saemControl, setOfv, tableControl, uobyqaControl, varComb, varConstPower, VarCorr, varExp, varFixed, varFunc, varIdent, varPower, varWeights, vpcSim

nlmixr2extra

bootplot, bootstrapFit, preconditionFit, profileFixed, profileFixedSingle, profileLlp

nlmixr2plot

traceplot, vpcCens, vpcCensTad, vpcPlot, vpcPlotTad

nonmem2rx

as.nonmem2rx, as.nonmem2rx, nmcov, nmcov, nmcov, nmext, nmext, nminfo, nminfo, nmtab, nmtab, nmxml, nmxml, nonmem2rx, nonmem2rx

rxode2

.minfo, .minfo, add.dosing, add.sampling, et, et, etExpand, eventTable, expit, geom_amt, geom_cens, ini, ini<-, logit, lotri, model, model<-, probit, probitInv, rxCat, rxClean, rxControl, rxDerived, rxFun, rxModelVars, rxModelVars, rxode, RxODE, rxode2, rxParam, rxParams, rxSetPipingAuto, rxSetSeed, rxSolve, rxUiGet, rxUiGet, stat_amt, stat_cens

Convert RxODE syntax to monolix syntax

Description

Convert RxODE syntax to monolix syntax

Usage

rxToMonolix(x, ui)

Arguments

x

Expression

ui

rxode2 ui

Value

Monolix syntax

Author(s)

Matthew Fidler

Convert RxODE syntax to NONMEM syntax

Description

Convert RxODE syntax to NONMEM syntax

Usage

rxToNonmem(x, ui)

Arguments

x

Expression

ui

rxode2 ui

Value

NONMEM syntax

Author(s)

Matthew Fidler

Simplify units by removing repeated units from the numerator and denominator

Description

Simplify units by removing repeated units from the numerator and denominator

Usage

simplifyUnit(numerator = "", denominator = "")

Arguments

numerator

The numerator of the units (or the whole unit specification)

denominator

The denominator of the units (or NULL if numerator is the whole unit specification)

Details

NA or "" for numerator and denominator are considered unitless.

Value

The units specified with units that are in both the numerator and denominator cancelled.

See Also

Other Unit conversion: modelUnitConversion()

Examples

simplifyUnit("kg", "kg/mL")
# units that don't match exactly are not cancelled
simplifyUnit("kg", "g/mL")