The vision of nlmixr2 is to develop a R-based open-source nonlinear mixed-effects modeling software package that can compete with commercial pharmacometric tools and is suitable for regulatory submissions.
In short, the goal of nlmixr2 is to support easy and robust nonlinear mixed effects models in R. This is supported by our team and advisory committee
For more information about ongoing development, best practices, and news about nlmixr2, please see the nlmixr2 blog.
For all versions of R, we need to have a compiler setup to run
nlmixr2 and rxode2
For Windows the compilers come from RTools. Download and the install the version of RTools for your version of R from https://cran.r-project.org/bin/windows/Rtools/
To setup the mac compilers, simply
Install Xcode from app store
Install gfortran:
Download and install from https://mac.r-project.org/tools/
Add gfortran directory to the path with:
export PATH=$PATH:/usr/local/gfortran/bin
Installation nlmixr2 itself is easiest the latest version of R because no further compilation is required and all supporting packages are available. From R, run:
install.packages("nlmixr2",dependencies = TRUE)For R-4.0.x and R-4.1.x, the symengine package will need
to be downgraded to run in those earlier R versions. This
can be done by:
# install.packages("remotes")
remotes::install_version("symengine", version = "0.1.6")followed by:
install.packages("nlmixr2",dependencies = TRUE)You can check that your installation is likely setup correctly with
the following command after installing the nlmixr2
package:
nlmixr2::nlmixr2CheckInstall()Once the compilers are setup and a compatible version of
symengine is installed, you can install the development
version of nlmixr2 and its nlmixr2-family dependencies either by using
the r-universe or by installing manually.
For many people this is the fastest way to install the development
version of nlmixr2 since it provides binaries for mac,
windows for the latest and last version of R (no need to wait for a
compile).
install.packages(c("dparser", "nlmixr2data", "lotri", "rxode2ll",
"rxode2", "nlmixr2est", "nlmixr2extra", "nlmixr2plot",
"nlmixr2"),
repos = c('https://nlmixr2.r-universe.dev',
'https://cloud.r-project.org'))If you are using a Ubuntu latest flavor (at the time of this writing
jammy) you can also use the binaries (though if you use
bspm you should install any dependencies first to reduce
your computation time)
# bspm::disable() # if you are using r2u or other ubuntu binary for CRAN
oldOptions <- options()
options(repos=c(
linux = 'https://nlmixr2.r-universe.dev/bin/linux/jammy/4.2/',
sources = 'https://nlmixr2.r-universe.dev',
cran = 'https://cloud.r-project.org'
))
install.packages(c("dparser", "nlmixr2data", "lotri", "rxode2ll",
"rxode2", "nlmixr2est", "nlmixr2extra", "nlmixr2plot",
"nlmixr2"))
options(oldOptions)
#bspm::enable()Support packages from the R universe can also be installed for the
packages in the nlmixr2 domain:
install.packages(c("xpose.nlmixr2", # Additional goodness of fit plots
# baesd on xpose
"nlmixr2targets", # Simplify work with the
# `targets` package
"babelmixr2", # Convert/run from nlmixr2-based
# models to NONMEM, Monolix, and
# initialize models with PKNCA
"nonmem2rx", # Convert from NONMEM to
# rxode2/nlmixr2-based models
"nlmixr2lib", # a model library and model
# modification functions that
# complement model piping
"nlmixr2rpt" # Automated Microsoft Word and
# PowerPoint reporting for nlmixr2
),
repos = c('https://nlmixr2.r-universe.dev',
'https://cloud.r-project.org'))
# Some additional packages outside of the `nlmixr2.r-univers.dev`
# install.packages("remotes")
remotes::install_github("ggPMXdevelopment/ggPMX") # Goodness of fit plots
remotes::install_github("RichardHooijmaijers/shinyMixR") # Shiny run manager (like Piranha)For Ubuntu latest it is similar
# bspm::disable() # if you are using r2u or other ubuntu binary for CRAN
oldOptions <- options()
options(repos=c(
linux = 'https://nlmixr2.r-universe.dev/bin/linux/jammy/4.2/',
sources = 'https://nlmixr2.r-universe.dev',
cran = 'https://cloud.r-project.org'
))
install.packages(c("xpose.nlmixr2", "nlmixr2targets", "babelmixr2", "nonmem2rx", "nlmixr2lib", "nlmixr2rpt"))
options(oldOptions)
#bspm::enable()
# install.packages("remotes")
remotes::install_github("ggPMXdevelopment/ggPMX") # Goodness of fit plots
remotes::install_github("RichardHooijmaijers/shinyMixR") # Shiny run manager (like Piranha)remotesThis is sure to give the latest development version
# install.packages("remotes")
remotes::install_github("nlmixr2/dparser-R")
remotes::install_github("nlmixr2/nlmixr2data")
remotes::install_github("nlmixr2/lotri")
remotes::install_github("nlmixr2/rxode2ll")
remotes::install_github("nlmixr2/rxode2")
remotes::install_github("nlmixr2/nlmixr2est")
remotes::install_github("nlmixr2/nlmixr2extra")
remotes::install_github("nlmixr2/nlmixr2plot")
remotes::install_github("nlmixr2/nlmixr2")Optional supporting packages can be installed like so:
# install.packages("remotes")
# Goodness of fit plots
remotes::install_github("ggPMXdevelopment/ggPMX")
# Additional goodness of fit plots
remotes::install_github("nlmixr2/xpose.nlmixr2")
# Shiny run manager (like Piranha)
remotes::install_github("RichardHooijmaijers/shinyMixR")
# Simplify work with the `targets` package
remotes::install_github("nlmixr2/nlmixr2targets")
# Convert/run from nlmixr2-based models to NONMEM, Monolix, and initialize
# models with PKNCA
remotes::install_github("nlmixr2/babelmixr2")
# Convert from NONMEM to rxode2/nlmixr2-based models
remotes::install_github("nlmixr2/nonmem2rx")
# A library of models and model modification functions
remotes::install_github("nlmixr2/nlmixr2lib")
# Automated Microsoft Word and PowerPoint reporting for nlmixr2
remotes::install_github("nlmixr2/nlmixr2rpt")If you have difficulties due to errors while compiling models, it may
be useful to re-install all of nlmixr2 and its dependencies. For
development versions, please use the
remotes::install_github() or the
install.package() with the r-universe above.
For the stable version, please use the following command:
install.packages(c("dparser", "lotri", "rxode2ll", "rxode2parse",
"rxode2random", "rxode2et", "rxode2",
"nlmixr2data", "nlmixr2est", "nlmixr2extra",
"nlmixr2plot", "nlmixr2"))This is a basic example which shows you how to solve a common problem:
library(nlmixr2)
## The basic model consists of an ini block that has initial estimates
one.compartment <- function() {
ini({
tka <- log(1.57); label("Ka")
tcl <- log(2.72); label("Cl")
tv <- log(31.5); label("V")
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
# and a model block with the error specification and model specification
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)
})
}
## The fit is performed by the function nlmixr/nlmixr2 specifying the model, data and estimate
fit <- nlmixr2(one.compartment, theo_sd, est="saem", saemControl(print=0))
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print(fit)
#> ── nlmixr² SAEM OBJF by FOCEi approximation ──
#>
#> Gaussian/Laplacian Likelihoods: AIC() or $objf etc.
#> FOCEi CWRES & Likelihoods: addCwres()
#>
#> ── Time (sec $time): ──
#>
#> setup covariance saem table compress other
#> elapsed 0.00089 0.007004 4.546 0.05 0.018 1.785106
#>
#> ── Population Parameters ($parFixed or $parFixedDf): ──
#>
#> Parameter Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka Ka 0.46 0.196 42.7 1.58 (1.08, 2.33) 71.9 -0.291%
#> tcl Cl 1.01 0.0839 8.29 2.75 (2.34, 3.25) 27.0 3.42%
#> tv V 3.45 0.0469 1.36 31.6 (28.8, 34.7) 14.0 10.7%
#> add.sd 0.694 0.694
#>
#> Covariance Type ($covMethod): linFim
#> No correlations in between subject variability (BSV) matrix
#> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)
#> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink
#> Censoring ($censInformation): No censoring
#>
#> ── Fit Data (object is a modified tibble): ──
#> # A tibble: 132 × 19
#> ID TIME DV PRED RES IPRED IRES IWRES eta.ka eta.cl eta.v cp
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0.74 0 0.74 0 0.74 1.07 0.0988 -0.484 -0.0843 0
#> 2 1 0.25 2.84 3.27 -0.433 3.87 -1.03 -1.49 0.0988 -0.484 -0.0843 3.87
#> 3 1 0.57 6.57 5.85 0.718 6.82 -0.247 -0.356 0.0988 -0.484 -0.0843 6.82
#> # ℹ 129 more rows
#> # ℹ 7 more variables: depot <dbl>, center <dbl>, ka <dbl>, cl <dbl>, v <dbl>,
#> # tad <dbl>, dosenum <dbl>You can use the built-in plot with the fit and it will
produce a standard set of goodness of fit plots:
pdf(file="myplots.pdf")
plot(fit)
dev.off()The {xpose.nlmixr2} package extends xpose support for nlmixr2. You simply need to convert the fit results into an xpose database:
library(xpose.nlmixr2)
xpdb = xpose_data_nlmixr(fit)Then you can use any of the xpose functions for generating goodness of fit plots:
library(xpose)
plt <- dv_vs_ipred(xpdb)Another option is to use the ggPMX package. You first create a ggPMX controller object from the nlmixr fit object. Then that controller object can be used to generate figures:
library(ggPMX)
ctr = pmx_nlmixr(fit)
pmx_plot_dv_ipred(ctr)