Title:	Nonlinear Mixed Effects Models in Population PK/PD, Plot Functions
Version:	3.0.2
Description:	Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for 'ggplot2' plotting methods for 'nlmixr2' objects.
License:	GPL (≥ 3)
URL:	https://github.com/nlmixr2/nlmixr2plot, https://nlmixr2.github.io/nlmixr2plot/
BugReports:	https://github.com/nlmixr2/nlmixr2plot/issues/
Imports:	ggplot2 (≥ 3.4.0), nlmixr2est, nlmixr2extra, rxode2, utils, vpc, xgxr
Suggests:	testthat (≥ 3.0.0), dplyr, withr, nlmixr2data
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-06-25 02:25:15 UTC; matt
Author:	Matthew Fidler [aut, cre], Bill Denney [ctb], Wenping Wang [aut], Vipul Mann [aut]
Maintainer:	Matthew Fidler <matthew.fidler@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-25 05:30:02 UTC

Plot a nlmixr2 augPred object

Description

Plot a nlmixr2 augPred object

Usage

## S3 method for class 'nlmixr2AugPred'
plot(x, y, ...)

Arguments

Value

Nothing called for side effects

Examples

library(nlmixr2est)
## The basic model consiss of an ini block that has initial estimates
one.compartment <- function() {
  ini({
    tka <- 0.45 # Log Ka
    tcl <- 1 # Log Cl
    tv <- 3.45    # Log V
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
  })
  # and a model block with the error sppecification 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/nlmix2 specifying the model, data and estimate
fit <- nlmixr2est::nlmixr2(one.compartment, theo_sd,  est="saem", saemControl(print=0))

# augPred shows more points for the fit:

a <- nlmixr2est::augPred(fit)

# you can plot it with plot(augPred object)
plot(a)

Plot a nlmixr2 data object

Description

Plot some standard goodness of fit plots for the focei fitted object

Usage

## S3 method for class 'nlmixr2FitData'
plot(x, ...)

Arguments

Value

An nlmixr2PlotList object (a list of ggplot2 objects with easier plotting for all of them at the same time)

Author(s)

Wenping Wang & Matthew Fidler

Examples

library(nlmixr2est)
one.compartment <- function() {
  ini({
    tka <- 0.45
    tcl <- 1
    tv <- 3.45
    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)
  })
}

## The fit is performed by the function nlmixr/nlmix2 specifying the model, data and estimate
fit <- nlmixr2(one.compartment, theo_sd,  est="saem", saemControl(print=0, nBurn = 10, nEm = 20))

# This shows many goodness of fit plots
plot(fit)

Objects exported from other packages

Description

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

rxode2

geom_amt, geom_cens, stat_amt, stat_cens

Produce trace-plot for fit if applicable

Description

Produce trace-plot for fit if applicable

Usage

traceplot(x, ...)

## S3 method for class 'nlmixr2FitCore'
traceplot(x, ...)

Arguments

Value

Fit traceplot or nothing.

Author(s)

Rik Schoemaker, Wenping Wang & Matthew L. Fidler

Examples

library(nlmixr2est)
## The basic model consiss of an ini block that has initial estimates
one.compartment <- function() {
  ini({
    tka <- 0.45 # Log Ka
    tcl <- 1 # Log Cl
    tv <- 3.45    # Log V
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
  })
  # and a model block with the error sppecification 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/nlmix2 specifying the model, data and estimate
fit <- nlmixr2(one.compartment, theo_sd,  est="saem", saemControl(print=0))

# This shows the traceplot of the fit (useful for saem)
traceplot(fit)

VPC based on ui model

Description

VPC based on ui model

Usage

vpcPlot(
  fit,
  data = NULL,
  n = 300,
  bins = "jenks",
  n_bins = "auto",
  bin_mid = "mean",
  show = NULL,
  stratify = NULL,
  pred_corr = FALSE,
  pred_corr_lower_bnd = 0,
  pi = c(0.05, 0.95),
  ci = c(0.05, 0.95),
  uloq = fit$dataUloq,
  lloq = fit$dataLloq,
  log_y = FALSE,
  log_y_min = 0.001,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  smooth = TRUE,
  vpc_theme = NULL,
  facet = "wrap",
  scales = "fixed",
  labeller = NULL,
  vpcdb = FALSE,
  verbose = FALSE,
  ...,
  seed = 1009,
  idv = "time",
  cens = FALSE
)

vpcPlotTad(..., idv = "tad")

vpcCensTad(..., cens = TRUE, idv = "tad")

vpcCens(..., cens = TRUE, idv = "time")

Arguments

Value

Simulated dataset (invisibly)

Author(s)

Matthew L. Fidler

Examples

one.cmt <- function() {
 ini({
   tka <- 0.45; label("Ka")
   tcl <- log(c(0, 2.7, 100)); label("Cl")
   tv <- 3.45; label("V")
   eta.ka ~ 0.6
   eta.cl ~ 0.3
   eta.v ~ 0.1
   add.sd <- 0.7; label("Additive residual error")
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

fit <-
  nlmixr2est::nlmixr(
    one.cmt,
    data = nlmixr2data::theo_sd,
    est = "saem",
    control = list(print = 0)
  )

vpcPlot(fit)