ggproto for position_scale

Description

ggproto for position_scale

Usage

PositionScale

Format

An object of class PositionScale (inherits from PositionDodge, Position, ggproto, gg) of length 5.

ggproto for stat_corr

Description

ggproto for stat_corr adding correlation to x-y plots

Usage

StatCorr

Format

An object of class StatCorr (inherits from Stat, ggproto, gg) of length 3.

ggproto for stat_denx

Description

ggproto for stat_denx adding density for x with y-scaled to fit current scale

Usage

StatDenx

Format

An object of class StatDenx (inherits from Stat, ggproto, gg) of length 3.

ggproto for stat_dnorm

Description

ggproto for stat_dnorm doing normal density to histograms

Usage

StatDnorm

Format

An object of class StatDnorm (inherits from Stat, ggproto, gg) of length 3.

ggproto for stat_hmean

Description

ggproto for stat_hmean doing horizontal reference line at mean

Usage

StatHmean

Format

An object of class StatHmean (inherits from Stat, ggproto, gg) of length 3.

ggproto for stat_hmedian

Description

ggproto for stat_hmedian doing horizontal reference line at median

Usage

StatHmedian

Format

An object of class StatHmedian (inherits from Stat, ggproto, gg) of length 3.

Add caption to ggplot object

Description

Adds caption text as returned by get.caption to a ggplot object

Usage

add.caption(p, control = GOF.control())

Arguments

Basic 4- and 6-panel GOF

Description

basic.GOF4 provides a 4-panel GOF plot showing
i) Observations (DV) vs population predictions (PRED)
ii) Observations (DV) vs individual predictions (IPRED)
iii) sqrt(abs(CWRES)) vs individual predictions (IPRED), and
iV) CWRES vs TIME or other columns as set by idv

basic.GOF6 provides 2 additional GOF's showing histogram and qqnorm GOF's for CWRES.

Caption is added as reurned by get.caption

Usage

basic.GOF4(
  data,
  residual = "CWRES",
  idv1 = "IPRED",
  idv2 = "TIME",
  title = "",
  color = "",
  log.scale = FALSE,
  global.ggplot.options = NULL,
  refline = TRUE,
  add.loess = TRUE,
  control = GOF.control()
)

basic.GOF6(
  data,
  residual = "CWRES",
  idv1 = "IPRED",
  idv2 = "TIME",
  title = "",
  color = "",
  log.scale = FALSE,
  global.ggplot.options = NULL,
  refline = TRUE,
  add.loess = TRUE,
  bins = NA,
  control = GOF.control()
)

Arguments

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
## Example 4 panel basic GOF
basic.GOF4(subset(sdtab,DV>0),idv2="TAPD")
## Example 6 panel basic GOF
basic.GOF6(subset(sdtab,DV>0),idv1="TAPD",idv2="PRED")

Histogram and/or QQ-norm GOF for ETA's

Description

Provides histogram and/or QQ-norm GOF for all ETA's included in the input data.frame with caption added as reurned by get.caption

Usage

basic.eta.GOF(
  data,
  title = "",
  global.ggplot.options = NULL,
  type = c("both", "qqnorm", "hist"),
  ETA.subset = NULL,
  refline = TRUE,
  drop.fixed = TRUE,
  id.column = "ID",
  standardize = TRUE,
  bins = NA,
  control = GOF.control()
)

eta.hist.GOF(
  data,
  title = "",
  drop.fixed = TRUE,
  refline = TRUE,
  id.column = "ID",
  standardize = TRUE,
  bins = NA,
  control = GOF.control()
)

eta.qqnorm.GOF(
  data,
  title = "",
  drop.fixed = TRUE,
  refline = TRUE,
  id.column = "ID",
  standardize = TRUE,
  control = GOF.control()
)

Arguments

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
basic.eta.GOF(sdtab)

Load the covariance matrix from a nonmem .cov output file.

Description

Load the covariance matrix from a nonmem .cov output file. Either the covariance matrix of all THETA parameters (default) or the covariance matrix of all parameters, THETA, OMEGA and SIGMA.

For cov-files with multiple table results either only the last table result is loaded (last.table.only=TRUE, default) or all table results are loaded (last.table.only=FALSE). The result is then a named list of covariance matrix's, LastTable is the last table in the cov-files.

Usage

covload(
  model,
  use.model.path = TRUE,
  last.table.only = TRUE,
  theta.only = TRUE
)

Arguments

Value

The covariance matrix or a named list of covariance matrix's

Examples

##### Load the .cov file "run001.cov"
# 1) Get path to the example file included in nonmem2R package
file1 <- system.file("extdata", "run001.cov", package = "nonmem2R")
# 2) Load the file using the covload function
covload(file1)

Internal package function

Description

Internal package function

Usage

covload.sub.table(model, skip, nrow, use.model.path = TRUE, theta.only = TRUE)

Arguments

Value

Named list including theta, omega, sigma, and ofv. For MCMC output file each object are matrixes.

Y vs categorical X GOF-plot

Description

GOF plot with boxplots of Y grouped by categorical data in X, with refline and with caption added as reurned by get.caption

Usage

do.cat.GOF(
  data,
  x,
  y,
  color = "",
  add.points = TRUE,
  refline = c("abline", "href0", "hrefmean", "hrefmedian", "none"),
  title = "",
  lines.by.id = FALSE,
  id.column = "ID",
  fx = NULL,
  fy = NULL,
  control = GOF.control()
)

Arguments

Individual GOF-plots

Description

DV, IPRED and PRED (default) individual GOF plots with one panel per subject. Caption added as reurned by get.caption

Usage

do.individual.GOF(
  data,
  x = "TIME",
  y = c("DV", "IPRED", "PRED"),
  type = c("point", "line", "line"),
  title = "",
  per.page = 20,
  fx = NULL,
  fy = NULL,
  ylab = "Observations/Predictions",
  equal.lims = TRUE,
  global.ggplot.options = NULL,
  control = GOF.control()
)

Arguments

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
do.individual.GOF(subset(sdtab,DV>0 & ID<13))

Multiple Y vs single X GOF-plot

Description

X-Y GOF plots for multiple Y's and shared X with reflines and data smoother and with caption added as reurned by get.caption

Usage

do.multi.GOF(
  data,
  x,
  y,
  color = "",
  add.loess = TRUE,
  refline = c("none", "abline", "href0", "hrefmean", "hrefmedian"),
  title = "",
  lines.by.id = FALSE,
  id.column = "ID",
  fx = NULL,
  fy = NULL,
  ylab = "Observations/Predictions",
  control = GOF.control()
)

Arguments

X-Y GOF-plot

Description

X-Y GOF plot with reflines and data smoother and with caption added as reurned by get.caption

Usage

do.one.GOF(
  data,
  x,
  y,
  color = "",
  add.loess = TRUE,
  refline = c("abline", "href0", "hrefmean", "hrefmedian", "none"),
  title = "",
  lines.by.id = FALSE,
  id.column = "ID",
  fx = NULL,
  fy = NULL,
  control = GOF.control()
)

Arguments

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
do.one.GOF(subset(sdtab,DV>0),"IPRED","DV")

Covariate and pairs ETA GOFs

Description

ETA's vs numerical covariates (eta.cov.GOF) and ETA's vs categorical covariates GOF plots on multiple or single pages with reflines, data smoother, and caption added as reurned by get.caption.

eta.pairs.GOF provides a pairs plot of all ETA's.

Usage

eta.cov.GOF(
  data,
  covariates = c("AGE", "BWT"),
  ETA.subset = NULL,
  exclude.zero.ETA = FALSE,
  title = "",
  drop.fixed = TRUE,
  id.column = "ID",
  standardize = TRUE,
  refline = TRUE,
  type = c("all-in-one", "covariate-by-page", "eta-by-page"),
  layout = c("ETAbyROW", "ETAbyCOL"),
  add.loess = TRUE,
  control = GOF.control()
)

eta.cat.GOF(
  data,
  covariates = c("SEXM"),
  ETA.subset = NULL,
  exclude.zero.ETA = FALSE,
  title = "",
  drop.fixed = TRUE,
  id.column = "ID",
  standardize = TRUE,
  refline = TRUE,
  type = c("all-in-one", "covariate-by-page", "eta-by-page"),
  layout = c("ETAbyROW", "ETAbyCOL"),
  add.points = TRUE,
  control = GOF.control()
)

eta.pairs.GOF(
  data,
  ETA.subset = NULL,
  title = "",
  drop.fixed = TRUE,
  id.column = "ID",
  density2D = c("none", "upper", "lower"),
  standardize = TRUE,
  control = GOF.control()
)

Arguments

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
eta.cov.GOF(sdtab,covariates=c("AGE","BWT"))
# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
eta.cat.GOF(sdtab,covariates=c("SEXM"))
# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab999", package = "nonmem2R")
sdtab<-read.table(file=file1,skip=1,header=TRUE)
set.script.name("MyScript.R")
eta.pairs.GOF(sdtab)

Internal package function

Description

Formatting of the named list returned by extload

Usage

extFormat(ext, format.estimate = "% -#6.4g", format.rse = "%#6.3g")

Arguments

Value

a character-matrix

Compile parameter table suitable for reports

Description

Load parameter values from a nonmem .ext output file and compile to table suitable for reports. Format can be "wide" (wide=TRUE, default) with a similar look as from sumoR, or similar a 3 column layout (wide=FALSE).

Usage

extToTable(
  model,
  use.model.path = TRUE,
  tableType = 2,
  wide = TRUE,
  format.estimate = "% -#6.4g",
  format.rse = "%#6.3g"
)

Arguments

Value

a character-matrix

Examples

##### Load the .ext file "run001.ext"
# 1) Get path to the example file included in nonmem2R package
file1 <- system.file("extdata", "run001.ext", package = "nonmem2R")
# 2) Load the file using the extload function
extToTable(file1)

Internal package function

Description

Transforming of the named list returned by extload

Usage

extTransform(ext, type = 2)

Arguments

Value

named list according to output from extload

Load final parameter values from a nonmem .ext output file.

Description

Load parameter values from a nonmem .ext output file. For output files from FO, FOCE and IMP only the final parameter values are loaded. For output tables of MCMC and SAEM all parameter values from ITERATIONS>0 will be loaded unless positive.iterations=FALSE in case all the output from all iterations are loaded.

For ext-files with multiple table results either only the last table result is loaded (last.table.only=TRUE, default) or all table result are loaded (last.table.only=FALSE). All but the last table results are then returned as sub-lists to the last table result.

Usage

extload(
  model,
  use.model.path = TRUE,
  positive.iterations.only = TRUE,
  last.table.only = TRUE
)

Arguments

Value

Named list including theta, theta.sd, omega, omega.sd, sigma, sigma.sd, and ofv. Here .sd is the vector(matrix) with standard errors estimated parameter values in theta(omega & sigma).

For MCMC output files each object are matrixes.

Examples

##### Load the .ext file "run001.ext"
# 1) Get path to the example file included in nonmem2R package
file1 <- system.file("extdata", "run001.ext", package = "nonmem2R")
# 2) Load the file using the extload function
extload(file1)

Internal package function

Description

Internal package function

Usage

extload.sub.table(
  model,
  skip,
  nrows,
  positive.iterations.only,
  use.model.path = TRUE
)

Arguments

Value

Named list including theta, omega, sigma, and ofv. For MCMC output file each object are matrixes.

Get, set, and modify the global (or local) GOF parameters and GOF dictionary for labels

Description

The current global GOF parameters and dictionary is automatically applied to every plot you draw. Use 'get.GOF.params' to get the current GOF parameters, and 'set.GOF.params' to change one or several parameters. To change settings for one GOF only you can use GOF.control as input to the GOF function. See details below for complete list of GOF parameters.

Similarly use 'get.GOF.dictionary' and 'set.GOF.dictionary' for the dictionary of labels. 'set.GOF.dictionary' also allows for adding new items to the dictionary.

Use ‘set.ETA.labels' to set labels for ETA’s in all plots

Usage

get.GOF.dictionary()

set.GOF.dictionary(...)

default.GOF.dictionary()

set.GOF.params(...)

get.GOF.params()

default.GOF.params()

set.ETA.labels(labels)

set.script.name(script.name)

GOF.control(...)

Arguments

Details

Table below describe each of the global GOF parameters that can be modified by 'set.GOF.params'

Examples

### Example setting ETA labels
set.ETA.labels(c("Ka","CL","V"))
### Example setting script name
set.script.name("MyScript.R")

Labels for known NONMEM variables

Description

get.label match known NONMEM variables to the GOF-dictionary and returns the matched label. Unless trans is NULL, the label is modified to 'f(matched label)'

Usage

get.label(x, trans = NULL)

Arguments

See Also

[get.GOF.dictionary()], [set.GOF.dictionary()], and [default.GOF.dictionary()].

Get and set model.path

Description

The model.path in the nonmem2R set of global variables is used by functions loading NONMEM or PSN generated output file. The model.path can ge retrived by get.model.path and can be set using set-model.path. When loading nonmem2R the model-path is set to "", e.g. the current working directory. The model.path can be set abolute or relative, see examples.

Usage

get.model.path()

set.model.path(model.path)

Arguments

Examples

## Not run: 
## Example for setting absolute path
set.model.path("c:/NONMEM")
## Example for setting relative path,
set.model.path("../NONMEM")

## End(Not run)

Combine fix grid and simulated grid based on multivariate normal distribution

Description

grid.sim produce comparable output to mvnorm, however for methods 2 to 4 grid.sim make use of center and scaling, and or use a fix grid of values for one column of the output. The intended use of grid.sim is for computing confidence intervals (CI) for model predictions. Method 1 is the same as rmvnorm, however can handle cases of zero variances in sigma. Method 2 is method 1 followed by centering and scaling of the simulated matrix. Thus output when using method 2 always have sample mean and covariance equal to input parameters means and sigma. Method 3 is the same as method 1 however with one column having a fixed range of values rather than a simulated range of values. Method 4 is method 3 followed by centering and scaling of the simulated matrix. Thus output when using method 4 always have sample mean and covariance equal to input parameters means and sigma.

As the number of simulations (n) goes to infinity all methods in grid.sim are identical to mvnorm in that the sample covariance (mean) of the output will converge to the input covariance matrix sigma (vector means).

The advantage with methods 2 to 4 (over method 1 and rmvnorm) is that they provides more stable results, hence number of simulations can be reduced and still have equally stable results when used to represent parameter uncertainty and or population variability in model predictions.

Usage

grid.sim(n, means = NULL, sigma, grid.param = NULL, method = 4)

Arguments

Value

row-matrix of parameters

Examples

sigma<-matrix(c(1,0.5,0.5,2),ncol=2)
sim1<-grid.sim(1000,sigma=sigma)
pairs(sim1)
cov(sim1)

Internal function

Description

Internal function

Usage

grid.sim.internal1(n, means = NULL, sigma, grid.param = NULL, pure.sim = FALSE)

Arguments

Value

row-matrix of parameters

Internal function

Description

Internal function

Usage

grid.sim.internal2(n, means = NULL, sigma, grid.param = NULL)

Arguments

Value

row-matrix of parameters

Internal function

Description

Internal function

Usage

grid.sim.internal3(n, means = NULL, sigma)

Arguments

Value

row-matrix of parameters

Histogram GOF

Description

Histogram GOF showing histogram, smooth density, and normal density as reference line and caption added as reurned by get.caption

Usage

histGOF(
  data,
  x,
  title = "",
  color = "",
  fx = NULL,
  bins = NA,
  refline = TRUE,
  control = GOF.control()
)

Arguments

Examples

dd<-data.frame(CWRES=rnorm(100),gr=rep(LETTERS[1:5],20))
histGOF(dd,"CWRES",color="gr")

Merging 2, 4, or 6 GOF's into one graph

Description

Merging multiple GOFs (ggplot objects) into one graph with caption added as reurned by get.caption. Any legend present in first GOF is added to the combined graph.

Usage

merge2GOF(p1, p2, byrow = TRUE)

merge4GOF(p1, p2, p3, p4)

merge6GOF(p1, p2, p3, p4, p5, p6)

Arguments

Show model file

Description

Load and dump model file in the consol.

Usage

modload(model, use.model.path = TRUE)

Arguments

Value

data.frame

Examples

##### Load the .ext file "run001.mod"
# 1) Get path to the example file included in nonmem2R package
file1 <- system.file("extdata", "run001.mod", package = "nonmem2R")
# 2) Load the file using the extload function
modload(file1)

Position for scaling y

Description

Position for scaling y

Usage

position_scale(y = 1)

Arguments

Details

Position for scaling y

Print function for compiled summary information of class extToTable

Description

Print function for compiled summary information of class extToTable

Usage

## S3 method for class 'extToTable'
print(x, ...)

Arguments

Print function for loaded mod file

Description

Print function for loaded mod file class modload

Usage

## S3 method for class 'modload'
print(x, ...)

Arguments

Print function for compiled summary information of class sumoR

Description

Print function for compiled summary information of class sumoR

Usage

## S3 method for class 'sumoR'
print(x, ...)

Arguments

QQ-norm GOF

Description

QQ-norm GOF qqnorm plots with reference line and caption added as reurned by get.caption

Usage

qqnormGOF(
  data,
  sample,
  title = "",
  color = "",
  fx = NULL,
  refline = TRUE,
  control = GOF.control()
)

Arguments

Examples

dd<-data.frame(CWRES=rnorm(100))
qqnormGOF(dd,"CWRES")

Internal package function

Description

Internal package function

Usage

readVpc(vpc.results = NULL, npc.results = NULL, verbose = FALSE, ...)

Arguments

Value

Named list

Adding correlation to x-y plots

Description

Correlation to x-y plots. This stat is indended for providing correlations e.g. above the diagonal of a pairs plot

Usage

stat_corr(
  mapping = NULL,
  data = NULL,
  geom = "text",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

Provide normal QQplot

Density for x in x-y plot

Description

Density for x in x-y plot. This stat is indended for providing the a density on the diagonal of a pairs plot

Usage

stat_denx(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

Provide normal QQplot

Normal density

Description

Normal density with ggplot2

Usage

stat_dnorm(
  mapping = NULL,
  data = NULL,
  geom = "line",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

Provide normal QQplot

Horizontal reference at mean

Description

Horizontal reference at mean with ggplot2

Usage

stat_hmean(
  mapping = NULL,
  data = NULL,
  geom = "hline",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

Provide normal QQplot

Horizontal reference at median

Description

Horizontal reference at median with ggplot2

Usage

stat_hmedian(
  mapping = NULL,
  data = NULL,
  geom = "hline",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

Provide normal QQplot

Aesthetics

geom_smooth understands the following aesthetics (required aesthetics are in bold):
x, alpha, colour, fill, group, shape, size, stroke

Compile summary information for NONMEM model based on the lst file, ext file, and the cov file.

Description

Compile summary information similar to that of the sumo PSN function, based on the NONMEM output files lst, ext, and if covariance setp was run, the cov file.

Usage

sumoR(
  model,
  use.model.path = TRUE,
  tableType = 2,
  format.estimate = "% -#6.4g",
  format.rse = "%#6.3g"
)

Arguments

Value

named list of class sumoR

Examples

##### Compile summary information from the .lst file "run001.lst"
# 1) Get path to the example file included in nonmem2R package
file1 <- system.file("extdata", "run001.lst", package = "nonmem2R")
# 2) Compile summary information from "run001.lst"
sumoR(file1)

Internal package function

Description

Internal function for sumoR on unix/linux system

Usage

sumoRU(file.path)

Arguments

Value

named list

Function for testing grid.sim and compare with rmvnorm

Description

Test grid.sim

Usage

test.grid.sim(n = 1000, k = 1:4)

Arguments

Value

graphics

Examples

## Not run: 
require(lattice)
test.grid.sim(n=1000)

## End(Not run)

Visual Predictive Check (VPC) based on Perl-speaks-NONMEM (PsN) generated VPC files (lattice version).

Description

This function creates VPC using output files from the vpc command in Pearl Speaks NONMEM (PsN). Graphs are generated using the lattice package with many arguments for plot settings are passed directly to the xyplot function and have the same flexibility as when used in xyplot.

Usage

vpcfig(
  vpcdir = NULL,
  vpctab = NULL,
  vpcresult = NULL,
  use.model.path = TRUE,
  strata.names = NULL,
  strata.subset = NULL,
  percentile = 10,
  fy = function(y) {     y },
  fx = function(x) {     x },
  xlab = NULL,
  ylab = NULL,
  col.data = 8,
  cex.data = 0.3,
  pch.data = 3,
  col.line = c("blue", "red", "blue"),
  lwd.line = c(1, 2, 1),
  lty.line = c(1, 1, 1),
  col.segm = c("lightblue", "pink", "lightblue"),
  alpha.segm = c(0.5, 0.5, 0.5),
  type = 3,
  ...
)

Arguments

Details

The lattice package is used for creating the VPC and vpcfig have functionality for e.g. modify names of strata, change layout of panels, use log-scale.

The data used is either specified by the directory of the PsN generated files, or by specifying the file names of both the vpctab-file and the vpcresult file. See examples 1 and 2 below.

Names of strata can be changed with the strata.names argument, if strata.names is NULL the names as specified in the vpcresult file are used.

strata.subset can be used to select a subset of strata, and or to change the order of stratas. Se example 3. NOTE: strata.subset is matched with strata.names unless strata.names=NULL. If strata.names=NULL then strata.subset is matched to names as specified in vpcresult file. Default is to use all strata's (strata.subset=NULL).

The fy and fx arguments can be used to alter the scale of data plotted. For example, using fy=function(y)(log(y+1)) is a convenient way to get log-scale for y-axis but with an off-set to show any values==0. Proper y tick marks can then be set by using the xyplot argument scales.

Value

lattice object of VPC plot

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "vpctab004.dat", package = "nonmem2R")
file2 <- system.file("extdata", "vpc_results.csv", package = "nonmem2R")


# Ex 1, produce VPC with default setting, here specifying both vpctab and vpcresult
vpcfig(vpctab=file1,vpcresult=file2)

## Not run: 
# Ex 2, produce VPC with default setting, here specifying only directory of vpc files
path1<-gsub("vpctab004.dat","",file1)
vpcfig(vpcdir=path1)

## End(Not run)

# Ex 3, produce VPC with i) modifies strata names, ii) strata in reverse order, and
#        iii) labels
strata.names<-c("Group A","Group B")
xlab<-"Time after dose (hrs)"
ylab<-"Plasma Conc(mmol/L)"
vpcfig(vpctab=file1,vpcresult=file2,strata.names=strata.names,strata.subset=2:1,
       xlab=xlab,ylab=ylab)

Visual Predictive Check (VPC) based on Perl-speaks-NONMEM (PsN) generated VPC files (ggplot2-version).

Description

This function creates VPC using output files from the vpc command in Pearl Speaks NONMEM (PsN). Graphs are generated using the ggplot2 package and the return object is an ggplot class and can be further modified, see details below and documentation for ggplot2 for further details.

Usage

vpcfig2(
  vpcdir = NULL,
  vpctab = NULL,
  vpcresult = NULL,
  use.model.path = TRUE,
  strata.names = NULL,
  strata.subset = NULL,
  median.only = FALSE,
  bin.idv = c("median", "midpoint"),
  percentile = 10,
  fy = function(y) {     y },
  fx = function(x) {     x },
  xlab = NULL,
  ylab = NULL,
  col.data = "gray20",
  cex.data = 1,
  pch.data = 19,
  alpha.data = 0.5,
  col.line = "grey20",
  lwd.line = 1,
  lty.line = c(2, 1, 2),
  col.segm = c("steelblue", "gray50", "steelblue"),
  alpha.segm = 0.6,
  type = 3,
  panel.height.censored = 0.25,
  censoring.labels = c("ALQ", "BLQ"),
  ignore.censoring = FALSE,
  control = GOF.control()
)

Arguments

Details

The ggplot2 package is used for creating the VPC and vpcfig2 have functionality for e.g. modify names of strata, order of and subset of strata's, and transform the x- and or y-axis before plotting.

The data used is either specified by the directory of the PsN generated files, or by specifying the file names of both the vpctab-file and the vpcresult file. See examples 1 and 2 below.

Names of strata can be changed with the strata.names argument, if strata.names is NULL the names as specified in the vpcresult file are used.

strata.subset can be used to select a subset of strata, and or to change the order of stratas. See example 3.

Strata.subset is matched with strata.names unless strata.names=NULL. If strata.names=NULL then strata.subset is matched to names as specified in vpcresult file. Default is to use all strata's (strata.subset=NULL).

The fy and fx arguments can be used to alter the scale of data plotted. For example, using fy=function(y){log(y+1)} is a convenient way to get log-scale for y-axis but with an off-set to show any values==0. Proper y tick marks can then be set using the scale_y_continuous ggplot2 function.

Since the returned objest is a ggplot-class object is can be further modified to e.g. to log-scale for y.axis;

vpcfig2(...)+scale_y_log10(),

adding/changing labels & titles;

vpcfig2(...)+labs(y="Modified y-label", title="New title").

Axis-limits are preferably set using the ggplot2 function coord_cartesian(...). This way data points outside the axis-limit are only hidden when plotting.

Stratified VPC's are created with facet_wrap(~strata) as deafult but can be modified to use facet_grid for setting the panel grid.

However faceting must be done with ~strata.

See examples below and documentation for ggplot2 for further details.

Value

ggplot object of VPC plot

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "vpctab004.dat", package = "nonmem2R")
file2 <- system.file("extdata", "vpc_results.csv", package = "nonmem2R")


# Ex 1, produce VPC with default setting, here specifying both vpctab and vpcresult
vpcfig2(vpctab=file1,vpcresult=file2)

## Not run: 
# Ex 2, produce VPC with default setting, here specifying only directory of vpc files
path1<-gsub("vpctab004.dat","",file1)
vpcfig2(vpcdir=path1)

## End(Not run)

# Ex 3, produce VPC with i) modifies strata names, ii) strata in reverse order, and
#        iii) labels
strata.names<-c("Group A","Group B")
xlab<-"Time after dose (hrs)"
ylab<-"Plasma Conc(mmol/L)"
vpcfig2(vpctab=file1,vpcresult=file2,strata.names=strata.names,strata.subset=2:1,
       xlab=xlab,ylab=ylab)


# Example using the fy argument to transform y-axis setting y-ticks using scale_y_continuous(...)
tmp<-c(1,2,3,4,6)
yticks<-c(0.1,tmp,tmp*10,tmp*100,tmp*1000)
vpcfig2(vpctab=file1,vpcresult=file2,fy=function(y){log(y+1)})+
 scale_y_continuous(breaks=log(yticks+1),labels=yticks,minor_breaks=NULL)

## Not run: 
# Example changing to slog-scale using the ggplot2 function scale_y_log10
vpcfig2(vpctab=file1,vpcresult=file2)+scale_y_log10()

## End(Not run)
# Example changing y-axis label and adding figure title using ggplot2 function labs(...).
vpcfig2(vpctab=file1,vpcresult=file2)+labs(y="Modified y-label", title="New title")

Visual Predictive Check (VPC) based on raw Perl-speaks-NONMEM (PsN) generated VPC files.

Description

This function compile VPC simulated data from the vpc command in Pearl Speaks NONMEM (PsN). The complied data is then plotted (unless type=0) and returned (ggplot-object) and can then be further modifed.

Usage

vpcfig3(
  sdtab,
  vpcdir,
  use.model.path = TRUE,
  dv = "DV",
  idvs = "TIME",
  numerical = rep(TRUE, length(idvs)),
  strata = NULL,
  method = c("loess", "spline"),
  pred.corr = c("none", "pred-corr-pro p", "pred-corr-add"),
  maxNsim = NULL,
  knots = 5,
  minobs = 8,
  col.data = "gray20",
  cex.data = 1,
  pch.data = 19,
  alpha.data = 0.5,
  col.line = "grey20",
  lwd.line = 1,
  lty.line = 1,
  col.segm = "steelblue",
  alpha.segm = 0.6,
  type = 3,
  control = GOF.control(),
  ...
)

Arguments

Details

The sdtab and vpcdir can either be file name of the sdtab file and the folder name of the psn generated VPC, or can be data.frames of sdtab and simulation data loaded outside vpcfig3, see example below for the latter case.

Loading the sdtab and matrix file outside vpcfig2 is convenient when dosing multipe VPC plots or for VPC using new strata ( or idv) variables not included in the sdtab file, see example.

If vpcdir is given as a folder names, then a matrix.csv file is loaed from the /m1 subfolder of vpcdir.

The raw simulated results from the matrix file is loaded to compute mean of data with confidence intervals For numerical idv variables loess-smooth or linear spline is used on observed actual data and on simulated data. For categorical idv mean is computed for each unique value if idv

For method=spline, the actual number of knots(bins) is set as min(knots,N/minobs) where N is the number of observations.

Value

A ggplot object if type>0, or if type==0 a list of 3 data.frames, D1=individual data, D3=mean data, D5=mean model with confidence intervals

Examples

# Get path to the example files included in nonmem2R package
file1 <- system.file("extdata", "sdtab", package = "nonmem2R")
file2 <- system.file("extdata", "DV_matrix.csv", package = "nonmem2R")

#load sdtab and matrix file
sdtab<-read.table(file=file1,skip=1,header=TRUE)
vpc<-read.table(file=file2,header=FALSE,sep=",")

# VPC stratified by gender
vpcfig3(sdtab,vpc,dv="DV",idvs="TAD",strata="SEX")

# create new strata variable and do VPC
sdtab$age.group<-cut(sdtab$AGE,c(22,30,50))
vpcfig3(sdtab,vpc,dv="DV",idvs="TAD",strata="age.group")