Type: | Package |
Title: | Seamless 'Nonmem' Simulation Platform |
Version: | 0.2.4 |
Maintainer: | Philip Delff <philip@delff.dk> |
Description: | A complete and seamless 'Nonmem' simulation interface within R. Turns 'Nonmem' control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by 'Nonmem', eliminating manual work and risks of re-implementation of models in other tools. |
License: | MIT + file LICENSE |
RoxygenNote: | 7.3.2 |
Depends: | R (≥ 3.5.0) |
Imports: | data.table, NMdata (≥ 0.2.0), R.utils, MASS, fst, xfun |
Suggests: | testthat, knitr, rmarkdown, ggplot2, ggstance, patchwork, stringr, tracee, tidyvpc, kableExtra, coveffectsplot, NMcalc, waldo |
Enhances: | simpar |
Encoding: | UTF-8 |
Additional_repositories: | https://mpn.metworx.com/snapshots/stable/2024-09-23 |
BugReports: | https://github.com/nmautoverse/NMsim/issues |
Language: | en-US |
URL: | https://nmautoverse.github.io/NMsim/ |
NeedsCompilation: | no |
Packaged: | 2025-07-16 01:06:07 UTC; philipde |
Author: | Philip Delff [aut, cre], Brian Reilly [ctb], Sanaya Shroff [ctb], Boris Grinshpun [ctb] |
Repository: | CRAN |
Date/Publication: | 2025-07-16 02:40:02 UTC |
Add simulation (sample) records to dosing records
Description
Adds simulation events to all subjects in a data set. Copies over columns that are not varying at subject level (i.e. non-variying covariates). Can add simulation events relative to previous dosing time. This function was previously called 'addEVID2()'.
Usage
NMaddSamples(
data,
TIME,
TAPD,
CMT,
EVID,
DV,
col.id = "ID",
args.NMexpandDoses,
unique = TRUE,
by,
quiet = FALSE,
as.fun,
doses,
time.sim,
extras.are.covs
)
Arguments
data |
Nonmem-style data set. If using 'TAPD' an 'EVID' column must contain 1 for dosing records. |
TIME |
A numerical vector with simulation times. Can also be a data.frame in which case it must contain a 'TIME' column and is merged with 'data'. |
TAPD |
A numerical vector with simulation times, relative to previous dose. When this is used, 'data' must contain rows with 'EVID=1' events and a 'TIME' column. 'TAPD' can also be a data.frame in which case it must contain a 'TAPD' column and is merged with 'data'. |
CMT |
The compartment in which to insert the EVID=2 records. Required if 'CMT' is a column in 'data'. If longer than one, the records will be repeated in all the specified compartments. If a data.frame, covariates can be specified. |
EVID |
The value to put in the 'EVID' column for the created rows. Default is 2 but 0 may be prefered even for simulation. |
DV |
Optionally provide a single value to be assigned to the 'DV' column. The default is to assign nothing which will result in 'NA' as samples are stacked ('rbind') with 'data'. If you assign a different value in 'DV', the default value of 'EVID' changes to '0', and 'MDV' will be '0' instead of '1'. An example where this is useful is when generating datasets for '$DESIGN' where 'DV=0' is often used. |
col.id |
The name of the column in 'data' that holds the unique subject identifier. |
args.NMexpandDoses |
Only relevant - and likely not needed - if data contains ADDL and II columns. If those columns are included, 'NMaddSamples()' will use 'NMdata::NMexpanDoses()' to evaluate the time of each dose. Other than the 'data' argument, 'NMaddSamples()' relies on the default 'NMexpanDoses()' argument values. If this is insufficient, you can specify other argument values in a list, or you can call 'NMdata::NMexpanDoses()' manually before calling 'NMaddSamples()'. |
unique |
If 'TRUE' (default), events are reduced to unique time points before insertion. Sometimes, it's easier to combine sequences of time points that overlap (maybe across 'TIME' and 'TAPD'), and let 'NMaddSamples()' clean them. If you want to keep your duplicated events, use 'unique=FALSE'. |
by |
If |
quiet |
Suppress messages? Default is 'FALSE'. |
as.fun |
The default is to return data as a 'data.frame'. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use 'as.fun="data.table"'. The default can be configured using 'NMdataConf()'. |
doses |
Deprecated. Use 'data'. |
time.sim |
Deprecated. Use 'TIME'. |
extras.are.covs |
Deprecated. Use 'by'. |
Details
The resulting data set is ordered by ID, TIME, and EVID. You may have to reorder for your specific needs.
Value
A data.frame with dosing records only using column names in covs.data (from data) that are not in TIME.
All rows in TIME get reused for all matches by column names common with covs.data - the identified subject-level covariates (and col.id). This is with the exception of the TIME column itself, because in case of single dose, TIME would be carried over.
Examples
(doses1 <- NMcreateDoses(TIME=c(0,12,24,36),AMT=c(2,1)))
NMaddSamples(doses1,TIME=seq(0,28,by=4),CMT=2)
## two named compartments
dt.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
dt.cmt <- data.frame(CMT=c(2,3),analyte=c("parent","metabolite"))
res <- NMaddSamples(dt.doses,TIME=seq.time,CMT=dt.cmt)
## Separate sampling schemes depending on covariate values
dt.doses <- NMcreateDoses(TIME=data.frame(regimen=c("SD","MD","MD"),TIME=c(0,0,12)),AMT=10,CMT=1)
seq.time.sd <- data.frame(regimen="SD",TIME=seq(0,3))
seq.time.md <- data.frame(regimen="MD",TIME=c(0,12,24))
seq.time <- rbind(seq.time.sd,seq.time.md)
NMaddSamples(dt.doses,TIME=seq.time,CMT=2,by="regimen")
## All subjects get all samples
NMaddSamples(dt.doses,TIME=seq.time,by=FALSE,CMT=2)
## an observed sample scheme and additional simulation times
df.doses <- NMcreateDoses(TIME=0,AMT=50,addl=list(ADDL=2,II=24))
dense <- c(seq(1,3,by=.1),4:6,seq(8,12,by=4),18,24)
trough <- seq(0,3*24,by=24)
sim.extra <- seq(0,(24*3),by=2)
time.all <- c(dense,dense+24*3,trough,sim.extra)
time.all <- sort(unique(time.all))
dt.sample <- data.frame(TIME=time.all)
dt.sample$isobs <- as.numeric(dt.sample$TIME%in%c(dense,trough))
dat.sim <- NMaddSamples(dt.doses,TIME=dt.sample,CMT=2)
## TAPD - time after previous dose
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
NMaddSamples(df.doses,TAPD=seq.time,CMT=2)
## TIME and TAPD
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
NMaddSamples(df.doses,TIME=seq.time,TAPD=3,CMT=2)
## Using a custom DV value affects EVID and MDV
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(4)
NMaddSamples(df.doses,TAPD=seq.time,CMT=2,DV=0)
Easily and flexibly generate dosing records
Description
Columns will be extended by repeating last value of the column if needed in order to match length of other columns. Combinations of different columns can be generated by specifying covariates on the columns where the regimens differ.
Usage
NMcreateDoses(
TIME,
AMT,
EVID = 1,
CMT = 1,
ADDL = NULL,
II = NULL,
RATE = NULL,
SS = NULL,
addl = NULL,
addl.lastonly = TRUE,
col.id = "ID",
as.fun
)
Arguments
TIME |
The time of the dosing events. Required. |
AMT |
vector or data.frame with amounts amount. Required. |
EVID |
The event ID to use for doses. Default is to use EVID=1, but EVID might also be wanted. |
CMT |
Compartment number. Default is to dose into CMT=1. Use 'CMT=NA' or 'CMT=NULL' to omit in result. |
ADDL |
Number of additional dose events. Must be in combination with and consistent with II. Notice if of length 1, only applied to last event in each regimen. |
II |
Dosing frequency of additional events specified in 'ADDL'. See 'ADDL' too. |
RATE |
Infusion rate. Optional. |
SS |
steady-state flag. Optional. |
addl |
A list of ADDL and II that will be applied to last dose. This may be prefered if II and ADDL depend on covariates - see examples. Optional. |
addl.lastonly |
If ADDL and II are of length 1, apply only to last event of a regimen? The default is 'TRUE'. |
col.id |
Default is to denote the dosing regimens by an ID column. The name of the column can be modified using this argument. Use 'col.id=NA' to omit the column altogether. The latter may be wanted if repeating the regimen for a number of subjects after running 'NMcreateDoses()'. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Details
Only TIME and AMT are required. AMT, RATE, SS, II, ADDL, CMT are of length 1 or longer. Those not of max length 1 are repeated. If TIME is longer than those, they are extended to match length of TIME. All these arguments can be data.frames with additional columns that define distinct dosing regimens - with distinct subject ids. However, if covariates are applied to ADDL+II, see the addl argument and see examples.
Allowed combinations of AMT, RATE, SS, II here: https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.12404
Value
A data.frame with dosing events
Examples
library(data.table)
## Users should not use setDTthreads. This is for CRAN to only use 1 core.
data.table::setDTthreads(1)
## arguments are expanded - makes loading easy
NMcreateDoses(TIME=c(0,12,24,36),AMT=c(2,1))
## Different doses by covariate
NMcreateDoses(TIME=c(0,12,24),AMT=data.table(AMT=c(2,1,4,2),DOSE=c(1,2)))
## Make Nonmem repeat the last dose. This is a total of 20 dosing events.
## The default, addl.lastonly=TRUE means if ADDL and II are of
## length 1, they only apply to last event.
NMcreateDoses(TIME=c(0,12),AMT=c(2,1),ADDL=9*2,II=12)
dt.amt <- data.table(DOSE=c(100,400))
## multiple dose regimens.
## Specifying the time points explicitly
dt.amt <- data.table(AMT=c(200,100,800,400)*1000,DOSE=c(100,100,400,400))
doses.md.1 <- NMcreateDoses(TIME=seq(0,by=24,length.out=7),AMT=dt.amt)
doses.md.1$dose <- paste(doses.md.1$DOSE,"mg")
doses.md.1$regimen <- "QD"
doses.md.1
## or using ADDL+II
dt.amt <- data.table(AMT=c(200,100,800,400)*1000,DOSE=c(100,100,400,400))
doses.md.2 <- NMcreateDoses(TIME=c(0,24),AMT=dt.amt,addl=data.table(ADDL=c(0,5),II=c(0,24)))
doses.md.2$dose <- paste(doses.md.2$DOSE,"mg")
doses.md.2$regimen <- "QD"
doses.md.2
## ADDL and II can be wrapped in a data.frame. This allows including covariates
NMcreateDoses(TIME=c(0,12),AMT=c(2,1),addl=data.frame(ADDL=c(NA,9*2),II=c(NA,12),trt=c("A","B")))
Create text lines for OMEGA and SIGMA Nonmem sections
Description
Create text lines for OMEGA and SIGMA Nonmem sections
Usage
NMcreateMatLines(omegas, as.one.block = FALSE, fix = FALSE, type)
Arguments
omegas |
A data.table with at least 'i', 'j' and 'value' columns. See 'NMdata::NMreadExt' and the pars element returned by that function. Must at least have columns 'i', 'j', 'value', 'iblock', 'blocksize', 'FIX'. |
as.one.block |
If 'TRUE', all values are printed as one block. If 'FALSE' (default), matrix will be separeted into blocks based on position non-zero off-diagonal values. Generally speaking, for 'OMEGA' matrices (var-cov matrices for ETAs), this should be 'FALSE', and for variance-covariance matrices (like 'THETAP'), this should be 'TRUE'. |
fix |
Include 'FIX' for all lines? If 'FALSE', fixing will not be modified. Notice, 'fix=TRUE' will fix everything - individual parameters cannot be controlled. For finer control and way more features, see 'NMdata::NMwriteInits()'. |
type |
The matrix type. 'OMEGA' or 'SIGMA' - case in-sensitive. Will be used to print say '$OMEGA' in front of each line. |
Value
Character vector
Execute Nonmem and archive input data with model files
Description
Execute Nonmem from within R - optionally but by default in parallel. Archiving the input data ensures that postprocessing can still be reproduced if the input data files should be updated.
Usage
NMexec(
files,
file.pattern,
dir,
sge = TRUE,
input.archive,
nc,
dir.data = NULL,
wait = FALSE,
path.nonmem,
update.only = FALSE,
fun.post,
method.execute,
nmfe.options,
dir.psn,
args.psn.execute,
files.needed,
clean = 1,
backup = TRUE,
quiet = FALSE,
nmquiet = FALSE,
system.type
)
Arguments
files |
File paths to the models (control streams) to run nonmem on. See file.pattern too. |
file.pattern |
Alternatively to files, you can supply a regular expression which will be passed to list.files as the pattern argument. If this is used, use dir argument as well. Also see data.file to only process models that use a specific data file. |
dir |
If file.pattern is used, dir is the directory to search for control streams in. |
sge |
Use the sge queing system. Default is TRUE. Disable for quick models not to wait for the queue to run the job. |
input.archive |
A function of the model file path to generate the path in which to archive the input data as RDS. Set to FALSE not to archive the data. |
nc |
Number of cores to use if sending to the cluster. This
will only be used if |
dir.data |
The directory in which the data file is stored. This is normally not needed as data will be found using the path in the control stream. This argument may be removed in the future since it should not be needed. |
wait |
Wait for process to finish before making R console available again? This is useful if calling NMexec from a function that needs to wait for the output of the Nonmem run to be available for further processing. |
path.nonmem |
The path to the nonmem executable. Only used if
|
update.only |
Only run model(s) if control stream or data updated since last run? |
fun.post |
A function of the path to the control stream ('file.mod') that generates bash code to be evaluated once Nonmem is done. This can be used to automatically run a goodness-of-fit script or a simulation script after model estimation. |
method.execute |
How to run Nonmem. Must be one of 'psn', 'nmsim', or 'direct'.
See 'sge' as well. |
nmfe.options |
additional options that will be passed to nmfe. It is only used when path.nonmem is available (directly or using 'NMdataConf()'). Default is "-maxlim=2" For PSN, see 'args.psn.execute'. |
dir.psn |
The directory in which to find PSN executables. This is only needed if these are not searchable in the system path, or if the user should want to be explicit about where to find them (i.e. want to use a specific installed version of PSN). |
args.psn.execute |
A character string with arguments passed to execute. Default is "-model_dir_name -nm_output=coi,cor,cov,ext,phi,shk,xml". |
files.needed |
In case method.execute="nmsim", this argument specifies files to be copied into the temporary directory before Nonmem is run. Input control stream and simulation input data does not need to be specified. |
clean |
The degree of cleaning (file removal) to do after Nonmem execution. If 'method.execute=="psn"', this is passed to PSN's 'execute'. If 'method.execute=="nmsim"' a similar behavior is applied, even though not as granular. NMsim's internal method only distinguishes between 0 (no cleaning), any integer 1-4 (default, quite a bit of cleaning) and 5 (remove temporary dir completely). |
backup |
Before running, should existing results files be backed up in a sub directory? If not, the files will be deleted before running. |
quiet |
Suppress messages on what NMexec is doing? Default is FALSE. |
nmquiet |
Suppress terminal output from 'Nonmem'. This is likely to only work on linux/unix systems. |
system.type |
A charachter string, either \"windows\" or
\"linux\" - case insensitive. Windows is only experimentally
supported. Default is to use |
Details
Use this to read the archived input data when retrieving
the nonmem results:
NMdataConf(file.data=inputArchiveDefault)
Since 'NMexec' will typically not be used for simulations directly ('NMsim' is the natural interface for that purpose), the default method for 'NMexec' is currently to use 'method.execute="psn"' which is at this point the only of the methods that allow for multi-core execution of a single Nonmem job (NB: 'method.execute="NMsim"' can run multiple jobs in parallel which is normally sufficient for simulations).
Value
NULL (invisibly)
Examples
file.mod <- "run001.mod"
## Not run:
## run locally - not on cluster
NMexec(file.mod,sge=FALSE)
## run on cluster with 16 cores. 64 cores is default
NMexec(file.mod,nc=16)
## submit multiple models to cluster
multiple.models <- c("run001.mod","run002.mod")
NMexec(multiple.models,nc=16)
## run all models called run001.mod - run099.mod if updated. 64 cores to each.
NMexec(file.pattern="run0..\\.mod",dir="models",nc=16,update.only=TRUE)
## End(Not run)
Execute Nonmem inside a dedicated directory
Description
Like PSN's execute with less features. But easier to control from
NMexec. NMexecDirectory is not intended to be run by the user. Use
NMexec
or NMsim
instead.
Usage
NMexecDirectory(
file.mod,
path.nonmem,
files.needed,
dir.data = "..",
system.type,
clean,
sge = nc > 1,
nc = 1,
pnm,
nmfe.options,
fun.post = NULL
)
Arguments
file.mod |
Path to a Nonmem input control stream. |
path.nonmem |
Path to Nonmem executable. You may want to
control this with |
files.needed |
Files needed to run the control stream. This cold be a .phi file from which etas will be read. Notice, input data set will be handled automatically, you do not need to specify that. |
dir.data |
If NULL, data will be copied into the temporary directory, and Nonmem will read it from there. If not NULL, dir.data must be the relative path from where Nonmem is run to where the input data file is stored. This would be ".." if the run directory is created in a directory where the data is stored. |
clean |
The degree of cleaning (file removal) to do after Nonmem execution. If 'method.execute=="psn"', this is passed to PSN's 'execute'. If 'method.execute=="nmsim"' a similar behavior is applied, even though not as granular. NMsim's internal method only distinguishes between 0 (no cleaning), any integer 1-4 (default, quite a bit of cleaning) and 5 (remove temporary dir completely). |
Value
A bash shell script for execution of Nonmem
Versatile text extractor from Nonmem (input or output) control streams
Description
If you want to extract input sections like $PROBLEM, $DATA etc, see NMreadSection. This function is more general and can be used to extract eg result sections.
Usage
NMextractText(
file,
lines,
text,
section,
char.section,
char.end = char.section,
return = "text",
keep.empty = FALSE,
keep.name = TRUE,
keep.comments = TRUE,
as.one = TRUE,
clean.spaces = FALSE,
simplify = TRUE,
match.exactly = TRUE,
type = "mod",
linesep = "\n",
keepEmpty,
keepName,
keepComments,
asOne
)
Arguments
file |
A file path to read from. Normally a .mod or .lst. See lines and text as well. |
lines |
Text lines to process. This is an alternative to using the file and text arguments. |
text |
Use this argument if the text to process is one long character string, and indicate the line separator with the linesep argument. Use only one of file, lines, and text. |
section |
The name of section to extract. Examples: "INPUT", "PK", "TABLE", etc. It can also be result sections like "MINIMIZATION". |
char.section |
The section denoted as a string compatible with regular expressions. "$" (remember to escape properly) for sections in .mod files, "0" for results in .lst files. |
char.end |
A regular expression to capture the end of the section. The default is to look for the next occurrence of char.section. |
return |
If "text", plain text lines are returned. If "idx", matching line numbers are returned. "text" is default. |
keep.empty |
Keep empty lines in output? Default is FALSE. Notice, comments are removed before empty lines are handled if 'keep.comments=TRUE'. |
keep.name |
Keep the section name in output (say, "$PROBLEM") Default is TRUE. It can only be FALSE, if return="text". |
keep.comments |
Default is to keep comments. If FALSE, the will be removed. |
as.one |
If multiple hits, concatenate into one. This will most often be relevant with name="TABLE". If FALSE, a list will be returned, each element representing a table. Default is TRUE. So if you want to process the tables separately, you probably want FALSE here. |
clean.spaces |
If TRUE, leading and trailing are removed, and multiplied succeeding white spaces are reduced to single white spaces. |
simplify |
If asOne=FALSE, do you want the result to be simplified if only one table is found? Default is TRUE which is desirable for interactive analysis. For programming, you probably want FALSE. |
match.exactly |
Default is to search for exact matches of 'section'. If FALSE, only the first three characters are matched. E.G., this allows "ESTIMATION" to match "ESTIMATION" or "EST". |
type |
Either mod, res or NULL. mod is for information that is given in .mod (.lst file can be used but results section is disregarded). If NULL, NA or empty string, everything is considered. |
linesep |
If using the text argument, use linesep to indicate how lines should be separated. |
keepEmpty |
Deprecated. See keep.empty. |
keepName |
Deprecated. See keep.name. |
keepComments |
Deprecated. See keep.comments. |
asOne |
Deprecated. See as.one. |
Details
This function is planned to get a more general name and then be called by NMreadSection.
Value
character vector with extracted lines.
See Also
Other Nonmem:
NMreadSection()
Examples
library(NMdata)
NMreadSection(system.file("examples/nonmem/xgxr001.lst", package = "NMdata"),section="DATA")
Generate PNM file for sge clusters
Description
Generate PNM file for sge clusters
Usage
NMgenPNM(nc, file)
Arguments
nc |
number of cores wanted |
file |
The file path to write the result to |
Value
The file path (character string)
Tabulate information from parameter sections in control streams
Description
Tabulate information from parameter sections in control streams
Usage
NMreadInits(file, lines, section, return = "pars", as.fun)
Arguments
file |
Path to a control stream. See 'lines' too. |
lines |
A control stream as text lines. Use this or 'file'. |
section |
The section to read. Typically, "theta", "omega", or "sigma". Default is those three. |
return |
By default (when |
as.fun |
See ?NMscanData |
Value
A 'data.frame' with parameter values. If 'return="all"', a list of three tables.
Extract sections of Nonmem control streams
Description
This is a very commonly used wrapper for the input part of the model file. Look NMextractText for more general functionality suitable for the results part too.
Usage
NMreadSection(
file = NULL,
lines = NULL,
text = NULL,
section,
return = "text",
keep.empty = FALSE,
keep.name = TRUE,
keep.comments = TRUE,
as.one = TRUE,
clean.spaces = FALSE,
simplify = TRUE,
keepEmpty,
keepName,
keepComments,
asOne,
...
)
Arguments
file |
A file path to read from. Normally a .mod or .lst. See lines also. |
lines |
Text lines to process. This is an alternative to using the file argument. |
text |
Deprecated, use 'lines'. Use this argument if the text to process is one long character string, and indicate the line separator with the linesep argument (handled by NMextractText). Use only one of file, lines, and text. |
section |
The name of section to extract without "$". Examples: "INPUT", "PK", "TABLE", etc. Not case sensitive. |
return |
If "text", plain text lines are returned. If "idx", matching line numbers are returned. "text" is default. |
keep.empty |
Keep empty lines in output? Default is FALSE. Notice, comments are removed before empty lines are handled if 'keep.comments=TRUE'. |
keep.name |
Keep the section name in output (say, "$PROBLEM") Default is FALSE. It can only be FALSE, if return="text". |
keep.comments |
Default is to keep comments. If FALSE, the will be removed. See keep.empty too. Notice, there is no way for NMreadSection to keep comments and also drop lines that only contain comments. |
as.one |
If multiple hits, concatenate into one. This will most often be relevant with name="TABLE". If FALSE, a list will be returned, each element representing a table. Default is TRUE. So if you want to process the tables separately, you probably want FALSE here. |
clean.spaces |
If TRUE, leading and trailing are removed, and multiplied succeeding white spaces are reduced to single white spaces. |
simplify |
If asOne=FALSE, do you want the result to be simplified if only one section is found? Default is TRUE which is desirable for interactive analysis. For programming, you probably want FALSE. |
keepEmpty |
Deprecated. See keep.empty. |
keepName |
Deprecated. See keep.name. |
keepComments |
Deprecated. See keep.comments. |
asOne |
Deprecated. See as.one. |
... |
Additional arguments passed to NMextractText |
Value
character vector with extracted lines.
See Also
Other Nonmem:
NMextractText()
Examples
library(NMdata)
NMreadSection(system.file("examples/nonmem/xgxr001.lst", package="NMdata"),section="DATA")
Read simulation results based on NMsim's track of model runs
Description
Read simulation results based on NMsim's track of model runs
Usage
NMreadSim(
x,
check.time = FALSE,
dir.sims,
wait = FALSE,
quiet = FALSE,
progress,
skip.missing = FALSE,
rm.tmp = FALSE,
as.fun
)
Arguments
x |
Path to the simulation-specific rds file generated by NMsim, typically called 'NMsim_MetaData.rds'. Can also be a table of simulation runs as stored in 'rds' files by 'NMsim'. The latter should almost never be used. |
check.time |
If found, check whether 'fst' file modification time is newer than 'rds' file. The 'fst' is generated based on information in ‘rds', but notice that some systems don’t preserve the file modification times. Becasue of that, 'check.time' is 'FALSE' by default. |
dir.sims |
By default, 'NMreadSim' will use information about the relative path from the results table file ('_MetaData.rds') to the Nonmem simulation results. If these paths have changed, or for other reasons this doesn't work, you can use the 'dir.sims' argument to specify where to find the Nonmem simulation results. If an '.fst' file was already generated and is found next to the '_MetaData.rds', the path to the Nonmem simulation results is not used. |
wait |
If simulations seem to not be done yet, wait for them to finish? If not, an error will be thrown. If you choose to wait, the risk is results never come. 'NMreadSim' will be waiting for an 'lst' file. If Nonmem fails, it will normally generate an 'lst' file. But if 'NMTRAN' fails (checks of control stream prior to running Nonmem), the 'lst' file is not generated. Default is not to wait. |
quiet |
Turn off some messages about what is going on? Default is to report the messages. |
progress |
Track progress? Default is 'TRUE' if 'quiet' is FALSE and more than one model is being read. The progress tracking is based on the number of models completed/read, not the status of the individual models. |
skip.missing |
Skip models where results are not available? Default is 'FALSE' meaning an error will be thrown if one or more models do not have completed results. |
rm.tmp |
If results are read successfully, remove temporary simulation results files? This can be useful after a script is developed and intermediate debugging information is not needed. It cleans up and saves significant disk space. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Value
A data set of class defined by as.fun
Read simulation results from rds objects and/or NMsimModTab objects
Description
Read simulation results from rds objects and/or NMsimModTab objects
Usage
NMreadSimModTab(
x,
check.time = FALSE,
dir.sims,
wait = FALSE,
skip.missing = FALSE,
quiet = FALSE,
progress,
read.fst = NULL,
fast.tables = NULL,
carry.out = NULL,
as.fun
)
Arguments
x |
Path to the simulation-specific rds file generated by NMsim, typically called 'NMsim_MetaData.rds'. Can also be a table of simulation runs as stored in 'rds' files by 'NMsim'. The latter should almost never be used. |
check.time |
If found, check whether 'fst' file modification time is newer than 'rds' file. The 'fst' is generated based on information in ‘rds', but notice that some systems don’t preserve the file modification times. Becasue of that, 'check.time' is 'FALSE' by default. |
dir.sims |
By default, 'NMreadSim' will use information about the relative path from the results table file ('_MetaData.rds') to the Nonmem simulation results. If these paths have changed, or for other reasons this doesn't work, you can use the 'dir.sims' argument to specify where to find the Nonmem simulation results. If an '.fst' file was already generated and is found next to the '_MetaData.rds', the path to the Nonmem simulation results is not used. |
wait |
If simulations seem to not be done yet, wait for them to finish? If not, an error will be thrown. If you choose to wait, the risk is results never come. 'NMreadSim' will be waiting for an 'lst' file. If Nonmem fails, it will normally generate an 'lst' file. But if 'NMTRAN' fails (checks of control stream prior to running Nonmem), the 'lst' file is not generated. Default is not to wait. |
skip.missing |
Skip models where results are not available? Default is 'FALSE' meaning an error will be thrown if one or more models do not have completed results. |
quiet |
Turn off some messages about what is going on? Default is to report the messages. |
progress |
Track progress? Default is 'TRUE' if 'quiet' is FALSE and more than one model is being simulated. The progress tracking is based on the number of models completed, not the status of the individual models. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Read simulation results from an rds or a NMsimModTab object
Description
Read simulation results from an rds or a NMsimModTab object
Usage
NMreadSimModTabOne(
modtab,
check.time = FALSE,
dir.sims,
wait = FALSE,
quiet = FALSE,
skip.missing = FALSE,
progress,
read.fst = NULL,
fast.tables = NULL,
carry.out = NULL,
as.fun
)
Arguments
check.time |
If found, check whether 'fst' file modification time is newer than 'rds' file. The 'fst' is generated based on information in ‘rds', but notice that some systems don’t preserve the file modification times. Becasue of that, 'check.time' is 'FALSE' by default. |
dir.sims |
By default, 'NMreadSim' will use information about the relative path from the results table file ('_MetaData.rds') to the Nonmem simulation results. If these paths have changed, or for other reasons this doesn't work, you can use the 'dir.sims' argument to specify where to find the Nonmem simulation results. If an '.fst' file was already generated and is found next to the '_MetaData.rds', the path to the Nonmem simulation results is not used. |
wait |
If simulations seem to not be done yet, wait for them to finish? If not, an error will be thrown. If you choose to wait, the risk is results never come. 'NMreadSim' will be waiting for an 'lst' file. If Nonmem fails, it will normally generate an 'lst' file. But if 'NMTRAN' fails (checks of control stream prior to running Nonmem), the 'lst' file is not generated. Default is not to wait. |
quiet |
Turn off some messages about what is going on? Default is to report the messages. |
skip.missing |
Skip models where results are not available? Default is 'FALSE' meaning an error will be thrown if one or more models do not have completed results. |
progress |
Track progress? Default is 'TRUE' if 'quiet' is FALSE and more than one model is being read. The progress tracking is based on the number of models completed/read, not the status of the individual models. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Read simulation results from data.frames or fst files
Description
Read simulation results from data.frames or fst files
Usage
NMreadSimRes(x)
Arguments
x |
a data set or a fst file |
read one sim element. This will be run in lapply in NMreadSim.
Description
read one sim element. This will be run in lapply in NMreadSim.
Usage
NMreadSimResOne(x)
Arguments
x |
A path to an fst file or a data set |
Value
A data.table
Read SIZES info from a control stream
Description
Read SIZES info from a control stream
Usage
NMreadSizes(file.mod = NULL, lines = NULL)
Arguments
file.mod |
Control stream path. |
lines |
Character vector with control stream file. |
Value
A list with SIZES parameter values
Replace initial values in Nonmem control stream
Description
Replace initial values in Nonmem control stream
Usage
NMreplaceInits(inits, fix = FALSE, ...)
Arguments
inits |
A data.frame with new initial estimates, same style as returned by NMdata::NMreadExt. Column' par.type' can contain elements THETA, OMEGA, SIGMA. |
... |
Passed to NMdata::NMwriteSection. This is important for NMreplaceInits to run at all. |
Value
The modified control stream
Internal function to run Nonmem on linux
Description
Internal function to run Nonmem on linux
Usage
NMrunLin(
fn.mod,
dir.mod.abs,
exts.cp,
meta.tables,
path.nonmem,
clean,
sge,
nc,
pnm,
nmfe.options,
fun.post = NULL
)
Arguments
fn.mod |
Just the file name, not including path |
Add seed string to simulation model data.table
Description
This is an internal NMsim function.
Usage
NMseed(models, nseeds, dist, values, fun.seed = seedFunDefault)
Arguments
models |
A data.frame containing model paths etc as created
by |
nseeds |
Number of seeds in each simulation control stream. Default is to match length of dist. |
dist |
Distribution of random sources. These character
strings will be pasted directly into the Nonem control streams
after the seed values. Default is "" which means one normal
distribution. |
values |
Optionally, seed values. This can be a data.frame with as many columns as random sources. |
Value
An updated data.table with simulation model information including seed strings.
Simulate from an estimated Nonmem model
Description
Supply a data set and an estimation input control stream, and NMsim can create neccesary files (control stream, data files), run the simulation and read the results. It has additional methods for other simulation types available, can do multiple simulations at once and more. Please see vignettes for an introduction to how to get the most out of this.
Usage
NMsim(
file.mod,
data,
subproblems = NULL,
reuse.results = FALSE,
seed.R,
seed.nm,
name.sim,
table.vars,
table.options,
table.format = "s1PE16.9",
carry.out = TRUE,
method.sim = NMsim_default,
typical = FALSE,
inits,
modify,
filters,
sizes,
path.nonmem = NULL,
sge = FALSE,
nc = 1,
execute = TRUE,
script = NULL,
transform = NULL,
order.columns = TRUE,
method.execute,
nmfe.options,
nmrep,
col.flagn = FALSE,
dir.psn,
args.psn.execute,
args.NMscanData,
as.fun,
system.type = NULL,
dir.sims,
dir.res,
file.res,
wait,
text.sim = "",
auto.dv = TRUE,
clean,
sim.dir.from.scratch = TRUE,
create.dirs = TRUE,
quiet = FALSE,
nmquiet,
progress,
check.mod = TRUE,
format.data.complete = "rds",
text.table,
suffix.sim,
seed,
file.ext = NULL,
method.update.inits,
modify.model,
list.sections,
...
)
Arguments
file.mod |
Path(s) to the input control stream(s) to run the simulation on. The output control stream is for now assumed to be stored next to the input control stream and ending in .lst instead of .mod. The .ext file must also be present. If simulating known subjects, the .phi is necessary too. |
data |
The simulation data as a |
subproblems |
Number of subproblems to use as
|
reuse.results |
If simulation results found on file, should they be used? If TRUE and reading the results fail, the simulations will still be rerun. |
seed.R |
A value passed to |
seed.nm |
Control Nonmem seeds. If a numeric, a vector or a
'data.frame', these are used as the the seed values (a single
value or vector will be recycled so make sure the dimesnsions
are right, the number of columns in a Default is to draw seeds betwen 0 and 2147483647 (the values supported by Nonmem) for each simulation. You can pass a function that will be evaluated (say to choose a different pool of seeds to draw from). To avoid changing an exisiting seed in a control stream, use
In case |
name.sim |
Give all filenames related to the simulation a suffix. A short string describing the sim is recommended like "ph3_regimens". |
table.vars |
Variables to be printed in output table as a
character vector or a space-separated string of variable
names. The default is to export the same tables as listed in
the input control stream. If |
table.options |
A character vector or a string of
space-separated options. Only used if |
table.format |
A format for '$TABLE'. Only used if 'table.vars' is provided. Default is "s1PE16.9". NMsim needs a high-resolution format. The Nonmem default "s1PE11.4" is insufficient for simulation data sets of 1e5 rows or more. |
carry.out |
Variables from input data that should be included in results. Default is to include everything. If working with large data sets, it may be wanted to provide a subset of the columns here. If doing very large simulations, this may also be a way to save memory. |
method.sim |
A function (not quoted) that creates the
simulation control stream and other necessary files for a
simulation based on the estimation control stream, the data,
etc. The default is called |
typical |
Run with all ETAs fixed to zero? Technically all
ETAs=0 is obtained by replacing |
inits |
Control the parameter values. 'inits' is a list. The 'method' element controls which method is used to do this, and this corresponds to the old 'method.update.inits' argument. If using the new 'method=nmsim' you can specify parameter values, fix/unfix them, and edit lower and upper limits for estimation.
See also 'file.ext' which can now be handled by 'inits' too. This change collects the update of the "initial" parameter values into one interface rather than multiple arguments. |
modify |
Named list of additional control stream section edits. Note, these can be functions that define how to edit sections. This is an advanced feature which is not needed to run most simulations. It is however powerful for some types of analyses, like modifying parameter values. See vignettes for further information. |
filters |
Edit data filters ('IGNORE'/'ACCEPT' statements) before running model. This should normally only be used if no data set is provided. It can be useful if simulating for a VPC but a different subset of data needs to be simulated than the one used for estimation. A common example on this is inclusion of BLQ's in the VPC even if they were excluded in the estimation. See '?NMreadFilters' which returns a table you can edit and pass to 'filters'. You can also just pass a string representing the full set of filters to be used. If you pass a string, consider including "IGN=@" to avoid character rows, like the column headers. |
sizes |
If needed, adjust the '$SIZES' section by providing a list of arguments to 'NMupdateSizes()'. Example: ‘sizes=list(PD=80)'. See '?NMupdateSizes' for details. Don’t use arguments like 'file.mod' and 'newfile' which are handled internally. |
path.nonmem |
The path to the Nonmem executable to use. The could be something like "/usr/local/NONMEM/run/nmfe75" (which is a made up example). No default is available. You should be able to figure this out through how you normally execute Nonmem, or ask a colleague. |
sge |
Submit to cluster? Default is not to, but this is very useful if creating a large number of simulations, e.g. simulate with all parameter estimates from a bootstrap result. |
nc |
Number of cores used in parallelization. Only used if 'sge=TRUE'. |
execute |
Execute the simulation or only prepare it? 'execute=FALSE' can be useful if you want to do additional tweaks or simulate using other parameter estimates. |
script |
The path to the script where this is run. For stamping of dataset so results can be traced back to code. |
transform |
A list defining transformations to be applied after the Nonmem simulations and before plotting. For each list element, its name refers to the name of the column to transform, the contents must be the function to apply. |
order.columns |
reorder columns by calling
|
method.execute |
Specify how to call Nonmem. Options are "psn" (PSN's execute), "nmsim" (an internal method similar to PSN's execute), and "direct" (just run Nonmem directly and dump all the temporary files). "nmsim" has advantages over "psn" that makes it the only supported method when type.sim="NMsim_EBE". "psn" has the simple advantage that the path to nonmem does not have to be specified if "execute" is in the system search path. So as long as you know where your Nonmem executable is, "nmsim" is recommended. The default is "nmsim" if path.nonmem is specified, and "psn" if not. |
nmfe.options |
additional options that will be passed to nmfe. It is only used when path.nonmem is available (directly or using 'NMdataConf()'). Default is "-maxlim=2" For PSN, see 'args.psn.execute'. |
nmrep |
Include 'NMREP' as counter of subproblems? The default is to do so if 'subproblems>0'. This will insert a counter called 'NMREP' in the '$ERROR' section and include that in the output table(s). At this point, nothing is done to avoid overwriting existing variables. |
col.flagn |
Only used if 'data' is provided. Use this if you are including an exclusion flag column in data. However, what NMsim will then do is to require that column to equal '0' (zero) for the rows to be simulated. It is often better to subset the data before simulation. See 'filters' too. |
dir.psn |
The directory in which to find PSN's executables ('execute' and 'update_inits'). The default is to rely on the system's search path. So if you can run 'execute' and 'update_inits' by just typing that in a terminal, you don't need to specify this unless you want to explicitly use a specific installation of PSN on your system. |
args.psn.execute |
A charachter string that will be passed as arguments PSN's 'execute'. The default is "-model_dir_name -nm_output=coi,cor,cov,ext,phi,shk,xml -nmfe_options=\"-maxlim=2\"" in addition to the "-clean" based on the 'clean' argument. Notice, if 'path.nonmem' is provided, the default is not to use PSN. |
args.NMscanData |
If |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
system.type |
A charachter string, either "windows" or
"linux" - case insensitive. Windows is only experimentally
supported. Default is to use |
dir.sims |
The directory in which NMsim will store all generated files. Default is to create a folder called 'NMsim' next to 'file.mod'. |
dir.res |
Provide a path to a directory in which to save rds files with paths to results. Default is to use dir.sims. After running 'NMreadSim()' on these files, the original simulation files can be deleted. Hence, providing both 'dir.sims' and 'dir.res' provides a structure that is simple to clean. 'dir.sims' can be purged when 'NMreadSim' has been run and only small 'rds' and 'fst' files will be kept in 'dir.res'. Notice, in case multiple models are simulated, multiple 'rds' (to be read with 'NMreadSim()') files will be created by default. In cases where multiple models are simulated, see 'file.res' to get just one file refering to all simulation results. |
file.res |
Path to an rds file that will contain a table of
the simulated models and other metadata. This is needed for
subsequently retrieving all the results using
'NMreadSim()'. The default is to create a file called
'NMsim_..._MetaData.rds' under the |
wait |
Wait for simulations to finish? Default is to do so if simulations are run locally but not to if they are sent to the cluster. Waiting for them means that the results will be read when simulations are done. If not waiting, path(s) to 'rds' files to read will be returned. Pass them through 'NMreadSim()'. Conveniently, NMreadSim() also takes the 'wait' argument too, allowing flexibility to run Nonmem in the background, and then read the results, still waiting for Nonmem to finish. |
text.sim |
A character string to be pasted into $SIMULATION. This must not contain seed or SUBPROBLEM which is handled separately. Default is to include "ONLYSIM". You cannot avoid that using 'text.sim'. If needed, you can use 'onlysim=FALSE' which will be passed to 'NMsim_default()'. |
auto.dv |
Add a column called 'DV' to input data sets if a
column of that name is not found? Nonmem is generally
dependent on a 'DV' column in input data but this is typically
uninformative in simulation data sets and hence easily
forgotten when generating simulation data sets. If
|
clean |
The degree of cleaning (file removal) to do after Nonmem execution. If 'method.execute=="psn"', this is passed to PSN's 'execute'. If 'method.execute=="nmsim"' a similar behavior is applied, even though not as granular. NMsim's internal method only distinguishes between 0 (no cleaning), any integer 1-4 (default, quite a bit of cleaning) and 5 (remove temporary dir completely). |
sim.dir.from.scratch |
If TRUE (default) this will wipe the
simulation directory before running new simulations. The
directory that will be emptied is _not_ dir.sims where you may
keep many or all your simulations. It is the subdirectory
named based on the run name and |
create.dirs |
If the directories specified in dir.sims and dir.res do not exists, should it be created? Default is TRUE. |
quiet |
If TRUE, messages from what is going on will be suppressed. |
nmquiet |
Silent console messages from Nonmem? The default behaviour depends. It is FALSE if there is only one model to execute and 'progress=FALSE'. |
progress |
Track progress? Default is 'TRUE' if 'quiet' is FALSE and more than one model is being simulated. The progress tracking is based on the number of models completed, not the status of the individual models. |
check.mod |
Check the provided control streams for contents that may cause issues for simulation. Default is 'TRUE', and it is only recommended to disable this if you are fully aware of such a feature of your control stream, you know how it impacts simulation, and you want to get rid of warnings. |
format.data.complete |
For development purposes - users do not need this argument. Controls what format the complete input data set is saved in. Possible values are 'rds' (default), 'fst' (experimental) and 'csv'. 'fst' may be faster and use less disk space but factor levels may be lost from input data to output data. 'csv' will also lead to loss of additional information such as factor levels. |
text.table |
Deprecated. Use 'table.vars' and 'table.options' instead. |
suffix.sim |
Deprecated. Use name.sim instead. |
seed |
Deprecated. See |
file.ext |
Deprecated. Use 'inits=list(file.ext="path/to/file.ext")' instead. Optionally provide a parameter estimate file from Nonmem. This is normally not needed since 'NMsim' will by default use the ext file stored next to the input control stream (replacing the file name extension with '.ext'). If using method.update.inits="psn", this argument cannot be used. |
method.update.inits |
Deprecated, please migrate to 'inits' instead. The initial values of all parameters are by updated from the estimated model before running the simulation. NMsim can do this with a native function or use PSN to do it - or the step can be skipped to not update the values. |
modify.model |
Deprecated. Use modify instead. |
list.sections |
Deprecated. Use modify instead. |
... |
Additional arguments passed to |
Details
Loosely speaking, the argument method.sim
defines
_what_ NMsim will do, method.execute
define _how_ it
does it. method.sim
takes a function that converts an
estimation control stream into whatever should be
run. Features like replacing '$INPUT', '$DATA', '$TABLE', and
handling seeds are NMsim features that are done in addition to
the method.sim
. Also the modeify.model
argument
is handled in addition to the method.sim
. The
subproblems
and seed.nm
arguments are available
to all methods creating a $SIMULATION
section.
Notice, the following functions are internally available to
'NMsim' so you can run them by say method.sim=NMsim_EBE
without quotes. To see the code of that method, type
NMsim_EBE
.
-
NMsim_default
The default behaviour. Replaces any $ESTIMATION and $COVARIANCE sections by a $SIMULATION section. -
NMsim_asis
The simplest of all method. It does nothing (but again,NMsim
handles '$INPUT', '$DATA', '$TABLE' and more. Use this for instance if you already created a simulation (or estimation actually) control stream and want NMsim to run it on different data sets. -
NMsim_EBE
Simulates _known_ ETAs. By default, the ETA values are automatically taken from the estimation run. This is what is refered to as emperical Bayes estimates, hence the name of the method "NMsim_EBE". However, the user can also provide a different '.phi' file which may contain simulated ETA values (see the 'file.phi' argument). ID values in the simulation data set must match ID values in the phi file for this step to work. If refering to estimated subjects, the .phi file from the estimation run must be found next to the .lst file from the estimation with the same file name stem (say 'run1.lst' and 'run1.phi'). Again, ID values in the (simulation) input data must be ID values that were used in the estimation too. The method Runs an$ESTIMATION MAXEVAL=0
but pulls in ETAs for the ID's found in data. No$SIMULATION
step is run which unfortunately means no residual error will be simulated. -
NMsim_VarCov
LikeNMsim_default
but '$THETA', '$OMEGA', and 'SIGMA' are drawn from distribution estimated in covariance step. This means that a successful covariance step must be available from the estimation. NB. A multivariate normal distribution is used for all parameters, including '$OMEGA' and '$SIGMA' which is not the correct way to do this. In case the simulation leads to negative diagonal elements in $OMEGA and $SIGMA, those values are truncated at zero. This method is only valid for simulation of '$THETA' variability. The method accepts a table of parameter values that can be produced with other tools than 'NMsim'. For simulation with parameter variability based on bootstrap results, useNMsim_default
.
Value
A data.frame with simulation results (same number of rows as input data). If 'sge=TRUE' a character vector with paths to simulation control streams.
Check a simulation control streams for things that can cause trouble in NMsim
Description
Check a simulation control streams for things that can cause trouble in NMsim
Usage
NMsimCheckMod(file.mod, lines)
Arguments
file.mod |
A control stream to check |
lines |
The control stream as text lines. Only use of of 'file.mod' and 'lines'. |
Summarize and test NMsim configuration
Description
Summarize and test NMsim configuration
Usage
NMsimTestConf(
path.nonmem,
dir.psn,
method.execute,
must.work = FALSE,
system.type
)
Arguments
path.nonmem |
See ?NMsim |
dir.psn |
See ?NMsim |
method.execute |
See ?NMsim |
must.work |
Throw an error if the configuration does not seem to match system. |
system.type |
See ?NMsim |
Value
A list with configuration values
Use emperical Bayes estimates to simulate re-using ETAs
Description
Simulation reusing ETA values from
estimation run or otherwise specified ETA values. For observed subjects, this is refered to as emperical Bayes
estimates (EBE). The .phi file from the estimation run must be found
next to the .lst file from the estimation.This means that ID
values in the (simulation) input data must be ID values that were
used in the estimation too. Runs an $ESTIMATION MAXEVAL=0
but pulls in ETAs for the ID's found in data. No
$SIMULATION
step is run which may affect how for instance
residual variability is simulated, if at all. You can also specify a different .phi
file which can be a simulation result.
Usage
NMsim_EBE(file.sim, file.mod, data.sim, file.phi, return.text = FALSE)
Arguments
file.sim |
The path to the control stream to be edited. This function overwrites the contents of the file pointed to by file.sim. |
file.mod |
Path to the path to the original input control stream provided as 'file.mod' to 'NMsim()'. |
data.sim |
See |
file.phi |
A phi file to take the known subjects from. The default is to replace the filename extension on file.mod with .phi. A different .phi file would be used if you want to reuse subjects simulated in a previous simulation. |
return.text |
If TRUE, just the text will be returned, and resulting control stream is not written to file. |
Value
Path to simulation control stream
See Also
simPopEtas
Simulate with parameter variability using the NONMEM NWPRI subroutine
Description
Modify control stream for simulation with uncertainty using inverse-Wishart distribution for OMEGA and SIGMA parameters
This function does not run any simulations. To simulate, using this method, see 'NMsim()'. See examples.
Usage
NMsim_NWPRI(file.sim, file.mod, data.sim, PLEV = 0.999, ...)
Arguments
file.sim |
The path to the control stream to be edited. This function overwrites the contents of the file pointed to by file.sim. |
file.mod |
Path to the path to the original input control stream provided as 'file.mod' to 'NMsim()'. |
data.sim |
Included for compatibility with 'NMsim()'. Not used. |
PLEV |
Used in |
... |
Additional arguments passed to 'NMsim_default()'. |
Details
Simulate with parameter uncertainty. THETA parameters are sampled from a multivariate normal distribution while OMEGA and SIGMA are simulated from the inverse-Wishart distribution. Correlations of OMEGA and SIGMA parameters will only be applied within modeled "blocks".
Value
Path to simulation control stream
Author(s)
Brian Reilly, Philip Delff
References
See Also
NMsim_VarCov
Examples
## Not run:
simres <- NMsim(file.path,method.sim=NMsim_WPRI,typical=TRUE,subproblems=500)
## End(Not run)
Simulate with parameter values sampled from a covariance step
Description
Like NMsim_default
but '$THETA', '$OMEGA', and 'SIGMA' are
drawn from distribution estimated in covariance step. A successful
covariance step must be available from the estimation. In case the
simulation leads to negative diagonal elements in $OMEGA and
$SIGMA, those values are truncated at zero. For simulation with
parameter variability based on bootstrap results, use
NMsim_default
.
This function does not run any simulations. To simulate, using this method, see 'NMsim()'.
Usage
NMsim_VarCov(
file.sim,
file.mod,
data.sim,
nsims,
method.sample = "mvrnorm",
ext,
write.ext = NULL,
...
)
Arguments
file.sim |
The path to the control stream to be edited. This function overwrites the contents of the file pointed to by file.sim. |
file.mod |
Path to the path to the original input control stream provided as 'file.mod' to 'NMsim()'. |
data.sim |
Included for compatibility with 'NMsim()'. Not used. |
nsims |
Number of replications wanted. The default is 1. If greater, multiple control streams will be generated. |
method.sample |
When 'ext' is not used, parameters are sampled, using 'samplePars()'. 'method' must be either 'mvrnorm' or 'simpar'. Only used when 'ext' is not provided. |
ext |
Parameter values in long format as created by 'readParsWide' and 'NMdata::NMreadExt'. |
write.ext |
If supplied, a path to an rds file where the parameter values used for simulation will be saved. |
... |
Additional arguments passed to 'NMsim_default()'. |
Value
Character vector of simulation control stream paths
Simulation method that uses the provided control stream as is
Description
The simplest of all method. It does nothing (but again,
NMsim
handles '$INPUT', '$DATA', '$TABLE' and more. Use
this for instance if you already created a simulation (or
estimation actually) control stream and want NMsim to run it on
different data sets.
Usage
NMsim_asis(file.sim, file.mod, data.sim)
Arguments
file.sim |
See |
file.mod |
See |
data.sim |
See |
Value
Path to simulation control stream
Transform an estimated Nonmem model into a simulation control stream
Description
The default behaviour of NMsim
. Replaces any $ESTIMATION
and $COVARIANCE sections by a $SIMULATION section.
Usage
NMsim_default(
file.sim,
file.mod,
data.sim,
nsims = 1,
onlysim = TRUE,
replace.sim = TRUE,
return.text = FALSE
)
Arguments
file.sim |
See |
file.mod |
See |
data.sim |
See |
nsims |
Number of replications wanted. The default is 1. If greater, multiple control streams will be generated. |
onlysim |
Include 'ONLYSIM' in '$SIMULATION'? Default is 'TRUE'. Only applied when 'replace.sim='TRUE'. |
replace.sim |
If there is a $SIMULATION section in the
contents of file.sim, should it be replaced? Default is
yes. See the |
return.text |
If TRUE, just the text will be returned, and resulting control stream is not written to file. |
Value
Character vector of simulation control stream paths
NMsim_known is an old name for NMsim_EBE()
Description
NMsim_known is an old name for NMsim_EBE()
Usage
NMsim_known(...)
Arguments
... |
Everything passed to NMsim_EBE() |
Value
Path to simulation control stream
Typical subject simiulation method
Description
Like NMsim_default
but with all ETAs=0, giving a
"typical subject" simulation. Do not confuse this with a
"reference subject" simulation which has to do with covariate
values. Technically all ETAs=0 is obtained by replacing
$OMEGA
by a zero matrix.
Usage
NMsim_typical(file.sim, file.mod, data.sim, return.text = FALSE)
Arguments
file.sim |
See |
file.mod |
See |
data.sim |
See |
return.text |
If TRUE, just the text will be returned, and resulting control stream is not written to file. |
Value
Path to simulation control stream
Update file names in control stream to match model name
Description
Update file names in control stream to match model name
Usage
NMupdateFn(
x,
section,
model,
fnext,
add.section.text,
par.file,
text.section,
quiet = FALSE
)
Arguments
x |
a control stream, path or 'NMctl' object. |
section |
What section to update |
model |
Model name |
fnext |
The file name extension of the file name to be updated (e.g., one of "tab", "csv", "msf"). |
add.section.text |
Addditional text to insert right after $SECTION. It can be additional TABLE variables. |
par.file |
The Nonmem parameter that specifies the file. In $TABLE, this is FILE. In $EST it's probably MSFO. |
text.section |
This is used to overwrite the contents of the section. The section output file name will still handled/updated. |
quiet |
Suppress messages? Default is 'FALSE'. |
Create new Nonmem control stream with updated initial parameter values
Description
Create new Nonmem control stream with updated initial parameter values
Usage
NMupdateInits(file.mod, file.ext, newfile)
Arguments
file.mod |
The control stream to update. Will not be edited. |
file.ext |
Path to ext file. Default is to replace extension on 'file.mod'. |
newfile |
New file to generate |
Value
The resulting control stream path(s)
Write IGNORE/ACCEPT filters to NONMEM model
Description
Write IGNORE/ACCEPT filters to NONMEM model
Usage
NMwriteFilters(file = NULL, lines = NULL, filters, write)
Arguments
file |
Path to control stream. Use 'file' or 'lines'. |
lines |
Control stream as text lines. Use 'file' or 'lines'. |
filters |
A data frome with filters, like returned by 'NMreadFilters()'. |
write |
If 'file' is provided, write the results to file? If 'lines' is used, 'write' cannot be used. |
Value
Resulting control stream (lines) as character vector
Writes a parameter values to a control stream
Description
Edit parameter values, fix/unfix them, or edit lower/upper bounds.
Usage
NMwriteInits(
file.mod,
update = TRUE,
file.ext = NULL,
ext,
inits.tab,
values,
newfile,
...
)
Arguments
file.mod |
Path to control stream. |
update |
If 'TRUE' (default), the parameter values are updated based on the '.ext' file. The path to the '.ext' file can be specified with 'file.ext' but that is normally not necessary. |
file.ext |
Optionally provide the path to an '.ext' file. If not provided, the default is to replace the file name extention on 'file.mod' with '.ext'. This is only used if 'update=TRUE'. |
ext |
An long-format parameter table as returned by 'NMreadExt()'. Can contain multiple models if 'file.mod' does not. |
inits.tab |
A wide-format parameter table, well suited for customizing initial values, limits, and for fixing parameters. For multiple custom parameter specifications, this may be the most suitable argument. |
values |
A list of lists. Each list specifies a parameter with named elements. Must be named by the parameter name. 'lower', 'upper' and 'fix' can be supplied to modify the parameter. See examples. Notice, you can use '...' instead. 'values' may be easier for programming but other than that, most users will find '...' more intuitive. |
newfile |
If provided, the results are written to this file as a new input control stream. |
... |
Parameter specifications. See examples, |
Details
Limitations:
'NMwriteInits()' can only update specifications of existing parameters. It cannot insert new parameters.
lower, init, upper, and FIX must be on same line in control stream.
If using something like CL=(.1,4,15) in control stream, two of those cannot be on the same line.
Value
a control stream as lines in a character vector.
Examples
## Not run:
file.mod <- system.file("examples/nonmem/xgxr021.mod",package="NMdata")
## specify parameters using ...
NMwriteInits(file.mod,
"theta(2)"=list(init=1.4),
"THETA(3)"=list(FIX=1),
"omega(2,2)"=list(init=0.1)
)
## or put them in a list in the values argument
NMwriteInits(file.mod,
values=list( "theta(2)"=list(init=1.4),
"THETA(3)"=list(FIX=1),
"omega(2,2)"=list(init=0.1))
)
## End(Not run)
Create or update $SIZES in a control stream
Description
Update $SIZES parameters in a control stream. The control stream can be in a file or provided as a character vector (file lines).
Usage
NMwriteSizes(
file.mod = NULL,
newfile,
lines = NULL,
wipe = FALSE,
write = !is.null(newfile),
...
)
Arguments
file.mod |
A path to a control stream. See also alternative 'lines' argument. Notice, if 'write' is 'TRUE' (default) and 'newfile' is not provided, 'file.mod' will be overwritten. |
newfile |
An optional path to write the resulting control stream to. If nothing is provided, the default is to overwrite 'file.mod'. |
lines |
Control stream lines as a character vector. If you already read the control stream - say using 'NMdata::NMreadSection()', use this to modify the text lines. |
wipe |
The default behavior ('wipe=FALSE') is to add the '$SIZES' values to any existing values found. If SIZES parameter names are overlapping with existing, the values will be updated. If 'wipe=TRUE', any existing '$SIZES' section is disregarded. |
write |
Write results to 'newfile'? |
... |
The $SIZES parameters. Provided anything, like 'PD=40' See examples. |
Value
Character lines with updated control stream
Examples
## No existing SIZES in control stream
## Not run:
file.mod <- system.file("examples/nonmem/xgxr132.mod",package="NMdata")
newmod <- NMwriteSizes(file.mod,LTV=50,write=FALSE)
head(newmod)
## End(Not run)
## provide control stream as text lines
## Not run:
file.mod <- system.file("examples/nonmem/xgxr032.mod",package="NMdata")
lines <- readLines(file.mod)
newmod <- NMwriteSizes(lines=lines,LTV=50,write=FALSE)
head(newmod)
## End(Not run)
## By default (wipe=FALSE) variabels are added to SIZES
## Not run:
lines.mod <- NMwriteSizes(file.mod,LTV=50,write=FALSE)
newmod <- NMwriteSizes(lines=lines.mod,PD=51,write=FALSE)
head(newmod)
## End(Not run)
Add degrees of freedom by OMEGA/SIGMA block
Description
Calculate and add degrees of freedom to be used for simulation using the inverse Wishart distribution.
Usage
NWPRI_df(pars)
Arguments
pars |
Parameters in long format, as returned by 'NMreadExt()'. |
Details
The degrees of freedom are calculated as DF = 2*((est**2)/(se**2)) + 1 -blocksize-1 DF2 is then adjusted to not be greater than the blocksize, and the minumum degrees of freedom observed in the block is applied to the full block. For fixed parameters, DF2 equals the blocksize.
Value
A data.table with DF2 added. See details.
References
See Also
NMsim_NWPRI
Create function that adds text elements to vector
Description
Namely used to feed functions to modify control streams using 'NMsim()' arguments such as 'modify'. Those functions are often onveniently passed a function. 'add' and 'overwrite' are simple shortcuts to creating such functions. Make sure to see examples.
Usage
add(..., .pos = "bottom")
Arguments
... |
Elements to add. |
.pos |
Either "top" or "bottom". Decides if new text is prepended or appended to existing text. |
Value
A function that adds the specified text to character vectors
Examples
myfun <- add("b","d")
myfun("a")
## If more convenient, you can add a vector instead.
myfun2 <- add(c("b","d"))
myfun2("a")
myfun3 <- add("b","d",.pos="top")
myfun3("a")
Add blocking info to parameter set
Description
Add blocking info to parameter set
Usage
addBlocks(pars, col.model = "model")
Arguments
pars |
The parameter, as returned by 'NMreadExt()' |
col.model |
Name of the model name column. |
Add class if not already present
Description
Add class if not already present
Usage
addClass(data, class)
Arguments
data |
The object to add class to |
class |
The class to add (character) |
Value
Object with additional class
Add simulation records to dosing records
Description
Deprecated, use 'NMaddSampples()'. Adds simulation events to all subjects in a data set. Copies over columns that are not varying at subject level (i.e. non-variying covariates). Can add simulation events relative to previous dosing time.
Usage
addEVID2(
data,
TIME,
TAPD,
CMT,
EVID,
DV,
col.id = "ID",
args.NMexpandDoses,
unique = TRUE,
extras.are.covs = TRUE,
as.fun,
doses,
time.sim
)
Arguments
data |
Nonmem-style data set. If using 'TAPD' an 'EVID' column must contain 1 for dosing records. |
TIME |
A numerical vector with simulation times. Can also be a data.frame in which case it must contain a 'TIME' column and is merged with 'data'. |
TAPD |
A numerical vector with simulation times, relative to previous dose. When this is used, 'data' must contain rows with 'EVID=1' events and a 'TIME' column. 'TAPD' can also be a data.frame in which case it must contain a 'TAPD' column and is merged with 'data'. |
CMT |
The compartment in which to insert the EVID=2 records. Required if 'CMT' is a column in 'data'. If longer than one, the records will be repeated in all the specified compartments. If a data.frame, covariates can be specified. |
EVID |
The value to put in the 'EVID' column for the created rows. Default is 2 but 0 may be prefered even for simulation. |
DV |
Optionally provide a single value to be assigned to the 'DV' column. The default is to assign nothing which will result in 'NA' as samples are stacked ('rbind') with 'data'. If you assign a different value in 'DV', the default value of 'EVID' changes to '0', and 'MDV' will be '0' instead of '1'. An example where this is useful is when generating datasets for '$DESIGN' where 'DV=0' is often used. |
col.id |
The name of the column in 'data' that holds the unique subject identifier. |
args.NMexpandDoses |
Only relevant - and likely not needed - if data contains ADDL and II columns. If those columns are included, 'addEVID2()' will use 'NMdata::NMexpanDoses()' to evaluate the time of each dose. Other than the 'data' argument, 'addEVID2()' relies on the default 'NMexpanDoses()' argument values. If this is insufficient, you can specify other argument values in a list, or you can call 'NMdata::NMexpanDoses()' manually before calling 'addEVID2()'. |
unique |
If 'TRUE' (default), events are reduced to unique time points before insertion. Sometimes, it's easier to combine sequences of time points that overlap (maybe across 'TIME' and 'TAPD'), and let 'addEVID2()' clean them. If you want to keep your duplicated events, use 'unique=FALSE'. |
extras.are.covs |
If 'TIME' and/or 'TAPD' are 'data.frame's and contain other columns than 'TIME' and/or 'TAPD', those are by default assumed to be covariates to be merged with data. More specifically, they will be merged by when the sample times are added. If 'extras.are.covs=FALSE', they will not be merged by. Instead, they will just be kept as additional columns with specified values, aligned with the sample times. |
as.fun |
The default is to return data as a 'data.frame'. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use 'as.fun="data.table"'. The default can be configured using 'NMdataConf()'. |
doses |
Deprecated. Use 'data'. |
time.sim |
Deprecated. Use 'TIME'. |
Details
The resulting data set is ordered by ID, TIME, and EVID. You may have to reorder for your specific needs.
Value
A data.frame with dosing records
Examples
(doses1 <- NMcreateDoses(TIME=c(0,12,24,36),AMT=c(2,1)))
addEVID2(doses1,TIME=seq(0,28,by=4),CMT=2)
## two named compartments
dt.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
dt.cmt <- data.frame(CMT=c(2,3),analyte=c("parent","metabolite"))
res <- addEVID2(dt.doses,TIME=seq.time,CMT=dt.cmt)
## Separate sampling schemes depending on covariate values
dt.doses <- NMcreateDoses(TIME=data.frame(regimen=c("SD","MD","MD"),TIME=c(0,0,12)),AMT=10,CMT=1)
seq.time.sd <- data.frame(regimen="SD",TIME=seq(0,6))
seq.time.md <- data.frame(regimen="MD",TIME=c(0,4,12,24))
seq.time <- rbind(seq.time.sd,seq.time.md)
addEVID2(dt.doses,TIME=seq.time,CMT=2)
## an observed sample scheme and additional simulation times
df.doses <- NMcreateDoses(TIME=0,AMT=50,addl=list(ADDL=2,II=24))
dense <- c(seq(1,3,by=.1),4:6,seq(8,12,by=4),18,24)
trough <- seq(0,3*24,by=24)
sim.extra <- seq(0,(24*3),by=2)
time.all <- c(dense,dense+24*3,trough,sim.extra)
time.all <- sort(unique(time.all))
dt.sample <- data.frame(TIME=time.all)
dt.sample$isobs <- as.numeric(dt.sample$TIME%in%c(dense,trough))
dat.sim <- addEVID2(dt.doses,TIME=dt.sample,CMT=2)
## TAPD - time after previous dose
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
addEVID2(df.doses,TAPD=seq.time,CMT=2)
## TIME and TAPD
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(0,4,12,24)
addEVID2(df.doses,TIME=seq.time,TAPD=3,CMT=2)
## Using a custom DV value affects EVID and MDV
df.doses <- NMcreateDoses(TIME=c(0,12),AMT=10,CMT=1)
seq.time <- c(4)
addEVID2(df.doses,TAPD=seq.time,CMT=2,DV=0)
Add residual variability based on parameter estimates
Description
Add residual variability based on parameter estimates
Usage
addResVar(
data,
path.ext,
prop = NULL,
add = NULL,
log = FALSE,
par.type = "SIGMA",
trunc0 = TRUE,
scale.par,
subset,
seed,
col.ipred = "IPRED",
col.ipredvar = "IPREDVAR",
as.fun
)
Arguments
data |
A data set containing indiviudual predictions. Often a result of NMsim. |
path.ext |
Path to the ext file to take the parameter estimates from. |
prop |
Parameter number of parameter holding variance of the proportional error component. If ERR(1) is used for proportional error, use prop=1. Can also refer to a theta number. |
add |
Parameter number of parameter holding variance of the additive error component. If ERR(1) is used for additive error, use add=1. Can also refer to a theta number. |
log |
Should the error be added on log scale? This is used to obtain an exponential error distribution. |
par.type |
Use "sigma" if variances are estimated with the SIGMA matrix. Use "theta" if THETA parameters are used. See 'scale.par' too. |
trunc0 |
If log=FALSE, truncate simulated values at 0? If trunc0, returned predictions can be negative. |
scale.par |
Denotes if parmeter represents a variance or a standard deviation. Allowed values and default value depends on 'par.type'.
|
subset |
A character string with an expression denoting a subset in which to add the residual error. Example: subset="DVID=='A'" |
seed |
A number to pass to set.seed() before simulating. Default is to generate a seed and report it in the console. Use seed=FALSE to avoid setting the seed (if you prefer doing it otherwise). |
col.ipred |
The name of the column containing individual predictions. |
col.ipredvar |
The name of the column to be created by addResVar to contain the simulated observations (individual predictions plus residual error). |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Value
An updated data.frame
Examples
## Not run:
## based on SIGMA
simres.var <- addResVar(data=simres,
path.ext = "path/to/model.ext",
prop = 1,
add = 2,
par.type = "SIGMA",
log = FALSE)
## If implemented using THETAs
simres.var <- addResVar(data=simres,
path.ext = "path/to/model.ext",
prop = 8, ## point to elements in THETA
add = 9, ## point to elements in THETA
par.type = "THETA",
log = FALSE)
## End(Not run)
Convert object to class NMctl
Description
Convert object to class NMctl
Usage
as.NMctl(x, ...)
Arguments
x |
object to convert |
... |
Not used |
Value
An object of class 'NMctl'.
Generate system command to call Nonmem directly
Description
Generate system command to call Nonmem directly
Usage
callNonmemDirect(file.mod, path.nonmem)
Test if file modification times indicate that Nonmem models should be re-run
Description
Test if file modification times indicate that Nonmem models should be re-run
Usage
checkTimes(
file,
use.input = TRUE,
nminfo.input = NULL,
file.mod,
tz.lst = NULL,
use.tmp = TRUE
)
Arguments
file |
Path to Nonmem-created file. Typically an output control stream. |
use.input |
Scan input data for updates too? Default is TRUE. |
nminfo.input |
If you do want to take into account input data but avoid re-reading the information, you can pass the NMdata meta data object. |
file.mod |
The input control stream |
tz.lst |
If files are moved around on or between file systems, the file modification time may not be reflective of the Nonmem runtime. In that case, you can choose to extract the time stamp from the output control stream. The issue is that Nonmem does not write the time zone, so you have to pass that to checkTimes if this is wanted. |
Drop spaces and odd characters. Use to ensure generated file names are usable.
Description
Drop spaces and odd characters. Use to ensure generated file names are usable.
Usage
cleanStrings(x)
Arguments
x |
a string to clean |
Value
A character vector
Examples
NMsim:::cleanStrings("e w% # ff!l3:t,3?.csv")
NMsim:::cleanStrings("3!?:#;<>=, {}|=g+&-
.csv")
Expand a set of covariate values into a data.set with reference value
Description
Expand a set of covariate values into a data.set with reference value
Usage
completeCov(covlist, data, col.id = "ID", sigdigs = 2)
Arguments
covlist |
A covariate specififed in a list. See ?expandCovLists. |
data |
See ?expandCovLists. |
col.id |
The subject ID column name. Necessary because quantiles sould be quantiles of distribution of covariate on subjects, not on observations (each subject contributes once). |
sigdigs |
Used for rounding of covariate values if using quantiles or if using a function to find reference. |
Examples
NMsim:::completeCov(covlist=list(covvar="WEIGHTB",values=c(30,60,90),ref=50),sigdigs=3)
Assign i and j indexes based on parameter section text
Description
Assign i and j indexes based on parameter section text
Usage
count_ij(res)
Arguments
res |
elements as detected by 'NMreadInits()' |
A standard-evaluation interface to 'data.table::dcast()'
Description
A standard-evaluation interface to 'data.table::dcast()'
Usage
dcastSe(data, l, r, ...)
Arguments
data |
data set to transpose (widen) |
l |
left-hand side variables as character vector. Result will be long/vertical in these variables. |
r |
left-hand side variables as character vector. Result will be wide in these variables. |
... |
Additional arguments paseed to 'data.table::dcast()'. |
Apply function and return a data.table
Description
A convenience function that returns a data.table with a column representing the input values and a column with results. This is still experimental and will not work for many input structures.
Usage
dtapply(X, FUN, ...)
Arguments
... |
arguments passed to lapply |
Details
Only functions that return vectors are currently supported. dtapply should support functions that return data.frames.
Value
a data.table
Create data set where each covariate is univariately varied (see 'forestDefineCovs()')
Description
Create data set where each covariate is univariately varied (see 'forestDefineCovs()')
Usage
expandCovs(...)
Arguments
... |
Passed to 'forestDefineCovs()' |
Value
A data.frame
Filter control streams to only those updated since last run
Description
Filter control streams to only those updated since last run
Usage
findUpdated(mods)
Arguments
mods |
list of (input or output) control streams to consider |
Value
character vector of paths found models
paste something before file name extension.
Description
Append a file name like file.mod to file_1.mod or file_pk.mod. If it's a number, we can pad some zeros if wanted. The separator (default is underscore) can be modified.
Usage
fnAppend(fn, x, pad0 = 0, sep = "_", collapse = sep, allow.noext = FALSE)
Arguments
fn |
The file name or file names to modify. |
x |
A character string or a numeric to add to the file name |
pad0 |
In case x is numeric, a number of zeros to pad before the appended number. This is useful if you are generating say more than 10 files, and your counter will be 01, 02,.., 10,... and not 1, 2,...,10,... |
sep |
The separator between the existing file name (until extension) and the addition. |
collapse |
If 'x' is of length greater than 1, the default is to collapse the elements to a single string using 'sep' as separator. See the 'collapse' argument to '?paste'. If you want to treat them as separate strings, use 'collapse=NULL' which will lead to generation of separate file names. However, currently 'fn' or 'x' must be of length 1. |
allow.noext |
Allow 'fn' to be string(s) without extensions? Default is 'FALSE' in which case an error will be thrown if 'fn' contains strings without extensions. If 'TRUE', 'x' will be appended to fn in these cases. |
Value
A character (vector)
Create data set where each covariate is univariately varied
Description
Each covariate is univariately varied while other covariates are kept at reference values. This structure is often used for forest-plot type simulations.
Usage
forestDefineCovs(
...,
data,
col.id = "ID",
sigdigs = 2,
reduce.ref = TRUE,
as.fun
)
Arguments
... |
Covariates provided as lists - see examples. The name of the arguement must match columns in data set. An element called ref must contain either a reference value or a function to use to derive the reference value from data (e.g. 'median'). Provide either 'values' or 'quantiles' to define the covariate values of interest (typically, the values that should later be simulated and maybe shown in a forest plot). 'label' is optional - if missing, the argument name will be used. If quantiles are requested, they are derived after requiring unique values for each subject. |
data |
A data set needed if the reference(s) value of one or more covariates is/are provided as functions (like median), or if covariate values are provided as quantiles. |
col.id |
The subject ID column name. Necessary because quantiles sould be quantiles of distribution of covariate on subjects, not on observations (each subject contributes once). |
sigdigs |
Used for rounding of covariate values if using quantiles or if using a function to find reference. |
reduce.ref |
If 'TRUE' (default), only return one row with all reference values. If 'FALSE' there will be one such row for each covariate. When reduced to one line, all columns related to covariate-level information such as covariate name will contain 'NA' for the reference. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Value
A data.frame
Examples
## Not run:
file.mod <- system.file("examples/nonmem/xgxr134.mod",package="NMdata")
res <- NMdata::NMscanData(file.mod)
forestDefineCovs(
WEIGHTB=list(ref=70,values=c(40,60,80,100),label="Bodyweight (kg)"),
## notice, values OR quantiles can be provided
AGE=list(ref=median, quantiles=c(10,25,75,90)/100, label="Age (years)"
),
data=res
)
## End(Not run)
Summarize simulated exposures relative to reference subject
Description
Summarize simulated exposures relative to reference subject
Usage
forestSummarize(data, funs.exposure, cover.ci = 0.95, by, as.fun)
Arguments
data |
Simulated data to process. This data.frame must contain must contain multiple columns, as defined by 'NMsim::forestDefineCovs()'. |
funs.exposure |
A named list of functions to apply for derivation of exposure metrics. |
cover.ci |
The coverage of the confidence intervals. Default is 0.95. |
by |
a character vector of column names to perform all calculations by. This could be sampling subsets or analyte. |
as.fun |
The default is to return data as a 'data.frame'. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use 'as.fun="data.table"'. The default can be configured using 'NMdataConf()'. |
Details
This function is part of the workflow provided by NMsim to generate forest plots - a graphical representation of the estimated covariate effects and the uncertainty of those effect estimates. 'forestDefineCovs()' helps construct a set of simulations to perform, simulation methods like 'NMsim_VarCov' and 'NMsim_NWPRI' can perform siulations with parameter uncertainty, and 'forestSummarize()' can then summarize those simulation results into the numbers to plot in a forest plot. See the NMsim vignette on forest plot generation available on the NMsim website for a step-by-step demonstration.
The following columns are generated by 'forestDefineCovs()' and are expected to be present. Differences within any of them will lead to separate summarizing (say for as covariate value to be plotted):
'model': A model identifier - generated by 'NMsim()'.
'type': The simulation type. "ref" for reference subject, "value" for any other. This is generated by 'forestDefineCovs()'.
'covvar': The covariate (of interest) that is different from the reference value in the specific simulation. Example: "WT"
'covlabel': Label of the covariate of interest. Example: "Bodyweight (kg)"
'covref': Reference value of the covariate of interest. Example: 80
'covval': Value of the covariate of interest (not reference). Example 110.
Value
A data.frame
Generate a .phi file for further simulation with Nonmem
Description
This will typically be used in a couple of different situations. One is if a number of new subjects have been simulated and their ETAs should be reused in subsequent simulations. Another is internally by NMsim when simulating new subjects from models estimated with SAEM.
Usage
genPhiFile(data, file, overwrite = FALSE)
Arguments
data |
A dataset that contains "ID" and all 'ETA's. This can be obtained by 'NMdata::NMscanData'. |
file |
Path to the .phi file to be written. |
overwrite |
If 'file' exists already, overwrite it? Default is 'FALSE'. |
Value
Invisibly, character lines (strings) optionally written to file
See Also
simPopEtas
Default location of input archive file
Description
Default location of input archive file
Usage
inputArchiveDefault(file)
Arguments
file |
Path to input or output control stream. |
Value
A file name (character)
Row numbers of elements in a triangular representation of a symmetric matrix
Description
Row numbers of elements in a triangular representation of a symmetric matrix
Usage
itriag(blocksize, istart = 1, diag = "lower")
Column numbers of elements in a triangular representation of a symmetric matrix
Description
Column numbers of elements in a triangular representation of a symmetric matrix
Usage
jtriag(blocksize, istart = 1, diag = "lower")
print a data.table
Description
print a data.table
Usage
message_dt(x, ...)
Arguments
x |
a data.table or something to be converted to a data.table. |
... |
passed to print.data.table. |
Details
defaults arguments to print.data.table (in addition to 'x=dt' which cannot be overwritten) are 'class=FALSE', 'print.keys=FALSE', 'row.names=FALSE'.
Get NMsim model metadata
Description
Get NMsim model metadata
Usage
modTab(res)
Arguments
res |
NMsim results (class 'NMsimRes'). |
Details
ROWMODEL (integer): A unique row identifier
file.mod (character): Path to the originally provided input control stream, relative to current working directory.
path.sim (character): Path to the simulation input control stream, relative to current working directory.
path.rds (character): Path to the results meta data file (_path.rds0)
model (character): The name of the original model, no extension. Derived from file.mod. If file.mod is named, the provided name is used.;
model.sim (character): A unique and cleaned (no special characters) name for the derived model, without extension. Notice if a simulation method generates multiple models, model.sim will be distinct for those. This is unlike model and name.sim.
name.sim (character): The value of the NMsim() argument of the same name at function call.
fn.sim (character): Name of the mod file to be simulated. Has .mod extension. It will differ from file mod in being derived from model.sim so it is unique and cleaned.
dir.sim (character): Relative path from point of execution to simulation directory. Cleaned.
path.mod.exec (character): Path to the control stream executed by Nonmem, relative to current working directory.
Value
A table with model details
Internal method for handling modify argument to NMsim
Description
Internal method for handling modify argument to NMsim
Usage
modifyModel(modify, dt.models = NULL, list.ctl = NULL)
Arguments
modify |
A list |
dt.models |
a data.table |
list.ctl |
List of coontrol streams as lines |
Value
dt.models (data.table) or result list.ctl (list) depending on whether the 'dt.models' or the 'list.ctl' argument was provided.
Create file names for multiple list elements
Description
Create file names for multiple list elements
Usage
nameMultipleFiles(fn, list.obj, simplify = TRUE)
Arguments
fn |
File name to provide stem for all file names |
list.obj |
List of objects to provide names for |
simplify |
If only one file path, skip numbering? Default is TRUE. |
Create function that modifies text elements in a vector Namely used to feed functions to modify control streams using 'NMsim()' arguments such as 'modify'. Those functions are often onveniently passed a function. 'add' and 'overwrite' are simple shortcuts to creating such functions. Make sure to see examples.
Description
Create function that modifies text elements in a vector Namely used to feed functions to modify control streams using 'NMsim()' arguments such as 'modify'. Those functions are often onveniently passed a function. 'add' and 'overwrite' are simple shortcuts to creating such functions. Make sure to see examples.
Usage
overwrite(..., fixed = TRUE)
Arguments
... |
Passed to 'gsub()' |
fixed |
This is passed to gsub(), but ‘overwrite()'’s default behavior is the opposite of the one of 'gsub()'. Default is 'FALSE' which means that strings that are exactly matched will be replaced. This is useful because strings like 'THETA(1)' contains special characters. Use 'fixed=FALSE' to use regular expressions. Also, see other arguments accepted by 'gsub()' for advanced features. |
Value
A function that runs 'gsub' to character vectors
Examples
myfun <- overwrite("b","d")
myfun(c("a","b","c","abc"))
## regular expressions
myfun2 <- overwrite("b.*","d",fixed=FALSE)
myfun2(c("a","b","c","abc"))
pad zeros on integers
Description
pad zeros on integers
Usage
padZeros(x, nchars)
Arguments
x |
integers to pad. They can be coded as characters already. |
nchars |
Optional specification of length of character strings to return. If not supplied, characters will be padded to match length of max value in x. |
Value
A character vector
Paste string to start of vector only
Description
paste(str,x) will prepend str to all values of x. use pasteBegin to only paste it to the first value of x.
Usage
pasteBegin(x, add, ...)
pasteEnd(x, add, ...)
Arguments
x |
A vector of strings |
add |
A string to add |
... |
Aditional arguments to 'paste()'. |
Print OMEGA and SIGMA matrices for NONMEM sections in block format. Note: This function currently only works with fixed blocks as in the NMsim_NWPRI functionality for printing $THETAPV.
Description
Print OMEGA and SIGMA matrices for NONMEM sections in block format. Note: This function currently only works with fixed blocks as in the NMsim_NWPRI functionality for printing $THETAPV.
Usage
prettyMatLines(block_mat_string)
Arguments
block_mat_string |
Output of NMsim::NMcreateMatLines. This is a string of OMEGA/SIGMA estimates that will be wrapped onto multiple lines for ease of reading in NONMEM control streams. |
Details
This function is currently not used by any functions in NMsim and is for now deprecated. NMcreateMatLines() handles this internally.
Value
Character vector
print method for NMsimRes summaries
Description
print method for NMsimRes summaries
Usage
## S3 method for class 'summary_NMsimRes'
print(x, ...)
Arguments
x |
The summary object to be printed. See ?summary.NMsimRes |
... |
Arguments passed to other print methods. |
Value
NULL (invisibly)
first path that works
Description
When using scripts on different systems, the Nonmem path may change from run to run. With this function you can specify a few paths, and it will return the one that works on the system in use.
Usage
prioritizePaths(paths, must.work = FALSE)
Arguments
paths |
vector of file paths. Typically to Nonmem executables. |
must.work |
If TRUE, an error is thrown if no paths are valid. |
Read as class NMctl
Description
Read as class NMctl
Usage
readCtl(x, ...)
Arguments
x |
object to read. |
... |
Not used. |
Value
An object of class 'NMctl'.
Parameter data from csv
Description
Reads output table from simpar and returns a long format data.table. This is the same format as returned by NMreadExt() which can be used by NMsim.
Usage
readParsWide(
data,
col.model,
col.model.sim,
strings.par.type = c(THETA = "^T.*", OMEGA = "^O.*", SIGMA = "^S."),
as.fun
)
Arguments
data |
A data.frame or a path to a delimited file to be read using 'data.table::fread'. |
col.model |
Column containing name of the original model. By default a column called "model" will contain "Model1". |
col.model.sim |
Name of the model counter, default is "model.sim". If the provided name is not found in data, it will be created as a row counter. Why needed? Each row in data represents a set of parameters, i.e. a model. In the long format result, each model will have multiple rows. Hence, a model identifier is needed to distinguish between models in results. |
strings.par.type |
Defines how column names get associated with THETA, OMEGA, and SIGMA. Default is to look for "T", "O", or "S" as starting letter. If customizing, make sure each no column name will be matched by more than one criterion. |
as.fun |
The default is to return data as a data.frame. Pass
a function (say |
Details
The wide data format read by 'readParsWide' is not a Nonmem format. It is used to bridge output from other tools such as simpar, and potentially PSN.
This function reads a data that is "wide" in parameters - it has a column for each parameter, and one row per parameter set or "model". It returns a data set that is "long" in model and parameters. The long format contains
id.model.par The unique model-parameter identifier. The row-identifier.
model Model identifier.
par.type ("THETA", "OMEGA", "SIGMA")
i and j indexes for the parameters (j is NA for par.type=="THETA").
value The parameter value
parameter Nonmem-style parameter names. THETA1, OMEGA(1,1) etc. Notice the inconsistent naming of THETA vs others.
name.wide The column name in the wide data where this value was taken
The columns or "measure variables" from which to read values are specified as three regular expressions, called THETA, OMEGA, and SIGMA. The default three regular expressions will associate a column name starting with "T" with THETAs, while "O" or "S" followed by anything means "OMEGA" or "SIGMA".
readParsWide extracts i and j indexes from sequences of digits in the column names. TH.1 would be TETA1, SG1.1 is SIGMA(1,1).
Value
a long-format data.frame of model parameters
Examples
## Not run:
tab.ext <- readParsCsv("simpartab.csv")
## or
tab.simpar <- fread("simpartab.csv")
tab.ext <- readParsCsv(tab.simpar)
NMsim(...,method.sim=NMsim_VarCov,tab.ext=tab.ext)
## End(Not run)
Sample subject-level covariates from an existing data set
Description
Repeats a data set with just one subject by sampling covariates from subjects in an existing data set. This can conveniently be used to generate new subjects with covariate resampling from an studied population.
Usage
sampleCovs(
data,
Nsubjs,
col.id = "ID",
col.id.covs = "ID",
data.covs,
covs,
seed.R,
as.fun
)
Arguments
data |
A simulation data set with only one subject |
Nsubjs |
The number of subjects to be sampled. This can be greater than the number of subjects in data.covs. |
col.id |
Name of the subject ID column in 'data' (default is "ID"). |
col.id.covs |
Name of the subject ID column in 'data.covs' (default is "ID"). |
data.covs |
The data set containing the subjects to sample covariates from. |
covs |
The name of the covariates (columns) to sample from 'data.covs'. |
seed.R |
If provided, passed to 'set.seed()'. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Value
A data.frame
Examples
library(NMdata)
data.covs <- NMscanData(system.file("examples/nonmem/xgxr134.mod",package="NMsim"))
dos.1 <- NMcreateDoses(TIME=0,AMT=100)
data.sim.1 <- NMaddSamples(dos.1,TIME=c(1,4),CMT=2)
sampleCovs(data=data.sim.1,Nsubjs=3,col.id.covs="ID",data.covs=data.covs,covs=c("WEIGHTB","eff0"))
Sample model parameters using 'mvrnorm' or the 'simpar' package
Description
Sample model parameters using 'mvrnorm' or the 'simpar' package
Usage
samplePars(file.mod, nsims, method, seed.R, format = "ext", as.fun)
Arguments
file.mod |
Path to model control stream. Will be used for both 'NMreadExt()' and 'NMreadCov()', and extension will automatically be replaced by '.ext' and '.cov'. |
nsims |
Number of sets of parameter values to generate. Passed to 'simpar'. |
method |
The sampling method. Options are "mvrnorm" and "simpar". Each have pros and cons. Notice that both methods are fully automated as long as ".ext" and ".cov" files are available from model estimation. |
seed.R |
seed value passed to set.seed(). |
format |
The returned data set format "ext" (default) or "wide". "ext" is a long-format, similar to what 'NMdata::NMreadExt()' returns. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Details
samplePars() uses internal methods to sample using mvrnorm or simpar. Also be aware of NMsim_NWPRI which is based on the Nonmem-internal NWPRI subroutine. NMsim_NWPRI is much faster to execute. Simulation with paramater uncertainty on variance components ('OMEGA' and 'SIGMA') is only reliable starting from Nonmem 7.6.0.
mvrorm: The multivariate normal distribution does not ensure non-negative variances. Negative variances are not allowed and can not be simulated. To avoid this, 'method=mvrnorm' truncates negative variance diagonal elements at zero.
simpar: simpar must be installed.
Please refer to publications and vignettes for more information on sampling methods.
Value
A table with sampled model parameters
Author(s)
Sanaya Shroff, Philip Delff
Sample model parameters using the 'simpar' package
Description
Sample model parameters using the 'simpar' package
Usage
sampleParsSimpar(file.mod, nsim, format = "ext", seed.R, as.fun)
Arguments
file.mod |
Path to model control stream. Will be used for both 'NMreadExt()' and 'NMreadCov()', and extension will automatically be replaced by '.ext' and '.cov'. |
nsim |
Number of sets of parameter values to generate. Passed to 'simpar'. |
format |
"ext" (default) or "wide". |
seed.R |
seed value passed to set.seed(). |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
Value
A table with sampled model parameters
Author(s)
Sanaya Shroff, Philip Delff
Generate a population based on a Nonmem model
Description
Generate a population based on a Nonmem model
Usage
simPopEtas(
file,
N,
seed.R,
pars,
file.phi,
overwrite = FALSE,
as.fun,
file.mod,
seed,
...
)
Arguments
file |
Passed to 'NMdata::NMreadExt()'. Path to ext file. By default, 'NMreadExt()' uses a'auto.ext=TRUE' which means that the file name extension is replaced by '.ext'. If your ext file name extension is not '.ext', add 'auto.ext=FALSE' (see ...). |
N |
Number of subjects to generate |
seed.R |
Optional seed. Will be passed to 'set.seed'. Same thing as running 'set.seed' just before calling 'simPopEtas()'. |
pars |
A long-format parameter table containing par.type and i columns. If this is supplied, the parameter values will not be read from an ext file, and file has no effect. If an ext file is available, it is most likely better to use the file argument. |
file.phi |
An optional phi file to write the generated subjects to. |
overwrite |
If 'file.phi' exists already, overwrite it? Default is 'FALSE'. |
as.fun |
The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf. |
file.mod |
Deprecated. Use file instead. |
seed |
Deprecated. Use seed.R instead. |
... |
Additional arguments passed to NMdata::NMreadExt(). |
Value
A data.frame
Check that a variable is a single character string meeting specified requirements
Description
Check that a variable is a single character string meeting specified requirements
Usage
simpleCharArg(name.arg, val.arg, default, accepted, lower = TRUE, clean = TRUE)
Arguments
name.arg |
Name of the argument |
val.arg |
argument value |
default |
If val.arg is NULL, what should be returned? |
accepted |
What values are allowed |
lower |
run tolower? |
clean |
clean white spaces? |
Details
Better options may be available in packages like checkmate. This function doesn't only check the parameter value, it also sets it to the default value if missing.
Value
The resulting parameter value
Simplify file paths by dropping .. and //
Description
Simplify file paths by dropping .. and //
Usage
simplePath(path)
Arguments
path |
single or multiple file or dir paths as strings. |
Value
Simplified paths as strings
Examples
## Not run:
path <- c("ds/asf.t","gege/../jjj.r")
NMsim:::simplePath(path)
## End(Not run)
Summarize simulated exposures relative to reference subject (see 'forestSummarize()')
Description
Summarize simulated exposures relative to reference subject (see 'forestSummarize()')
Usage
summarizeCovs(...)
Arguments
... |
Passed to 'forestSummarize()' |
Value
A data.frame
summary method for NMsim results (NMsimRes objects)
Description
summary method for NMsim results (NMsimRes objects)
Usage
## S3 method for class 'NMsimRes'
summary(object, ...)
Arguments
object |
An NMsimRes object (from NMsim). |
... |
Not used |
Value
A list with summary information on the NMsimRes object.
Calculate number of elements for matrix specification
Description
calculate number of elements in the diagonal and lower triangle of a squared matrix, based on the length of the diagonal.
Usage
triagSize(diagSize)
Arguments
diagSize |
The length of the diagonal. Same as number of rows or columns. |
Value
An integer
Remove NMsimModTab class and discard NMsimModTab meta data
Description
Remove NMsimModTab class and discard NMsimModTab meta data
Check if an object is 'NMsimModTab'
Basic arithmetic on NMsimModTab objects
Usage
unNMsimModTab(x)
is.NMsimModTab(x)
## S3 method for class 'NMsimModTab'
merge(x, ...)
## S3 method for class 'NMsimModTab'
t(x, ...)
## S3 method for class 'NMsimModTab'
dimnames(x, ...)
## S3 method for class 'NMsimModTab'
rbind(x, ...)
## S3 method for class 'NMsimModTab'
cbind(x, ...)
Arguments
x |
an NMsimModTab object |
... |
arguments passed to other methods. |
Details
When 'dimnames', 'merge', 'cbind', 'rbind', or 't' is called on an 'NMsimModTab' object, the 'NMsimModTab' class is dropped, and then the operation is performed. So if and 'NMsimModTab' object inherits from 'data.frame' and no other classes (which is default), these operations will be performed using the 'data.frame' methods. But for example, if you use 'as.fun' to get a 'data.table' or 'tbl', their respective methods are used instead.
Value
x stripped from the 'NMsimModTab' class
logical if x is an 'NMsimModTab' object
An object that is not of class 'NMsimModTab'.
Remove NMsimRes class and discard NMsimRes meta data
Description
Remove NMsimRes class and discard NMsimRes meta data
Check if an object is 'NMsimRes'
Basic arithmetic on NMsimRes objects
Usage
unNMsimRes(x)
is.NMsimRes(x)
## S3 method for class 'NMsimRes'
merge(x, ...)
## S3 method for class 'NMsimRes'
t(x, ...)
## S3 method for class 'NMsimRes'
dimnames(x, ...)
## S3 method for class 'NMsimRes'
rbind(x, ...)
## S3 method for class 'NMsimRes'
cbind(x, ...)
Arguments
x |
an NMsimRes object |
... |
arguments passed to other methods. |
Details
When 'dimnames', 'merge', 'cbind', 'rbind', or 't' is called on an 'NMsimRes' object, the 'NMsimRes' class is dropped, and then the operation is performed. So if and 'NMsimRes' object inherits from 'data.frame' and no other classes (which is default), these operations will be performed using the 'data.frame' methods. But for example, if you use 'as.fun' to get a 'data.table' or 'tbl', their respective methods are used instead.
Value
x stripped from the 'NMsimRes' class
logical if x is an 'NMsimRes' object
An object that is not of class 'NMsimRes'.
Conveniently write text lines to file
Description
Conveniently write text lines to file
Usage
writeTextFile(lines, file, simplify = TRUE)
Arguments
lines |
the character lines to write |
file |
The file name path to write to |
simplify |
Passed to 'nameMultipleFiles()' |
Value
File paths as character strings