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NMreadSim() has a new argument called
rm.tmp which is used to remove the intermediate NONMEM
files if they are successfully read. Remember, NMreadSim()
creates a compressed results data set which will be read by
NMreadSim() in future function calls, so unless debugging
is needed on the simulation control streams and files returned by NONMEM
running the simulations, it may be better to delete the intermediate
files altogether and save the disk space.
expandCovs() has a new argument
reduce.ref which defaults to TRUE meaning that
by default there will be only one reference combination. If
FALSE expandCovs() will return one reference
for each covariate. The forest plot can be evaluated with just one
reference simulation.
New function summarizeCovs() introduced to summarize
simulation results for forest plots. This function is closely related to
expandCovs()
NMsim() no longer requires NONMEM to be available if
reuse.results=TRUE and NONMEM does not need to be
run.
NMsim 0.1.4 would not submit jobs to cluster when number of cores
was nc=1. Fixed. Workaround in 0.1.4, use
nc=2.
NMsim 0.1.4 erroneously concluded jobs had failed when sent to
the cluster if there was no existing queue. This is due to the exit
status of qsub in such cases. This has been fixed. As a
workaround in 0.1.4, just run your sim again once the queue has been
initiated by the first NMsim called.
When NONMEM failed in 0.1.4, NMsim might not return debugging info. Fixed.
sampleParsSimpar() is a new function that automates
sampling of parameter values from an estimated variance-variance matrix
in a successful $COVARIANCE step using the
simpar R package from Metrum Research Group.
simpar is currently not on CRAN, so the user must install
it from MPN or github to make use of sampleParsSimpar().
The sampled parameter values can be fed directly to NMsim
using the NMsim_VarCov method making it very easy to
simulate with parameter uncertainty based on simpar. I want
to thank Sanaya Shroff for her outstanding work on this functionality
and for her exciting work summarizing the available methods for
simulation with parameter uncertainty which she will be sharing at ACoP
2024. Also a big thanks to Eric Anderson for helping out with adjusting
the github workflows to pull simpar from MPN.
expandCovs() is a new function that puts together
data sets for univariately varying covariates while keeping other at
reference values. The function can derive both reference values and
covariate values to simulate at by using i.e. median() and
quantile().
NMsim()
model: The run name derived from
file.mod.name.sim: The same as provided in the
name.sim argument.model.sim The name of the generated model. In the
simple case, this is model and name.sim
combined. But in many cases, multiple models are being generated for
each simulated control stream..ext file if updating parameter
values using PSN’s update_inits. It is still recommended to
keep the .ext file since it provides higher accuracy than
the .lst file.NMexec()
When submitting all updated models, NMexec() will
now by default try to detect if a model is already running before
submitting it.
Provides a summary of models to be submitted before starting to do so.
NMcreateDoses()
ADDL and II are now also separate
arguments providing a simpler interface than the addl
argument. The addl argument provides the advantage of being
able to specify the two columns together in one data.frame,
possibly including covariates.
add.lastonly is a new argument. If TRUE
(default) and ADDL and II are of length 1,
they are only applied to the last event in a dosing regimen.
col.id argument to specify name of subject id column
or to omit altogether using col.id=NA.
Now checking that TIME is covering the length of all
other arguments. In contrast to other arguments, it does not make much
sense to try to extrapolate the TIME argument.
addEVID2() now has two arguments, TIME
and TAPD which allow for specification of time since first
dose and time after each dose at which to insert simulation records. The
two can even be combined. TIME replaces the now deprecated
time.sim argument, and TAPD is new.
A bug most likely affecting most Windows users for execution of Nonmem has been fixed. If on Windows, you should upgrade to NMsim 0.1.4. Thank you to Boris Grinshpun for reporting this!
When using method.execute="nmsim" there was an issue
with parallellization. This was not a major problem in most simulation
applications, but it should now be fixed.
NMsim()
NMsim() would fail with messages likeError in `:=`((col.sim), ..name.sim) :
Check that is.data.table(DT) == TRUE. Otherwise, := and `:=`(...) are defined for use in j, once only and in particular ways. See help(":=").The issue has been fixed. If using NMsim 0.1.3 or earlier, the
workaround is to do NMdataConf(as.fun="data.table"). Then
after having the simulation results as a data.table, convert it with
as.data.frame() or as preferred.
Notice, NMdataConf() affects the succeeding
NMsim() calls but also other NMdata and NMsim function
calls. When the VPC simulation has run, you may not want to continue
recieving data.tables, you should reset the default value for as.fun:
NMdataConf(as.fun=NULL) which will turn it back to
returning data.frames by default. If you prefer tibbles, you can do
NMdataConf(as.fun=tibble::as_tibble). Generally, if you
prefer to work with something that is not data.frames (data.table and
tibble the most common alternatives), it is recommended to use
NMdataConf() to customize your default.
NMexec()
NMexec would fail running control streams named
starting in numerals (like 1.mod) when
sge=TRUE. This is due to the way sge job names
are generated by NMexec(). Fixed by prepending “NMsim_” in
these cases.NMcreateDoses
NMsim()
New simulation method NMsim_NWPRI() to simulate with
parameter uncertainty. This automates simulation with parameter
uncertainty using Nonmem’s NWPRI subroutine for models with
a successful covariance step. For now this method only works for
THETA since we have found that the parameter distributions
sampled for OMEGA and SIGMA do not always
match the model estimates and therefore cannot be trusted. To ensure
that only THETA are sampled and simulated, this method
should only be run using the typical=TRUE argument. This
method is much faster than the existing methods in NMsim for simulation
with parameter uncertainty (NMsim_VarCov()). This method
depends on NMdata version 0.1.7 or greater. Big thanks to
Brian Reilly for his excellent work on this important
contribution.
The add() function to be used in
NMsim()’s modify.model argument now supports
prepending and appending of text lines to control stream sections. The
default is still to append and add("text",.pos="top") will
now prepend “text”.
NMexec()
A “cleaning” feature has been added to NMexec(),
removing some of the temporary files generated by Nonmem after ended
execution. The interface is inspired by PSN’s clean
argument supporting values 0 (no cleaning), 1-4 (quite some cleaning -
so far no difference betwen these values), and 5 for complete deletion
of the temporary directory. When using method.execute="PSN"
NMsim calls PSN’s execute passing on the clean
value.
Default number of cores to be used by NMexec() can
be controlled using NMdataConf(nc=N) where N
is the desired default. Notice, NMsim() will not use this
default. This is because parallellization is not as commonly used in
simulation as in estimation.
A useful backup feature has been added. Before execution, any existing results files related to a model are by default moved to a backup folder. The backup files are not
NMsimTestConf() - Function to summarize and test
configuration. This is used internally and provides important debugging
information.
readParsWide() - A function to read wide-format
parameter tables - is now exported. This is useful when simulating with
parameter values that have been sampled outside Nonmem, e.g. using the
simpar package.
NMexec() would fail on linux when run on models with
multiple $TABLE statements. Fixed.
NMsim’s internal method to update parameter initial
values had an issue running on models with $OMEGA block
structures. Fixed.
NMreadSim() would fail if working directory had been
changed. Fixed.
No changes since 0.1.1 except for disabling a test that was failing on some systems due to technical reasons.
While no critical and generally only few bugs have been found in NMsim 0.1.0, NMsim 0.2.0 includes several improvements and upgrades. The interface feels smoother too. I want to thank Ron Keizer for feedback and advice.
NMsim() has a much reduced and improved messaging to
the user. If more than one model or data set is supplied or generated,
progress trackers will be shown while starting Nonmem, while waiting for
Nonmem to finish, and while collecting the simulation results.
The messages include information about where intermediate files and final results files are stored.
NMexec() has improved support for estimation.
method.execute="nmsim" and
method.execute="psn" both work on linux and windows, even
though less thoroughly tested on windows. Thank you to Boris Grinshpun
for testing.
Names of files containing final results from NMsim()
have been renamed to be more intuitive. The previous
_paths.rds will now be called _MetaData.rds.
The results, once read and compressed, will be in a file called
_ResultsData.fst. Notice, both these files are required to
fully recover simulation results. Thanks to Brian Reilly for discussions
on this and many other design aspects.
It is now possible to provide specific parameters
(THETA, OMEGA and SIGMA) for
Nonmem simulation. NMsim() table for simulations. See
argument file.ext and NMsim_VarCov’s argument
ext.
New arguments to control seeds. NMsim can either use
R’s set.seed before generating the seeds for Nonmem.
Detailed control of the seeds, including how many to include and the
distribution of the random sources in Nonmem, can be controlled using
the seed.nm argument. This way, the user can add random
processes to the estimated control stream. The actual Nonmem seed values
can also be provided.
method.sim=NMsim_typical() has been replaced by
argument typical=TRUE. This means typical subject
simulations can now be combined with other simulations methods like
NMsim_VarCov.
NMsim() now adds a column called sim
which carries the name of the simulation defined by the
name.sim argument.
Several checks for existence and consistency of files are implemented.
The native Nonmem execution method now also works for estimation.
pnm files are now saved with the model for
transparency.
Running rbind on results from NMsim
would throw errors. Thanks to Simone Cassani for reporting this.
Fixed.
Using other file name extensions than .mod on input
control streams in combination with NMdataConf(file.mod)
would make NMsim fail. Thanks to Brian Reilly for reporting.
Fixed.
NMsim_known() renamed to
NMsim_EBE().
Generated control streams have been stripped off of the “NMsim_”
prefix. These files are located in NMsim generated folders
so the prefix was uninformative.
In case of multi-threaded (cluster) execution and something went
wrong NMexec() used to write some output files from Nonmem
in the current working directory. All these are now being written to the
model execution directory for clarity and tidyness.
For the first time NMsim works on Windows. There may still be some
limitations but initial testing looks very promising. Make sure to set
path.nonmem. See the configuration vignette on the website:
NMsim-config.html
0.1.0 is also an important upgrade that solidifies the way NMsim reads results from simulations. In addition to important bug fixes, it allows for NMsim to wait on Nonmem to complete simulations - even when they are run on a cluster. This means even large simulations with NMsim can be integrated in scripts.
Works on Windows - at least most features do.
NMsim() and NMreadSim() now have
wait arguments which controls if they will wait for Nonmem
to finish simulating. This will also work if jobs were sent to the
cluster.
NMsim() respects the reuse.results
argument. If TRUE it will use results file on the file
system. This can be used in stead of putting NMsim() calls
inside an if-statement to disable the simulation but read results on
file.
NMsim() looks for a couple of features of the
provided control streams that are known to be able to cause issues.
Warnings will be issued if these are found.
addEVID2 has a new argument, EVID to
specify what value the EVID column should have. It can be
useful sometimes to use EVID=0 for simulation
records.
NMreadSim() would not get the path right
to the simulation results leading to failures in reading simulation
results. Fixed.NMreadExt and NMreadPhi have
been removed from NMsim. They live and are being maintained in the
NMdata package. In NMsim, were deprecated and unmaintained
functions.NMsim 0.0.9 had an unfortunate bug in NMreadSim() which
has been fixed. That bugfix is difference between 0.0.9 and 0.0.10.
NMsim 0.0.9 is almost identical to 0.0.8 but ensures compatibility with older R versions.
NMreadSim would not be able to read and
combine results from models that returned different data variables.
Fixed.NMsim 0.0.1 would generate an rds file
with paths to simulation files and results for each model+data set
simulated. This has been changed to now only generate one table per
model. This makes it simpler to read simulation results in some
cases.
NMreadSim should now be the best and only way for
the user to read NMsim simulation results. It interprets
rds files (which are the ones intended for reading),
fst files, tables of NMsim runs, and
NMsim results. This makes it less confusing what can be
processed by NMreadSim and also it sometimes easier to
generalize code reading simulation results. Also, NMsim now
always reads results using NMreadSim. This has the
advantage that an fst file will always be produced if NMsim
waits to read the results.
NMreadSim has a new argument,
check.time by default disabling checking whether a
collected fst file is newer than the results files
generated by NMsim. Normally, it’s a good thing to check
this but some ways of sharing file files may not retain file
modification times needed to check for this. NMsim will
delete the fst files if it finds any so normally it should
not be a problem to skip this check.
modify.model is the argument to use to modify the
control stream after NMsim is done preparing the
simulation. A couple of helper functions are available making it really
easy to add contents (very commonly used) or modify contents.
NMsim now tries to reuse stored results if
reuse.results=TRUE. It does so in a simple way - if they
exist, they will be attempted read - so be careful to rerun simulations
without this option if you change any arguments.
NMsim will by default add a DV column
with NA values if DV is not in input data.
Nonmem most often needs that column, and it is uninformative for
simulations. Disable this feature by using
auto.dv=FALSE.
The transform option has been integrated into the
table of simulations created by NMsim(). This means even if
the results are not read by NMsim (because the simulation
is not executed or it is submitted to a cluster), the transformation
will still be applied by NMreadSim() later.
NMsim()'s dir.sims and
dir.res arguments can be controlled using
NMdata::NMdataConf(). Often these two arguments are used
all the time, so it’s convenient to be able to configure those once and
for all in a script.
NMreadSim was only able to read results if the
current working directory was the same as when NMsim was
executed. Now fixed.
In some cases NMsim would fail on models with
multiple output tables when the table.vars argument was not
used. Fixed.
NMsim’s sim.dir.from.scratch argument
was not respected due to a simple bug, leading to dir.sims
growing each time a simulation was rerun.
In case simulation data is a list of data sets NMsim
would not order columns when order.columns was
TRUE. Now fixed.
In case of lists of data sets, and the list element (data set)
names included spaces, NMsim() would throw and error.
Spaces in data set names are now replaced with under scores (“_“) to
avoid that. It will often happen when data sets are split into lists
using data.table::split.data.table() - which is an
excellent way to do this, by the way.
Function simPopEtas() was not exported, so only
available as NMsim:::simPopEtas(). Fixed.
Function simPopEtas() to generate a population from
a model. The population can be saved as a phi file to be
reused in subsequent simulations. The function is by mistake not
exported in 0.0.7 so for now you must use
NMsim:::simPopEtas() to use it.
Function NMreadSim() provides a very simple
interface to reading simulation results. Especailly in cases where the
simulation is being parallelized or otherwise spawns multiple Nonmem
jobs, this is a useful feature.
A list of simulation data sets will now be simulated with separate Nonmem runs. This is an efficient way to parellelize large simulation runs.
sge type clusters (using qsub). See arguments
sge and nc.genPhiFile()
when having more than 10 ETAs in a model. Now fixed.Full support for models estimated with SAEM. Especially, simulation of “known” subjects, i.e. re-using emperical Bayes estimates, is slightly different with these models.
Experimental support for windows with PsN. dir.psn
argument has to point to a directory where executables
execute and update_inits are found. Thanks to
Sjoerd Koopman for debugging and testing this. Hopefully in future
versions, PsN will not be needed on Windows (like it is not
needed on Linux).
The simulation method called NMsim_known now accepts other
.phi files to use than the .phi file generated by the
estimation run. This is useful if one wants to reuse subjects generated
in a previous simulation.
xml, ext,
cov, cor, coi, phi,
shk - in addition to output table files and the archived
input data of course.NMsim supports type.sim="typical" which means all
OMEGAS will be fixed to zero. This requires the ext file to be
present.
Experimental support for simulation of estimated subjects using
type.sim="known".
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.