Built 2021-07-08 using NMdata 0.0.8.
This vignette is still under development. Please make sure to see latest version available here.
This vignettes aims at enabling you at
Using NMscanData to read and combine all output and input data based only on (the path to) the Nonmem list file (understanding how NMscanData prioritizes output and input data in case of redundancy)
Switching between combining output and input data by mimicking the Nonmem data filters (IGNORE/ACCEPT) and merging by a row identifier
Configuring NMdata
to return the data class of your preference (say data.table
or tbl
) instead of data.frame
which is default
Using automatically generated meta data to look up information on input and output tables, how they were combined, and results of checks performed by NMscanData
.
Including input data rows that were not processed by Nonmem (ACCEPT
and IGNORE
)
Combining such data sets for of a multiple models
If available, using an rds file to represent the input data in order to preserve all data properties (e.g. factor levels) from data set preparation
After having checked the rare exceptions, feeling confident that NMscanData
should work on all your Nonmem models
This vignette focuses on how to use NMdata
to automate what needs to be trivial: get one dataset out of a Nonmem run, combining all output tables and including additional columns and rows from the input data. After scanning the Nonmem list file and/or control stream for file and column names, the data files are read and combined.
In brevity, the most important steps are:
.lst
): Identify input and output table filesAn additional complication is the potential renaming of input data column names in the Nonmem $INPUT
section. NMscanData
by default (but optionally) follows the column names as read by Nonmem.
This way of reading the output and input data is fully compatible with most other of the great R packages for reading data from Nonmem.
In most cases, the steps above are not too hard to do. But with the large degree of flexibility Nonmem offers, the code will likely have to be adjusted between models. The implementation in NMdata
works for the vast majority of models and aims at preventing and checking for as many caveats as possible. It is fast too.
Default argument values can be configured depending on your setup (data standards, directory structure and other preferences).
Like the rest of NMdata
, this functionality assumes as little as possible about how you work. It assumes nothing about the Nonmem model itself and as little as possible about the organization of data and file paths/names. This makes it powerful for meta analyses, for reading a model developed by someone else - or one written by ourselves when we used to do things slightly differently. It will work out of the box in the vast majority of cases.
We start by attaching NMdata
. Also, I use data.table
for a few post-processing steps. You can just as well use base R or dplyr
if you prefer. Then ggplot2
.
library(NMdata)
## not necessary for NMdata to run, but we use thse in the examples
library(data.table)
library(ggplot2)
theme_set(theme_bw()+theme(legend.position="bottom"))
For the examples we will be using files that are available in the NMdata
package. To type a little less, we use this shortcut function:
file.NMdata <- function(...) system.file(file.path("examples/nonmem",...), package="NMdata")
Depending on your Nonmem setup, habits and preferences, you may name your control streams and list files differently than this vignette. Here, we use the NMdata
default which is .mod
and .lst
. You can easily configure NMdata
to match your preferences. See the FAQ for how. So for now, rest assured that this is easy to adjust and read on.
Try NMscanData
on a control stream or a list file:
res1 <- NMscanData(file.NMdata("xgxr018.lst"))
#> Model: xgxr018
#> Input and output data combined by translation of
#> Nonmem data filters (not recommended).
#>
#> Used tables, contents shown as used/total:
#> file rows columns IDs
#> xgxr018_res.txt 905/905 6/6 NA/NA
#> xgxr018_res_vols.txt 905/905 3/7 150/150
#> xgxr018_res_fo.txt 150/150 1/2 150/150
#> xgxr4.rds (input) 905/1502 21/23 150/150
#> (result) 905 31+2 150
#>
#> Distribution of rows on event types in returned data:
#> EVID Output
#> 0 755
#> 1 150
NMscanData
tells that it has read a model called xgxr018
and how output and input data were combined. We shall see how these properties can be modified in a bit. Then follows an overview of how much data is used from the data files that were read. It used
$TABLE
section(s) in the .lst
file) from which it used all 905 rows and all 15 column, totaling 150 different values of ID
.In the resulting data, 755 out of the 905 rows are EVID==0
, the remaining 150 rows are EVID==1
.
Let’s take a quick look at key properties of the data that was returned. It’s a data.frame
with the additional NMdata
class (for now, we just use it as a data.frame
).
class(res1)
#> [1] "NMdata" "data.frame"
dim(res1)
#> [1] 905 33
The data used for the example is a PK single ascending dose data set, great thanks to the xgxr package authors.
The obtained dataset contains both model predictions (i.e. from output tables) and a character variable, trtact
(i.e. from input data). To the .lst
(output control stream) file path was supplied by us.
head(res1,n=2)
#> ID NOMTIME TIME EVID CMT AMT DV FLAG STUDY KA Q PRED RES WRES V2
#> 1 31 0 0 1 1 3 0 0 1 0.1812 2307400 0 0 0 0.042
#> 2 32 0 0 1 1 3 0 0 1 0.1812 2307400 0 0 0 0.042
#> V3 BLQ CYCLE DOSE PART PROFDAY PROFTIME WEIGHTB EFF0 CL EVENTU
#> 1 0.1785 0 1 3 1 1 0 87.031 56.461 0.7245691 mg
#> 2 0.1785 0 1 3 1 1 0 100.620 45.096 0.7245691 mg
#> NAME TIMEUNIT TRTACT flag trtact model nmout
#> 1 Dosing Hours 3 mg Dosing 3 mg xgxr018 TRUE
#> 2 Dosing Hours 3 mg Dosing 3 mg xgxr018 TRUE
You may have noticed that when reading the model, we were told that 37 columns were read while 39 columns are found in the result. The reason is the last two columns added by NMscanData
called model
and nmout
. model
obviously contains the name of the model which is by default derived from the list file name. See later in the “Recover rows” section what nmout
represents.
Column in output data can overlap, and data can be available in both output and input data. The following main principles are followed by NMscanData
:
FIRSTONLY
or LASTONLY
) tablesuse.input=TRUE
)
$INPUT
section in Nonmem.DROP
or SKIP
) are included by NMscanData
.$INPUT
are named as in the input data file.recover.rows
argument), no information from output is merged onto these rows.Once you have data from NMscanData
, NMinfo
can be used to browse meta information on what data was combined and how that was done.
Above, we were told that “Input and output data combined by translation of Nonmem data filters (not recommended).” Because of the very commonly used ACCEPT
and IGNORE
statements in Nonmem $DATA
sections, the rows in output tables are often a subset of the input data rows. If no other information is available, NMscanData
reads and interprets the ACCEPT
or IGNORE
statements and applies them to the input data before combining with the output data.
A more robust approach is using a unique row identifier in both input data and output data. NMscanData
can use this for merging the data. This means that the ACCEPT
or IGNORE
are not interpreted at all. Even though NMscanData
should work even without, it is always recommended to always include a unique row identifier in both input and output tables (in fact, we just need it in one full-length output table).
The following model happens to have such a unique row identifier in the column called ROW
. The default NMscanData
behavior is to use the row identifier if it can find it. The name of the column with the row identifier can be supplied using the col.row
argument (and the default can be changed using the NMdataConf
function). The default is to look for ROW
.
All features shown below will work whether you supply col.row
or not. We use col.row
because it is more robust and because it allows us to easily trace a row in the analysis back to the source data. We are now told that the data was merged by ROW
- that’s better.
res1.tbl <- NMscanData(file.NMdata("xgxr003.lst"),as.fun=tibble::as_tibble)
#> Model: xgxr003
#> Input and output data merged by: ROW
#>
#> Used tables, contents shown as used/total:
#> file rows columns IDs
#> xgxr003_res.txt 905/905 7/7 NA/NA
#> xgxr003_res_vols.txt 905/905 3/7 150/150
#> xgxr003_res_fo.txt 150/150 1/2 150/150
#> xgxr1.csv (input) 905/1502 21/24 150/150
#> (result) 905 32+2 150
#>
#> Distribution of rows on event types in returned data:
#> EVID Output
#> 0 755
#> 1 150
NMscanData
When reading res1.tbl
, we also added the as.fun
argument. the “as.
” refers to as_tibble
, as.data.frame
, as.data.table
etc. - a function applied to the data before it’s returned by NMscanData
(or any other NMdata
function). So now we have a tibble
:
class(res1.tbl)
#> [1] "NMdata" "tbl_df" "tbl" "data.frame"
I happen to be a data.table
user so I am more comfortable working that way. Instead of using the as.fun
all the time, we will change the default behavior using the NMdataConf
function. Because NMdata
is implemented in data.table
we don’t need to pass the data.table::as.data.table
function but we can (better) use the string "data.table"
(again, data.table
is the exception - for anything else, please pass a function):
NMdataConf(as.fun="data.table")
Notice, NMdataConf
will set the default value for all NMdata
functions that use that argument. So when setting as.fun
this way, we will get the desired class returned from all data generating NMdata
functions.
We don’t want the same information about the dimensions repeated, so we use the quiet
argument this time.
res1.dt <- NMscanData(file.NMdata("xgxr003.lst"),quiet=TRUE)
As expected we got a data.table
this time:
class(res1.dt)
#> [1] "NMdata" "data.table" "data.frame"
An NMdata
object returned by NMscanData
comes with meta information about when and how what was read, and how the data was combined. The NMinfo
function browses this information, and three options are available. It provides three sections of meta data:
“details”: A list including the function call, what options were effective (if input was included, rows recovered, if data was merged by a row identifier or combined by filters etc).
“tables”: Overview of the tables that were read and combined by NMscanData
and properties of the different tables.
“columns”: Information on the columns that were treated by NMscanData
(see example below).
The follwing show the “columns” information as example. Remember, we are still getting a data.table because we used NMdataConf
to change the configuration. We use the data.table
print function to only look at first and last ten rows.
print(NMinfo(res1,info="columns"),nrows=20,topn=10)
#> variable file source level COLNUM
#> 1: ID xgxr018_res_vols.txt output row 1
#> 2: NOMTIME xgxr4.rds input row 2
#> 3: TIME xgxr4.rds input row 3
#> 4: EVID xgxr4.rds input row 4
#> 5: CMT xgxr4.rds input row 5
#> 6: AMT xgxr4.rds input row 6
#> 7: DV xgxr018_res.txt output row 7
#> 8: FLAG xgxr4.rds input row 8
#> 9: STUDY xgxr4.rds input row 9
#> 10: KA xgxr018_res.txt output row 10
#> ---
#> 31: trtact xgxr4.rds input row 31
#> 32: model <NA> NMscanData model 32
#> 33: nmout <NA> NMscanData row 33
#> 34: DV xgxr018_res_vols.txt output row NA
#> 35: PRED xgxr018_res_vols.txt output row NA
#> 36: RES xgxr018_res_vols.txt output row NA
#> 37: WRES xgxr018_res_vols.txt output row NA
#> 38: ID xgxr4.rds input row NA
#> 39: DV xgxr4.rds input row NA
#> 40: ID xgxr018_res_fo.txt output id NA
The column names are sorted by the order in the resulting dataset, the order given by the COLNUM
column. The variables in the bottom that have COLNUM==NA
were redundant when combining the data (the same columns were included from other sources). The file names and their source (input/output) and a “level” are given. “level” is the information level of the source. Input data and full-length output tables are “row” level, a firstonly or lastonly table is id-level. And then there is the model
column added by NMscanData
which is obviously model-level. nmout
is the other column added by NMscanData
and both model
and nmout
have NA
file and NMscanData
as source.
Let’s have a quick look at the data we got back. The following is done with data.table
. The comments in the code should make it clear what happens if you are not familiar with data.table
. You can do this with base::tapply
, stats::aggregate
, a combination of dplyr::group_by
and dplyr::summarize
, or whatever you prefer.
gmPRED
is calculated for sample times only and represents the geometric mean of population prediction (PRED
) by dose and nominal time.
## trtact is a character. Make it a factor with levels ordered by
## numerical dose level. The := is a data.table assignment within
## res3. In dplyr, you could use mutate.
res1.dt[,trtact:=reorder(trtact,DOSE)]
## Derive geometric mean pop predictions by treatment and nominal
## sample time. In dplyr, use group_by, summarize, and ifelse?
res1.dt[EVID==0,gmPRED:=exp(mean(log(PRED))),
by=.(trtact,NOMTIME)]
Notice, how little data is shown on the small doses. Remember, only 905 of the 1502 rows in the input data were used? Most of the rows excluded in the analysis are so due to observation being below the quantification limit (BLQ). The next section shows how to recover all the input data rows with NMscanData
.
We may want to include the input data that was ignored by Nonmem. Use recover.rows=TRUE
to include all rows from input data.
res2 <- NMscanData(file.NMdata("xgxr014.lst"),recover.rows=TRUE)
#> Model: xgxr014
#> Input and output data merged by: ROW
#>
#> Used tables, contents shown as used/total:
#> file rows columns IDs
#> xgxr014_res.txt 905/905 12/12 NA/NA
#> xgxr2.rds (input) 1502/1502 22/24 150/150
#> (result) 1502 34+2 150
#>
#> Distribution of rows on event types in returned data:
#> EVID Input only Output
#> 0 597 755
#> 1 0 150
Besides the model
column holding the model name, NMscanData
creates one other column by default. nmout
is a boolean column created by NMscanData
expressing whether each row was in the output data (nmout==TRUE
) or they were recovered from the input data (nmout==FALSE
).
We recognize these numbers from the message from NMscanData
- the number of rows in output (905) and number of rows from input only (597). Since we changed the default value of as.fun
with NMdataConf
, res2
is a data.table
.
res2[,.N,by=nmout]
#> nmout N
#> 1: TRUE 905
#> 2: FALSE 597
We make use of the nmout
column to only calculate gmPRED
for observations (EVID==0
) processed by Nonmem.
## add geometric mean pop predictions by treatment and nominal sample
## time. Only use sample records.
res2[EVID==0&nmout==TRUE,
gmPRED:=exp(mean(log(PRED))),
by=.(trtact,NOMTIME)]
Obviously, we were lucky that meaningful values were assigned to DV
for the BLQ and pre-dose samples in input data, so we in this case could easily plot all the data.
NMscanData
by default adds a column called model
for convenience when working with multiple models. You can specify both column name (which is by model
default) and model name (contents of that column) as arguments in NMscanData
. Using NMdataConf
, You can also configure the default column name and the function that generates the model name.
The default is to derive the model name from the lst
file name (say, xgxr001.lst
becomes xgxr001
). In the following we use this to compare population predictions from two different models. We read them again just to show the use of the argument to name the models ourselves. Remember, we configure NMdata
’s as.fun
option so we are working with data.table
and we easily stack with rbind
(rbind.data.table
) filling in NA
’s. We add a couple of options to specify how input and output data are to be combined.
NMdataConf(as.fun="data.table", ## already set above, repeated for completeness
col.row="ROW", ## This is default, included for completeness
merge.by.row=TRUE ## Require input and output data to be combined by merge
)
res1.m <- NMscanData(system.file("examples/nonmem/xgxr001.lst", package="NMdata"),
quiet=TRUE)
## using a custom modelname for this model
res2.m <- NMscanData(system.file("examples/nonmem/xgxr014.lst", package="NMdata"),
modelname="One compartment",
quiet=TRUE)
## notice fill is an option to rbind with data.table (like bind_rows in dplyr)
res.mult <- rbind(res1.m,res2.m,fill=T)
## Notice, the NMdata class disappeared
class(res.mult)
#> [1] "data.table" "data.frame"
res.mult[EVID==0&nmout==TRUE,
gmPRED:=exp(mean(log(PRED))),
by=.(model,trtact,NOMTIME)]
In this, we specifically wanted to rename one model for illustration of the modelname
argument. We can pass a function to customize how NMscanData
derives it from the list file path. This one skips the characters and leading zeros, so we just get an integer. We could pass use the modelname
argument in NMdata
but why not changed the default instead?
namefun <- function(path) sub("^[[:alpha:]0]+","",fnExtension(basename(path),""))
NMdataConf(modelname=namefun)
res1.m <- NMscanData(system.file("examples/nonmem/xgxr001.lst", package="NMdata"),
quiet=TRUE)
res2.m <- NMscanData(system.file("examples/nonmem/xgxr014.lst", package="NMdata"),
quiet=TRUE)
## notice fill is an option to rbind with data.table (like bind_rows in dplyr)
res.mult <- rbind(res1.m,res2.m,fill=T)
res.mult[,.N,by=model]
#> model N
#> 1: 1 905
#> 2: 14 905
## resetting default
NMdataConf(modelname=NULL)
NMdataConf
can be used to change a lot of the default behaviour of the functions in NMdata
so it fits in with your current setup and preferred work flow.
rds
Return to the example above creating the dataset res22
. Notice in the list of tables in the message from NMscanData
, that input data was a .rds
file. This is why we could sort the plots correctly on the dose level without reordering the factor levels first.
res2[,class(trtact)]
#> [1] "factor"
res2[,levels(trtact)]
#> [1] "Placebo" "3 mg" "10 mg" "30 mg" "100 mg" "300 mg"
If the argument use.rds
is TRUE
, NMscanData
will look for an rds file next to the input data file (which is a delimited text file) the exact same name as the text file except the extension must be .rds
rather than say .csv
(for Nonmem and NMscanData
, the extension of the delimited text file doesn’t matter). If it finds the rds
file, this will be used instead. No checks are done of whether the contents are similar in any way to the delimited text file which is ignored in this case.
There are three advantages of using rds
files:
fread
from data.table
which is extremely fast for delimited files so in many cases this difference can be small).rds
. This can be a big advantage if you are transfering files or reading over a network connection. NMdata
is generally very fast (thanks to data.table
) so file/network access (I/O) is likely to be the main bottleneck.If you write Nonmem datasets with the NMdata::NMwriteData
, you can get an rds
file automatically, exactly where NMscanData
will look for it. Creating datasets using NMdata
is described in this vignette.
You probably want to use NMdataConf
to change the default behavior if you don’t want to use rds
files.
NMscaData
building blocksEach of the steps involved in reading and combining the data from a model run can be done separately.
The lst
file was scanned for output tables, and they were all read (including interpreting the possible firstonly
option). The input data has been used based on the $DATA
and $INPUT
sections of the control stream. The key steps in this process are available as independent functions.
NMreadTab
: Read an Nonmem output table based on the path to the output table file.
NMscanTables
: Read all output data files defined in a Nonmem run. Return a list of tables (as data.frames or data.tables).
NMtransInput
: Read input data based on a Nonmem file. Data will be processed and named like the Nonmem model. ACCEPT
and IGNORE
filters can be applied as well. There are a few limitations to this functionality at this point. More about them below.
The answer to this should be as close to “nothing” as possible - that’s more or less the aim of the function. You just have to make sure that the information that you need is present in input data and output data. No need to output information that is unchanged from input, but make sure to output what you need (like IPRED
, CWRES
, CL
, ETA1
etc which cannot be found in input). Some of these values can be found from other files generated by Nonmem but notice: NMscanData
uses only input and output data.
It is recommended to always use a unique row identifier in both input and output data. This is the most robust way to merge back with input data. In firstonly tables, include the subject ID. Again, everything will most likely work even if you don’t, I personally don’t like relying on “most likely” when I can just as well have robustness.
Even if there are a few limitations to what models NMscanData
can handle, there is a good chance you will never run into any of them, as they are mostly quite rare. If you do, reach out to me, and we’ll figure it out.
If merging with input data, the input data must be available as was when the model was run. If you want to avoid this potential issue, Nonmem can be run in a wrapper script that either copies the input data, or runs NMscanData
and saves the output in a compressed file format (like rds
or zip
).
Nested ACCEPT
and IGNORE
statements are not supported at this point. The resulting number of rows after applying filters is checked against row-level output table dimensions (if any available). In other words, you have to be unlucky to run into trouble without an error. But it is always recommended to use a unique row identifier in both input and output tables in order to avoid relying on interpretation of Nonmem code.
The RECORDS
and NULL
options in $DATA
are not implemented. If using RECORDS
, please use the col.row
option to merge by a unique row identifier.
Nonmem supports a clocktime input format for a column called TIME in input data. Based on a day counter and a character (“00:00”) clock format, Nonmem (or rather, NM-TRAN
) can calculate the individual time since first record. This behaviour is not mimicked by NMscanData, and the only ways to get TIME in this case are to either include it in an output TABLE
or to code the translation yourself after calling NMscanData
. Of course, this is on the todo list.
For now, only output tables returning either all rows or one row per subject can be merged with input. Tables written with options like FIRSTLASTONLY
(two rows per subject) and OBSONLY
are disregarded with a warning (you can read them with NMscanTables
). LASTONLY
is treated like FIRSTONLY
, i.e. as ID-level information if not available elsewhere.
In this vignette you should have learned to
NMscanData
can automatically read and combine all output and input data, only based on the path to the list (.lst) file
merge.by.row
is the argument of interestNMdata
to return your favorite data class
NMdataConf(as.fun="data.table")
for data.table
NMdataConf(as.fun=tibble::as_tibble)
for tibbles (tbl
)NMscanData
NMinfo
on the result coming out of NMscanData
recover.rows=TRUE
model
will hold the model name which you can use when combining (rbind
) multiple model data setsmodelname
option to change the model name or how the model name is derived from the list file path.rds
file to preserve all input data
NMdata::NMwriteData
writes this rds
file by defaultYou should have seen that NMscanData
have very little limitations in what Nonmem models it can read. You should not have to change anything in the way you work to make use of NMscanData
.