Pmetrics data are either R6 objects loaded into memory or files,
usually comma-separated (.csv). It is possible to use other separators,
like the semicolon, by setting the appropriate argument with
setPMoptions(sep = ";", dec = ",") #changes field separator to ";" from default "," #and decimal separator from "." to ","
Examples of programs that can save .csv files are any text editor (e.g. TextEdit on Mac, Notepad on Windows) or spreadsheet program (e.g. Excel). Please keep the number of characters in the file name ≤ 8.
R6 Pmetrics introduces a new concept, the data object. The idea of
this object is to represent a dataset that is going to be
modeled/simulated. All its behaviour is represented by the class
PM_data. This class allows datasets to be checked, plotted,
written to disk and more. Use
PM_data() object by reading the file.
#assume that data.csv is in the working directory data1 <- PM_data$new("data.csv")
You can also build an appropriate data frame in R and provide that as
an argument to
#assume df is data frame with at least these columns: #id, time, dose, out data1 <- PM_data$new(df)
Once you have created the
PM_data() object, you never
need to create it again during your R session. You also don’t have to
bother copying the data file to the Runs folder each time you run the
You must always have the the data file in the current working
directory. You can manually copy it there from a previous run or some
other folder or use the shortcut of providing a prior run number as an
R6 Pmetrics can use file or data frame input, unlike Legacy which can
only take file input. The format is also much more flexible in R6. The
only required columns are those below. There is no longer a requirement
for a header or to prefix the ID column with “#”. However, any
subsequent row that begins with “#” will be ignored, which is helpful if
you want to exclude data from the analysis, but preserve the integrity
of the original dataset, or to add comment lines. The column order can
be anything you wish, but the names should be the same as in the Legacy
format below. Ultimately,
PM_data$new() converts all valid
data into the format used in Legacy Pmetrics.
ID This field can be numeric or character and identifies each individual. All rows must contain an ID, and all records from one individual must be contiguous. IDs should be 11 characters or less but may be any alphanumeric combination. There can be at most 800 subjects per run.
TIME This is the elapsed time in decimal hours since the first event. You may also specify time as clock time if you include a DATE column below. In that case the default format is HH:MM. Other formats can be specified. See
PM_data()for more details. Every row must have an entry, and within a given ID, rows must be sorted chronologically, earliest to latest.
DATE This column is only required if TIME is clock time, detected by the presence of “:”. The default format of the date column is YYYY-MM-DD. As for TIME, other formats can be specified. See
?PM_datafor more details.
DOSE This is the dose amount. It should be “.” for observation rows.
OUT This is the observation, or output value, e.g., concentration. For dose rows, it should be “.”. If an observation occurred at a given time, but the result was missing, e.g. sample lost or below the limit of quantification, this should be coded as -99. There can be at most 150 observations for a given subject.
COVARIATES... Columns with names other than the above will be interpreted as covariates.
PM_data() reads a file, it will standardize it to
the Legacy format below. This means some inferences are made. For
example, in the absence of EVID, all doses are interpreted as oral. If
they are infusions, DUR must be included as for Legacy files below. EVID
only needs to be included if EVID=4 (reset event) is required, described
below. Similarly, INPUT and OUTEQ are only required if multiple inputs
or outputs are being modeled. Lastly, ADDL and II are optional. All
inferred columns function the same as below for Legacy.
Lastly, the standardized data are checked for errors with
PMcheck(), which no longer needs to be called directly by
Legacy Pmetrics can only use file input, typically a .csv, although
as for R6 above, other separators are possible by using
setPMoptions(). The format below is rigid for Legacy. All
columns are required, and the order, capitalization and names of the
header and the first 12 columns are fixed. All entries must be numeric,
with the exception of ID and “.” for non-required placeholder entries.
Any subsequent row that begins with “#” will be ignored, as above.
A full example data file is below, with details following.
POPDATA DEC_11 This is the fixed header for the file and must be in the first line. It identifies the version. It is not the date of your data file.
#ID This field must be preceded by the “#” symbol to confirm that this is the header row. It can be numeric or character and identifies each individual. All rows must contain an ID, and all records from one individual must be contiguous. Any subsequent row that begins with “#” will be ignored, which is helpful if you want to exclude data from the analysis, but preserve the integrity of the original dataset, or to add comment lines. IDs should be 11 characters or less but may be any alphanumeric combination. There can be at most 800 subjects per run.
EVID This is the event ID field. It can be 0, 1, or 4. Every row must have an entry.
0 = observation
1 = input (e.g. dose)
2, 3 are currently unused
4 = reset, where all compartment values are set to 0 and the time counter is reset to 0. This is useful when an individual has multiple sampling episodes that are widely spaced in time with no new information gathered. This is a dose event, so dose information needs to be complete.
TIME This is the elapsed time in decimal hours since the first event. It is not clock time (e.g. 09:30), although the
PMmatrixRelTime()function can convert dates and clock times to decimal hours. Every row must have an entry, and within a given ID, rows must be sorted chronologically, earliest to latest.
DUR This is the duration of an infusion in hours. If EVID=1, there must be an entry, otherwise it is ignored. For a bolus (i.e. an oral dose), set the value equal to 0.
DOSE This is the dose amount. If EVID=1, there must be an entry, otherwise it is ignored.
ADDL This specifies the number of additional doses to give at interval II. It may be missing for dose events (EVID=1 or 4), in which case it is assumed to be 0. It is ignored for observation (EVID=0) events. Be sure to adjust the time entry for the subsequent row, if necessary, to account for the extra doses. If set to -1, the dose is assumed to be given under steady-state conditions. ADDL=-1 can only be used for the first dose event for a given subject, or an EVID=4 event, as you cannot suddenly be at steady state in the middle of dosing record, unless all compartments/times are reset to 0 (as for an EVID=4 event). To clarify further, when ADDL=-1, all compartments in the model will contain the predicted amounts of drug at the end of the 100th II interval.
II This is the interdose interval and is only relevant if ADDL is not equal to 0, in which case it cannot be missing. If ADDL=0 or is missing, II is ignored.
INPUT This defines which input (i.e. drug) the DOSE corresponds to. Inputs are defined in the model file.
OUT This is the observation, or output value. If EVID=0, there must be an entry; if missing, this must be coded as -99. It will be ignored for any other EVID and therefore can be “.”. There can be at most 150 observations for a given subject.
OUTEQ This is the output equation number that corresponds to the OUT value. Output equations are defined in the model file.
C0, C1, C2, C3 These are the coefficients for the assay error polynomial for that observation. Each subject may have up to one set of coefficients per output equation. If more than one set is detected for a given subject and output equation, the last set will be used. If there are no available coefficients, these cells may be left blank or filled with “.” as a placeholder.
COVARIATES... Any column after the assay error coefficients is assumed to be a covariate, one column per covariate. The first row for any subject must have a value for all covariates, since the first row is always a dose. Covariate values are applied at the time of doses.
As we have seen,
PM_data$new("filename") will read an
appropriate data file in the current working directory to create a new
PM_data$write("filename") will write the
PM_data() object to a file called “filename”. This can be
useful if you have loaded or created a data file and then changed it in
PM_data$new() automatically standardizes the data into
the full format used by Legacy. This includes conversion of calendar
date / clock time into decimal elapsed time.
PM_data$new() automatically calls
so the data are validated as the data object is created.
PMreadMatrix("filename", ...) reads filename
and creates a PMmatrix object in R. However, unlike R6, it cannot be
used to run a model. For that, you need to copy the file into the
working directory each time, either yourself or by using the
NPrun(data = 1, ...) shortcut, for example.
PMwriteMatrix(data.frame, "filename", ...) writes an
appropriate data frame as a new .csv file. It will first check the
data.frame for errors via the
PMcheck() function below, and
writing will fail if errors are detected. This can be overridden with
No standardizing occurs in Legacy as the file format must always be
standard. To convert calendar dates and clock times to elapsed decimal
PMmatrixRelTime(). This function converts dates
and clock times of specified formats into relative times for use in the
NPAG, IT2B and Simulator engines. The output is used to create a data
frame with relative times that can be saved as a new .csv file with
PMwriteMatrix(), which in turn serves as input to a
PMcheck() will check a .csv file named filename
PMmatrix data frame containing a previously loaded
.csv file (the output of
PMreadMatrix()) for errors which
would cause the analysis to fail. If a model file is provided, and the
data file has no errors, it will also check the model file for errors.
If it finds errors, it will generate a new errors.xlsx file
with all errors highlighted and commented so that you can find and
correct them easily.
PMcheck() is automatically called with
SIMrun() call, unless the
nocheck = T
argument is used.
The following functions are the same in either R6 or Legacy.
PMwrk2csv()This function will convert old-style, single-drug USC*PACK .wrk formatted files into Pmetrics data .csv files.
PMmb2csv()This function will convert USC*PACK .mb files into Pmetrics data .csv files.
NM2PM()Although the structure of Pmetrics data files is similar to NONMEM, there are some differences. This function attempts to automatically convert to Pmetrics format. It has been tested on several examples, but there are probably NONMEM files which will cause it to crash. Running
PMcheck()afterwards is a good idea.
|Read data file||PM_data$new()||PMreadMatrix()|
|Check data file||Embedded in PM_data$new()||PMcheck()|
|Write data file||PM_data$write()||PMwriteMatrix()|
|Convert calendar dates and clock times||Embedded in PM_data$new()||PMmatrixReltime()|
|Convert from old USC*PACK .wrk format||PMwrk2csv()||PMwrk2csv()|
|Convert from NONMEM||NM2PM()||NM2PM()|
|Convert from old USC*PACK .mb format||PMmb2csv()||PMmb2csv()|