Running model fitsSource:
In this section, we suggest a workflow to help you maintain organized modeling projects.
When beginning a new modeling project, it is convenient to use the
PMtree(). This command will set up a new directory
in the current working directory named whatever you have included as the
In the above example, a directory called “DrugX” will be created in
the current working directory in R, which you can check with the
getwd function. Beneath the new DrugX directory, several
subdirectories will be also created.
- Rscript contains a skeleton R script to begin Pmetrics runs in the new project.
- Runs should contain all files required for a run (described next) and it will also contain the resulting numerically ordered run directories created after each Pmetrics NPAG or IT2B run.
- Sim can contain any files related to simulations
- src is a repository for original and manipulated source data files
You are free to edit this directory tree structure as you please, or make your own entirely.
There is a full tutorial encoded inside Pmetrics to teach new users the basic functionality of the whole package. To start that tutorial in R type:
Follow the instructions prompted in the terminal.
To setup a R6 Pmetrics run, you must provide
PM_model() objects. Once created, it does not matter
where the files (if there are any) are located on your system because
Pmetrics will use those objects, not the files.
To bring these together, use the
creator. It only needs two arguments: the name of the data file in the
working directory or in memory loaded via the Legacy
PMreadMatrix()and a model object.
will accept a model object created by
PM_model or the name
of a model file in Legacy format and in the working directory.
#Example 1 - data and model objects dat <- PM_data$new("data.csv") mod1 <- PM_model$new(list(...)) fit1 <- PM_fit$new(dat, mod1) #Example 2 - data file and PM_model object fit2 <- PM_fit$new(data = "data.csv", model = mod1) #Example 3 - data object and model file PMdata <- PMreadMatrix("data.csv") fit3 <- PM_fit$new(data = PMdata, model = "model.txt")
The fit object also contains a couple of methods attached to it.
fit1$check() #This will check the consistency between the model and the data fit2$save("fit2.rds") #This will save the fit to a file in your working directory fit2_again <- PM_fit$load("fit2.rds") #This will load the fit again from disk
To see the full list of methods available see
When you wish to execute a Pmetrics run, you must ensure that
appropriate Pmetrics model.txt and data.csv files are in the working
directory, i.e. the Runs subdirectory of the project directory. R can be
used to help prepare the data.csv file by importing and manipulating
read.csv). The Pmetrics function
PMcheck() can be used to check a .csv file or an R
dataframe that is to be saved as a Pmetrics data.csv file for errors. It
can also check a model file for errors in the context of a datafile,
e.g. covariates that do not match.
attempts to automatically rid data files of errors. The function
PMwriteMatrix can be used to write the R data object in the
correct format for use by IT2B, NPAG, or the Simulator.
#default run parameters fit3$run() #change the cycle number from default 100 fit3$run(cycles=500) #change the engine from default NPAG fit3$run(engine = "IT2B")
As you will see in the skeleton R script made by
PMtree() and placed in the Rscript subdirectory, if this is
a first-time run, the R commands to run IT2B or NPAG are as follows.
Recall that the “#” character is a comment character.
The first line will load the Pmetrics library of functions. The second line sets the working directory to the specified path. The third line generates the batch file to run NPAG or IT2B and saves it to the working directory.
NOTE: On Mac systems, the batch file will be automatically launched in a Terminal window. On Windows systems prior to version 1.9, the batch file must be launched manually by double clicking the npscript.bat or itscript.bat file in the working directory. As of version 1.9, Windows users no longer need to do this.
NPrun() are described in full
detail via their help commands in R. At minimum, they require a data
file and a model file. If the default names of “data.csv” and
“model.txt” are used, they may be called with no arguments. Again, the
data and model files must be in the current working directory, usually
the Runs folder.
Both functions return the full path of the output directory to the clipboard. By default, runs are placed in folders numbered sequentially, beginning with “1”.
When you wish to execute a Pmetrics run, you must ensure that both of
the appropriate Pmetrics data.csv and model.txt files are in the working
directory, i.e. the Runs subdirectory of the project directory. The
names are supplied as arguments to
ERRrun. A shorthand notation is to
supply the number of a previous run for either the data, model or both
files so that you do not have to manually copy them into the working
You can also download sample data and scripts from the Pmetrics downloads section of our website. Edit prior versions of model files to make new model files.
After the execution is done, you can load the output into R using
PM_load(). Note the underscore “_” to distinguish this
function from the Legacy
PMload(). The argument to the
function is the run number which you wish to load, corresponding to a
folder with the same number in your Runs folder (if you
my_run <- PM_load(1)
This creates a
PM_result() object called
my_run. Detailed information about the different elements
contained in the result object can be accessed via
?PM_result or by typing the result object into the
After that, that object can be used to access the different elements of the results, for example:
Now the output of IT2B or NPAG needs to be loaded into R, so the next command does this.
Details of these commands and what is loaded are described in the R
?PMload. The run_number
should be included within the parentheses to be appended to the names of
loaded R objects, allowing for comparison between runs,
PMload(1). Finally, at this point other Pmetrics
commands can be added to the script to process the data, such as the
Of course, the full power of R can be used in scripts to analyze data, but these simple statements serve as examples.
If you do not use the
PMtree structure, we suggest that
the R script for a particular project be saved into a folder called
“Rscript” or some other meaningful name in the working directory.
Folders are not be moved by the batch file. Within the script, number
runs sequentially and use comments liberally to distinguish runs, as
Remember in R that the command
provide examples for the specified function. Most Pmetrics functions