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Fitting data to models

In this section, we suggest a workflow to help you maintain organized modeling projects.

Setting up a Pmetrics project


When beginning a new modeling project, it is convenient to use the command PMtree(). This command will set up a new directory in the current working directory named whatever you have included as the “project name”.


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.

Getting the required inputs to run Pmetrics


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_data() and 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 PM_fit$new() object 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. PM_fit() 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 ?PM_fit.


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 spreadsheets (e.g. 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. PMcheck(...,fix=T) 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.

Running the model to fit the data


Once the PM_fit() object is created the $run() method will execute the fit. Arguments for this method can be found in the help for NPrun() and ITrun().

#default run parameters

#change the cycle number from default 100

#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.

#Run 1 - add your run description here
setwd("working directory")
NPrun() #for NPAG or ITrun() for IT2B

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.

ITrun() and 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 NPrun, ITrun, and 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 directory.

#Using default names data.csv and model.txt

#Using custom names
ITrun(model = "model1.txt", data = "mydata.csv")

#Grab data from run 1 and use default model.txt

#Use model and data from run 2 and continue where run 2 ended
NPrun(data=2, model=2, prior=2, cycles=1000)

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.

Loading results after a completed run


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 made one).

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 terminal.

After that, that object can be used to access the different elements of the results, for example:

exRes <- PM_load(1)
exRes$final$plot() #see ?plot.PM_final
exRes$op$plot(type="pop") #see ?plot.PM_op,
exRes$data$plot(overlay = F, 
                line = list(pred = list(exRes$post, color = "green"), join = F),
                marker = list(symbol = "diamond-open", color = "blue"), 
                log = T) # see ?plot.PM_data


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 documentation ?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, e.g. PMload(1). Finally, at this point other Pmetrics commands can be added to the script to process the data, such as the following.

plot(op.1,type="pop") #or plot(op.1$pop1)
plot(op.1) #default is to plot posterior predictions for output 1

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 shown below.

#Run 1 - Ka, Kel, V, all subjects
setwd("working directory")
NPrun() #assumes model="model.txt" and data="data.csv"

Remember in R that the command example(function) will provide examples for the specified function. Most Pmetrics functions have examples.