\clearpage
knitr::opts_chunk$set(out.width = "100%", warning = FALSE, message = FALSE) library(ggplot2) library(knitr) library(ggPMX) library(data.table) setDTthreads(2) theophylline <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline, "Monolix") input_data <- file.path(theophylline, "data_pk.csv") ctr <- theophylline()
The ggPMX
package generates standard diagnostic plots and tables for mixed effect models used in Pharmacometric (PMX) activities. The tool is built upon the ggplot2 package and supports models developped either with Monolix, NONMEM or nlmixr software. The current release (1.2) supports models fitted with Monolix versions 2016 and later, NONMEM version 7.2 and later and nlmixr.
The package aims to provide a workflow that is consistent, efficient and which results in high quality graphics ready to use in official documents and reports. The package allows a high degree of flexibility and customization, yet providing an acceptable default setting. The package also allows to fully automate plots and report generation.
The general context is the analysis of mixed effect models fitted to data. ggPMX was developed in the framework of Pharmacometric activities, in which case the model is a population pharmacokinetic (PK) and/or pharmacodynamic (PD) model and the data is clinical or pre-clinical PK and/or PD data.
In the context of model building, evaluation and qualification, it is good practice to assess the goodness-of-fit of models by inspecting (qualitatively and quantitatively) a set of graphs that indicate how well the model describes the data. Several types of diagnostic plots allow to evaluate a mixed effects model fit, the most common being:
a. residual-based plots b. prediction-based plots c. empirical Bayes estimates (EBE)-based plots d. simulation-based plots.
The following figures are examples of diagnotic plots using data from Monolix.
ctr <- theophylline() ctr %>% pmx_plot_dv_pred ctr %>% pmx_plot_npde_time ctr %>% pmx_plot_vpc ctr %>% pmx_plot_eta_box ctr %>% pmx_plot_eta_matrix( shrink=list(size=3, hjust=1.5, fun="var") )
This document introduces the ggPMX functionalities and syntax.
The high level architecture is represented in the figure below. The key components of the package are the following:
knitr::include_graphics("./ggPMX_arch.png")
The package is coded using object-oriented programming meaning that information is encoded in objects. The user can change or print the content of objets via functions. Such an implementation allows to have code that is modular and easily customizable.
The typical workflow of ggPMX is as follows:
The most important task for the user is the Controller creation. This step requires careful consideration because it involves different options according to the type of model (PK or PKPD) and software (Monolix, NONMEM or nlmixr) used for model fitting. The next section describes the Controller creation for the different possible cases.
Once the Controller is created, it implicitly calls the Reader and creates the diagnostic plots. The user can then generate the graphs by calling pre-defined functions. The same syntax is used independent of the model structure (PK or PKPD model) and of the fitting software (Monolix, NONMEM or nlmixr).
The Reporter creates one report per endpoint, i.e., one report for PK and one for each PD endpoint.
For the sake of this document, three types of datasets are defined.
A diagnostic session starts with the creation of a Controller. The Controller is the "user interface" of the package and allows to conrol all possible options. It is a container that stores configuration fields (model- and input data-related information), datasets and plots. It can be used as a reference object in which the user can see the names of the exisitng plots, the names of the ggPMX
datasets, etc. The syntax of the Controller creation differs depending on the software used for model fitting and on the number of model endpoints (or outputs). This section presents different cases of Controller creation. For simplicity, the case of models with one single output is presented first, then generalized to several outputs. Other Controller creation functions can be used with the different fitting softwares. Note that all these functions can also be used with models with several outputs.
In general, models with only one endpoint (or output) are mostly PK models, but these could also be k-PD models.
To illustrate ggPMX
functionalities, the single-endpoint built-in model called theophylline is used hereafter. The theophylline population PK example has the following characteristics:
The input modeling dataset has the following columns:
theophylline_path <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline_path, "Monolix") input_data_path <- file.path(theophylline_path, "data_pk.csv") input_data_theo <- read.csv(input_data_path) head(input_data_theo)
Note that the DVID (or CMT/YTYPE) column is missing, but since this is a single-endpoint model, it is not necessary in that case.
An example of Contoller creation is wrapped in a function called "theophylline()" for quick reference:
ctr <- theophylline()
pmx_mlx()
The controller initialization using the Monolix controller pmx_mlx()
, which is a wrapper function for pmx()
with sys="mlx"
(See Appendix A).
theophylline_path <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline_path, "Monolix") input_data_path <- file.path(theophylline_path, "data_pk.csv") ctr <- pmx_mlx( directory = work_dir, input = input_data_path, dv = "Y" )
pmx_mlxtran()
The controller initialization can be simplified by using the Monolix controller pmx_mlxtran()
. This function parses the mlxtran file of a Monolix project and assigns automatically the different fields necessary to the Controller creation. The only mandatory argument is file_name, the path to the mlxtran file.
mlxtran_path <- file.path(system.file(package = "ggPMX"), "testdata", "1_popPK_model", "project.mlxtran") ctr <- pmx_mlxtran(file_name = mlxtran_path)
The user can verify the content of the Controller and how parameters are assigned by printing it.
pmx_nm()
The controller initialization using the NONMEM controller pmx_nm()
is based on reading functions of the xpose package.
It is highly recommended (but not required) to use the "sdtab, patab, cotab, catab" table naming convention followed by a run number (e.g. sdtab001,cotab001)
This will enable automatic recognition of covariates. It is also recommended to name the model files accordingly (e.g. run001.lst).
In order to generate a VPC a simulation dataset is required (see section about VPC)
For controller generation it is recommended to use the model file:
nonmem_dir <- file.path(system.file(package = "ggPMX"), "testdata","extdata") ctr <- pmx_nm( directory = nonmem_dir, file = "run001.lst" )
or the run number. The standard prefix is "run", however can be specified using prefix
nonmem_dir <- file.path(system.file(package = "ggPMX"), "testdata","extdata") ctr <- pmx_nm( directory = nonmem_dir, runno = "001" #can be a string or a number )
pmx_nlmixr()
It is simple to create a ggPMX controller for a nlmixr object using pmx_nlmixr()
.
Using the theophylline example with a nlmixr model we have:
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- 1 # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr(one.cmt, theo_sd, est="saem", control=list(print=0))
The fit
object is a nlmixr fit; You can read it into the nlmixr controller by:
ctr <- pmx_nlmixr(fit, vpc = FALSE ## VPC is turned on by default, can turn off )
The following are some optional arguments to the controller function (for details of each option, see the corresponding section or use ?pmx_mlx
, ?pmx_nm
, ?pmx_nlmixr
):
pmx_settings()
) shared between all plotspmx_endpoint()
) or integer or charcater of the endpoint code (depends on the fitting software)Models with more than one endpoint (or output) are mostly PKPD models, but these could also be, for example, PK binding models in which there are measurements and predictions of both PK and its target.
ggPMX produces one diagnostics report per endpoint. As a consequence, the endpoint (if more than one) should be set at the time of the Controller creation in order to filter the observations dataset and to keep only the values corresponding to the endpoint of interest. There are two ways of dealing with endpoints, using pmx_endpoint() (only for Monolix), or a simplified syntax for endpoints which is supported by all software outputs.
To handle this, the user creates an "endpoint" object using the function pmx_endpoint()
having the following attributes:
list(predictions="predictions1",finegrid ="finegrid1")
To illustrate the Controller creation with multiple-endpoint models, a built-in PKPD example is used. The input dataset is called pk_pd.csv and has the following columns.
pkpd_path <- file.path(system.file(package = "ggPMX"), "testdata", "pk_pd") pkpd_work_dir <- file.path(pkpd_path, "RESULTS") pkpd_input_file <- file.path(pkpd_path, "pk_pd.csv") input_data <- read.csv(pkpd_input_file) head(input_data)
The dvid column contains values=3 for PK (first endpoint) and dose and =4 for PD (second endpoint). Monolix2016 outputs are found in folder RESULTS/ which contains predictions1.txt and finegrid1.txt for PK predictions, and predictions2.txt and finegrid2.txt for PD predictions. The Endpoint and Controller objects are created as follows:
pkpd_path <- file.path(system.file(package = "ggPMX"), "testdata", "pk_pd") pkpd_work_dir <- file.path(pkpd_path, "RESULTS") pkpd_input_file <- file.path(pkpd_path, "pk_pd.csv") ep <- pmx_endpoint( code = "4", label = "some_label", unit = "some_unit", file.code = "2", # will use predictions2.txt and finegrig2.txt trans = "log10" ) ctr <- pmx_mlx( directory = pkpd_work_dir, input = pkpd_input_file, dv = "dv", dvid = "dvid", endpoint = ep )
For NONMEM and nlmixr users, endpoint can be simply specified in the controller creation by e.g. endpoint = 1
NONMEM
ctr <- pmx_nm( directory = nonmem_dir, file = "run001.lst", endpoint = 1 ## select the first endpoint dvid = "DVID" ## use this column as observation id )
nlmixr
ctr <- pmx_nlmixr(fit, endpoint = 1 ## select the first endpoint dvid = "DVID" ## use this column as observation id )
Also for Monolix users, a simplified syntax for the Endpoint creation exists in the case where the endpoint code matches the files post-fixes (code=1 corresponds to predictions1.txt, code=2 corresponds to predictions2.txt). Instead of passing a pmxEndpoint object as argument of the Controller, the user can specify the numerical value corresponding to the YTYPE/CMT/DVID column.
pmx_mlx( dvid = "YTYPE", ## use this column as observation id endpoint = 1, ## select the first endpoint ...) ## other pmx parameters , config, input,etc..
Internally, a pmxEndpoint object will be created, and observations having YTYPE=x will be filtered.
Besides the mandatory fields to initialize a Controller, the user can set optional parameters related to covariates. This feature is illustrated below with the Theophylline example.
theophylline_path <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline_path, "Monolix") input_data_path <- file.path(theophylline_path, "data_pk.csv") ctr <- pmx_mlx( directory = work_dir, input = input_data_path, dv = "Y", cats = c("SEX"), conts = c("WT0", "AGE0"), strats = c("STUD", "SEX") )
Conts
are the continuous covariates. Cats
are categorical covariates used in the model, whereas strats
are categorical variables that can be used for plot stratification, but are not used as covariates in the model.
The covariates can be accessed using helper functions:
ctr %>% get_cats() ctr %>% get_conts() ctr %>% get_strats() ctr %>% get_covariates()
The content of the Controller can be seen by printing it:
ctr
It contains three tables:
The first table is the Controller configuration. The user can see the working directory, the input modeling dataset name, the dependent variable (DV) name and other fields related to the model (e.g., continuous and discrete covariates).
The second table lists the ggPMX
datasets. The first column (data_name
) of this table contains the ggPMX name of the dataset; the second column (data_file) contains the names of the output modeling datasets (for example estimates.txt); in the third column (data_label) contains the dataset description.
The third table provides the list of available plots in the Generator. It corresponds to Table \ref{tab:plots_list}.
The Controller is a container that stores all plots. To get the list of plots, the function plot_names()
is used:
ctr %>% plot_names()
An alternative way to display the names of the existing plots is by printing the content of the Controller as done above.
ggPMX
provides a specialized function to create and update each plot pmx_plot_xx()
where xx
is the plot name from the list above.
Each plot type is a class of similar plots. ggPMX
defines the following plot types:
The following syntax allows to see which type of plot corresponds to which plot name:
ctr %>% plots()
We create a helper function that outputs the xtable output.
# head table head_table <- function(out, label, caption, head=TRUE) { if (head) { hout <- head(out) } else { hout <- out } if (requireNamespace("xtable", quietly = TRUE)) { xt <- xtable::xtable(hout, label = label, caption = caption) print(xt, comment = F) } else { print(hout) } }
out <- rbind( c("input", "Input modeling dataset"), c("estimates", "Estimated population parameters"), c("eta", "Random effects, their standard deviation and residual errors (to calculate shrinkage)"), c("predictions", "Observations and predictions at times of observations dataset"), c("finegrid", "Additional predictions (at times without observations)") ) colnames(out) <- c("ggPMX dataset", "Description") head_table(out, label = "tab:ggPMX_datasets", caption = "ggPMX datasets")
theophylline <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline, "Monolix") input_data <- file.path(theophylline, "data_pk.csv") ctr <- theophylline()
The diagnostic plots of ggPMX are generated by calling the functions pmx_plot_xx()
where xx
is a placeholder for the plot name. The list of names of all available plots can be seen via:
ctr %>% plot_names()
As a convention, when plots are described as ???Y vs. X???, it is meant that Y is plotted on the vertical axis and X on the horizontal axis.
As an example, a plot of individual weighted residuals (IWRES) versus time (with default settings) can be generated using a single-line code:
ctr %>% pmx_plot_iwres_time
The complete list of available plots per plot type (given in parenthesis) is given below:
ctr %>% pmx_plot_dv_pred ctr %>% pmx_plot_dv_ipred ctr %>% pmx_plot_iwres_time ctr %>% pmx_plot_npde_time ctr %>% pmx_plot_iwres_ipred ctr %>% pmx_plot_abs_iwres_ipred ctr %>% pmx_plot_npde_pred
ctr %>% pmx_plot_eta_hist ctr %>% pmx_plot_eta_box
For individual plot which_pages parameter is used. which_pages parameter can either be:
ctr %>% pmx_plot_individual(which_pages=1)
ctr %>% pmx_plot_npde_qq ctr %>% pmx_plot_iwres_qq
ctr %>% pmx_plot_eta_matrix
In order to generate VPCs a simulation dataset is requried. Creation of VPC is slightly different dependening on the fitting software used (Monolix, NONMEM or nlmixr).
Monolix users, need to run a simulation with simulx. Here's an example code
## Create simulated object using simulx mysim <- simulx(project=project_dir, nrep=100) # ## Retrieve simulated dataset (assumed to be in y1) simdata <- mysim$LIDV
For use with ggPMX, it is required that the IDs are reverted to the original IDs as in the modelling dataset for ggPMX.
## Need to revert the original IDs as in modeling dataset for ggPMX ## Rename IDs column to same name as in modeling dataset, e.g. ## ???id??? in the example below simdata <- simdata %>% mutate(newId = as.numeric(as.character(id))) %>% left_join(., mysim$originalId) %>% mutate(id = as.numeric(as.character(oriId))) %>% select(-oriId, -newId) %>% data.table::data.table() ## It's highly recommended to store your simulation as .csv vpc_file <- write.csv(simdata, file = "my_VPC.csv", quote=FALSE, row.names = FALSE)
pmx_sim
creates a simulation object. It takes the following arguments:
Argumentss
Within pmx vpc controller, it is called like :
theoph_path <- file.path( system.file(package = "ggPMX"), "testdata", "theophylline" ) WORK_DIR <- file.path(theoph_path, "Monolix") input_file <- file.path(theoph_path, "data_pk.csv") vpc_file <- file.path(theoph_path, "sim.csv") ctr <- pmx_mlx( directory = WORK_DIR, input = input_file, dv = "Y", cats = c("SEX"), conts = c("WT0", "AGE0"), strats = "STUD", settings = pmx_settings( use.labels=TRUE, cats.labels=list( SEX=c("0"="Male","1"="Female") ) ), sim = pmx_sim( file = vpc_file, irun ="rep", idv="TIME" ) )
It is required to provide simulation tables to generate VPCs. Furthermore, it is highly recommended that simulation tables have a "sim"-suffix and are kept with the same naming convetion as for the prediction tables (e.g sdtab001sim, catab001sim)). In this case they're automatically recognized by the runnumber (runno) or by the model file if specified there. For post-hoc simulation it is possible to include an additional simfile:
ctr <- pmx_nm( directory = model_dir, file = "modelfile.ctl" #or .lst simfile = "simulation_modelfile.ctl" #or .lst )
Important: When simulations are performed post-hoc and the controller is generated by run number, the reader might load the wrong .ext file (used for parameters). A warning message is displayed.
For nlmixr users, the VPC is generated automatically with the controller creation and turned on by default vpc = TRUE
.
ctr <- pmx_nlmixr(fit) ## VPC will be generated automatically, vpc = TRUE ctr <- pmx_nlmixr(fit, vpc = FALSE ## But can be turned off )
The plot options are described in ?pmx_plot_vpc
function.
ctr %>% pmx_plot_vpc
By default the vpc plot is percentile ; , but we can plot the scatter type:
ctr %>% pmx_plot_vpc(type ="scatter")
ctr %>% pmx_plot_vpc(bin=pmx_vpc_bin(style = "kmeans",n=5))
ctr %>% pmx_plot_vpc(strat.facet=~SEX,facets=list(nrow=2))
User can customize the options to get a Monolix-like display.
ctr %>% pmx_plot_vpc( strat.facet=~SEX, facets=list(nrow=2), type="percentile", is.draft = FALSE, pi = pmx_vpc_pi(interval = c(0.1,0.9), median=list(color="green"), extreme= list(color="green")), obs = pmx_vpc_obs(color="blue",shape=18,size=2), ci = pmx_vpc_ci(interval = c(0.1,0.9), median=list(fill="red")) )
theophylline <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline, "Monolix") input_data <- file.path(theophylline, "data_pk.csv") ctr <- theophylline()
A report (in pdf and docx format) containing all default diagnostic plots can be created using the pmx_report function. The format can take three different values:
name.pdf
and name.png
specified in argument name, located in save_dir) with default diagnostic plotsggpmx_GOF
located in save_dir that contains all default diagnotic plots, each in a pdf and png file. The different plots are numerated in order to have an unique identifier for each plot (ex: ebe_box-1.pdf). This is necessary for having correct footnotes that indicated the path to the source file (for submission reports).Example:
ctr %>% pmx_report(name='Diagnostic_plots2', save_dir = work_dir, format='all')
Note that running the same command first with the option "format='plots'" and then with the option "format='report'" will remove the ggpmx_GOF folder.
Note also that by default, the report will have the DRAFT label on all plots. The label can be removed by using the settings argument in the Controller creation.
The user can customize the default report by creating a "template". To create a template, the user should create first a default report with the following command:
ctr %>% pmx_report(name='Diagnostic_plots1', save_dir = work_dir, output='report')
The Rmarkdown (.Rmd) file is the "template". The user can modify the Rmarkdown file as desired (ex: changing the size of some figures) and save the modified file. The new template can be used with the following command:
ctr %>% pmx_report(name='Diagnostic_plots3', save_dir = work_dir, output'='report', template=file.path(work_dir,'Diagnostic_plots1.Rmd'))
Any particular plot can be customized in two ways:
ctr %>% pmx_plot_xx(list of options)
ctr %>% pmx_update(???xx???, list of options)
Help(pmx_gpar) lists some options.
Help(pmx_plot_xx) lists some possible parameters to update a particular plot.
To obtain an exhaustive list of possible options for a particular plot, use the following:
ctr %>% get_plot_config("xx")
It is possible to visualize BLQ (below the limit of quantification) values in the individual plots.
For this, bloq
needs to be specified using the pmx_bloq function (see example below with the pmx_mlxtran()
function).
ctr %>% pmx_mlxtran(file_name = mlx_file, bloq=pmx_bloq(cens = ???BLQ???, limit = ???LIMIT???)) ctr %>% pmx_plot_individual()
Monolix users may want to plot simulated BLQs. This is possible for outputs with Monolix 2018 and later. An additional dataset is loaded (sim_blq), which will be used for plotting insted the regular "predictions"-dataset.
The sim_blq
statment can be specified within the plot (locally) ...
ctr %>% pmx_mlxtran(file_name = mlx_file)) ctr %>% pmx_plot_iwres_ipred(sim_blq = TRUE)
... or within the controller (globally). If this statment is used within the controller, all corresponding plots will plot simulated BLOQs by default.
ctr %>% pmx_mlxtran(file_name = mlx_file, sim_blq = TRUE)) ctr %>% pmx_plot_iwres_ipred()
pmx_settings()
The user can define a Controller with global settings that will be applied to all plots. For example remove draft annoataion, use abbreviation defintions to define axis labels, etc.
A settings object is defined by using the function pmx_settings()
. The created object is passed as the parameter "settings" to pmx()
. By doing so, the settings are defined globally and apply to all plots. For a complete list of global settings with their corresponding default values, please consult the ggPMX Help (?pmx_settings
).
## set one or more settings my_settings <- pmx_settings( is.draft = FALSE, use.abbrev = TRUE, ...) ### set other settings parameters here ctr <- pmx_mlx( ..., ## put here other pmx parametes settings = my_settings )
By default in the "standing" configuration, a DRAFT label is printed on all plots. In order to switch this label off, the user sets the is.draft
option of pmx_settings()
to FALSE
.
ctr <- theophylline(settings = pmx_settings(is.draft = FALSE))
The standing configuration initializes all plots using abbreviations for axis labels. Each abbreviation has its corresponding definition. To get the list of abbreviation :
ctr %>% get_abbrev
You can update one abbreviation to set a custom label
ctr %>% set_abbrev(TIME="TIME after the first dose")
Using use.abbrev
flag you can use abbreviation definition to set axis labels:
ctr <- theophylline(settings=pmx_settings(use.abbrev = TRUE)) ctr %>% set_abbrev(TIME="Custom TIME axis") ctr %>% pmx_plot_npde_time
finegrid.txt
file for individual plotswithin Monolix, user can choose to not use finegrid file even if it is present.
ctr <- theophylline() ctr %>% pmx_plot_individual(which_pages="all", use.finegrid =FALSE)
In case of color startfication user can customize the legend. For example here using the ggplot2::scale_color_manual
:
ctr <- theophylline() ctr %>% pmx_plot_npde_time(strat.color="STUD")+ ggplot2::scale_color_manual( "Study", labels=c("Study 1","Study 2"), values=c("1"="green","2"="blue"))
Another way to do it is to define a global scales.color
parameter that will applied to all plots with strat.color :
ctr <- theophylline( settings= pmx_settings( color.scales=list( "Study", labels=c("Study 1","Study 2"), values=c("1"="orange","2"="magenta")) ) ) ctr %>% pmx_plot_npde_time(strat.color="STUD")
ctr %>% pmx_plot_eta_box(strat.color="STUD")
In case of faceting by stratification user can redfine categorical labels to have more human readables strips. Lables are defined within cats.labels
argument and user can use them by setting use.lables
to TRUE.
ctr <- theophylline( settings= pmx_settings( cats.labels=list( SEX=c("0"="M","1"="F"), STUD=c("1"="Study 1","2"="Study 2") ), use.labels = TRUE ) ) ctr %>% pmx_plot_npde_time(strat.facet=~SEX)
ctr <- theophylline( settings= pmx_settings( cats.labels=list( SEX=c("0"="M","1"="F"), STUD=c("1"="Study 1","2"="Study 2") ), use.labels = TRUE ) ) ctr %>% pmx_plot_npde_time(strat.facet=~SEX)
ctr %>% pmx_plot_eta_box(strat.facet =~SEX)
pmx()
The function pmx()
is the generic function for greating a Controller, currently only works with Monolix. The user needs to specify a set of arguments such as the path to the model directory, the software used for model fitting (Monolix), the name of a configuration. A list of all existing configurations is provided in the Appendix. All mandatory arguments of pmx()
are listed in Table \ref{tab:pmx_mandatory}.
out <- rbind( c("sys", "Software used for model fittng (Monolix or nlmixr)", "mlx, mlx2018, nm"), c("config", "A pre-defined configuration is a set of default settings", "standing"), c("directory", "Path to the directory containing model output files", ""), c("input", "Path to input modeling dataset (dataset used for model fitting)", ""), c("dv", "Measurable variable name, as defined in the input modeling dataset", "DV, LIDV, LNDV, Y, etc."), c("dvid", "Endpoint (output) name, as defined in the input modeling dataset", "DVID, YTYPE, CMT, etc.") ) colnames(out) <- c("Argument", "Description", "Values") head_table(out, label = "tab:pmx_mandatory", caption = "Mandatory arguments of pmx() function")
The example below defines a Controller with the standing (standard) configuration.
theophylline_path <- file.path(system.file(package = "ggPMX"), "testdata", "theophylline") work_dir <- file.path(theophylline_path, "Monolix") input_data_path <- file.path(theophylline_path, "data_pk.csv") ctr <- pmx( sys = "mlx", directory = work_dir, input = input_data_path, dv = "Y", )
Note that the column "DVID" of data_pk.csv does not exist; however it is not needed here because there is only one single output of the model. As dvid is a mandatory argument, which is automatically set to "DVID" if dvid is missing.
The input dataset can be provided to ggPMX via its location (as in the example above) or as a data frame (maybe give an example). The modeling output datasets have to be in the location that is indicated as working directory (work_dir
in the example above).
ggPMX is compatible with Monolix versions 2016 and later, NONMEM version 7.2 and later, and nlmixr.
In order to be able to produce all available diagnostic plots, the following tasks should be executed in Monolix during the model fitting procedure:
In addition, make sure to export charts data (In Monolix 2018: Settings -> Preferences -> Export -> switch on the Export charts data button). Select at least the following plots to be displayed and saved: individual fits and scatter plot of the residuals.
NONMEM Version 7.2/7.3/7.4 Preferred output tables according ???sdtab, patab, cotab, catab??? convention Simulation are required for creation of VPC (e.g. sdtab1sim)
Fit objects need to be generated by nlmixr and have data attached.
Standard errors are required (a successful covariance plot) for full diagnostic checks. Can use boostrapFit()
to get standard error estimates if necessary
The main target of ggPMX is to create a report containing the following plots (see abbreviation list below):
out <- rbind( c("Scatter plot of NPDE vs population predictions", "SCATTER", "npde_pred"), c("Scatter plot of NPDE vs time", "SCATTER", "npde_time"), c("Scatter plot of IWRES vs time", "SCATTER", "iwres_time"), c("Scatter plot of observations vs population predictions", "SCATTER", "dv_pred"), c("Scatter plot of observations vs individual predictions", "SCATTER", "dv_ipred"), c("Scatter plot of absolute value of IWRES vs individual predictions", "SCATTER", "abs_iwres_ipred"), c("Scatter plot of IWRES vs individual predictions", "SCATTER", "iwres_ipred"), c("Plots of observations and model predictions per individual", "IND", "individual"), c("Histogram of EBE", "DIS", "ebe_hist"), c("Boxplot of EBE", "DIS", "ebe_box"), c("Distribution and quantile-quantile plot of IWRES", "QQ", "qq_iwres"), c("Distribution and correlation structure of RE (`ETA`)", "ETA_PAIRS", "eta_matrix"), c("Relationships between RE and categorical covariates", "ETA_COV", "eta_cats"), c("Relationships between RE and continuous covariates", "ETA_COV", "eta_conts"), c("Visual predictive check (VPC)", "VPC", "vpc") ) colnames(out) <- c("Plot Name", "ggPMX type", "ggPMX name") head_table(out, label = "tab:plots_list", caption = "List of all diagnostic plots")
Abbreviations:
ggPMX
implements few functions to generate and manipulate diagnostic plots.
(Should we list pmx and pmx_mlx separately and say the differences? Or it's maybe clear from the previous sections.)
out <- rbind( c("1", "pmx, or pmx_mlx", "Creates a controller"), c("2", "plot_names or plots", "Lists controller plots"), c("3", "get_data", "Lists controller data"), c("4", "get_plot", "Prints a plot"), c("5", "set_plot", "Creates a new plot"), c("6", "pmx_update", "Updates an existing plot"), c("7", "pmx_filter", "Filters globally the data of the current session"), c("8", "pmx_copy", "Returns a deep copy of the controller") ) colnames(out) <- c(" ", "Function name", "Description") head_table(out, label = "tab:func_list", caption = "List of all `ggPMX` functions", head=FALSE)
(Apparently, it's not the full list. Add all functions.)
The design of the package is around the central object: the controller. It can introspected or piped using the %>%
operand.
Note that:
The controller is an R6
object, it behaves like a reference object. Some functions (methods) can have a side effect on the controller and modify it internally. Technically speaking we talk about chaining not piping here. However, using pmx_copy
user can work on a copy of the controller.
Graphical parameters in ggPMX
are set internally using the pmx_gpar
function. A number of graphical parameters can be set for the different plot types.
args(pmx_gpar)
More information can be found in the help document ?pmx_gpar
and in the examples that follow.
For the moment we are mainly using standing configuration. In the next release user can specfiy configuration either by cretaing a custom yaml file or an R configuration object. Also ggPMX will create many helper functions to manipulate the configuration objects.
The shrinkage is a computation within controller data. In general it is used to annotate the plots. Although one can get it independently from any plot using pmx_comp_shrink
. It is part of the pmx_compt_xx
layer( In the future we will add , pmx_comp_cor
, pmx_comp_summary
,..)
Here the basic call:
ctr %>% pmx_comp_shrink
We get the shrinkage for each effect (SHRINK column).
The same values can be shown on distribution plot , for example :
ctr %>% pmx_plot_eta_box
or :
ctr %>% pmx_plot_eta_hist
You can add or remove shrinkage annotation using is.shrink
argument ( TRUE by default) :
ctr %>% pmx_plot_eta_box( is.shrink = FALSE)
You can compute shrinkage by applying either standard deviation sd
or variance var
:
ctr %>% pmx_comp_shrink(fun="var")
ctr %>% pmx_plot_eta_box(shrink=pmx_shrink(fun = "var"))
Shrinkage can be applied after stratification :
ctr %>% pmx_comp_shrink(strat.facet = ~SEX)
or by grouping like :
ctr %>% pmx_comp_shrink(strat.color = "SEX")
We can
ctr %>% pmx_plot_eta_hist(is.shrink = TRUE, strat.facet = ~SEX, facets=list(scales="free_y"))
or
ctr %>% pmx_plot_eta_box(is.shrink = TRUE, strat.facet = ~SEX, facets=list(scales="free_y",ncol=2))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.