R functions to run PKanalix

#

addAdditionalCovariate("firstDoseAmount")

addAdditionalCovariate(transformation = "observationNumberPerIndividual", headerName = "CONC")

## End(Not run)

#

----------------------------------------------------------------------------------------

LINE [ integer ]

applyFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows

----------------------------------------------------------------------------------------

ID [ character | integer ]

If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as character data.

applyFilter( filter = list(selectIds = "id==100") ) # select the subject called '100'

applyFilter( filter = list(list(removeIds = "id!='id_2'")) ) # select all the subjects excepted the one called 'id_2'

----------------------------------------------------------------------------------------

ID INDEX [integer]

applyFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one

----------------------------------------------------------------------------------------

OCC [ integer ]

applyFilter( filter = list(selectIds = "occ1==1", removeIds = "occ2!=3") ) # select the subjects whose first occasion level is '1' and whose second one is different from '3'

----------------------------------------------------------------------------------------

TIME [ double ]

applyFilter( filter = list(removeIds='TIME>120') ) # remove the subjects who have time over 120

applyFilter( filter = list(selectLines='TIME>120') ) # remove the all the lines where the time is over 120

----------------------------------------------------------------------------------------

OBSERVATION [ double ]

applyFilter( filter = list(selectLines = "CONC>=5.5", removeLines = "CONC>10")) # select the lines where CONC value superior or equal to 5.5 or strictly higher than 10

applyFilter( filter = list(removeIds = "CONC<0") ) # remove subjects who have negative CONC values

applyFilter( filter = list(removeIds = "E==0") ) # remove subjects for who E equals 0

----------------------------------------------------------------------------------------

OBSID [ character ]

applyFilter( filter = list(removeIds = "y1==1") ) # remove subject who have at least one observation for y1

applyFilter( filter = list(selectLines = "y1!=2") ) # select all lines corresponding to observations exepected those for y2

----------------------------------------------------------------------------------------

AMOUNT [ double ]

applyFilter( filter = list(selectIds = "AMOUT==10") ) # select subjects who have a dose equals to 10

----------------------------------------------------------------------------------------

INFUSION RATE AND INFUSION DURATION [ double ]

applyFilter( filter = list(selectIds = "RATE<10") ) # select subjects who have dose with a rate less than 10

----------------------------------------------------------------------------------------

COVARIATE [ character (categorical) | double (continuous) ]

applyFilter( filter = list(selectIds = "SEX==M", selectIds = "WEIGHT<80") ) # select subjects who are men and whose weight is lower than 80kg

----------------------------------------------------------------------------------------

REGERSSOR [ double ]

applyFilter( filter = list(selectLines = "REG>10") ) # select the lines where the regressor value is over 10

----------------------------------------------------------------------------------------

COMPLEMENT

applyFilter(origin = "data_filtered", filter = "complement" )

## End(Not run)

#

----------------------------------------------------------------------------------------

LINE [ integer ]

createFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows

----------------------------------------------------------------------------------------

ID [ character | integer ]

If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as character data.

createFilter( filter = list(selectIds = "id==100") ) # select the subject called '100'

createFilter( filter = list(list(removeIds = "id!='id_2'")) ) # select all the subjects excepted the one called 'id_2'

----------------------------------------------------------------------------------------

ID INDEX [integer]

createFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one

----------------------------------------------------------------------------------------

OCC [ integer ]

createFilter( filter = list(selectIds = "occ1==1", removeIds = "occ2!=3") ) # select the subjects whose first occasion level is '1' and whose second one is different from '3'

----------------------------------------------------------------------------------------

TIME [ double ]

createFilter( filter = list(removeIds='TIME>120') ) # remove the subjects who have time over 120

createFilter( filter = list(selectLines='TIME>120') ) # remove the all the lines where the time is over 120

----------------------------------------------------------------------------------------

OBSERVATION [ double ]

createFilter( filter = list(selectLines = "CONC>=5.5", removeLines = "CONC>10")) # select the lines where CONC value superior or equal to 5.5 or strictly higher than 10

createFilter( filter = list(removeIds = "CONC<0") ) # remove subjects who have negative CONC values

createFilter( filter = list(removeIds = "E==0") ) # remove subjects for who E equals 0

----------------------------------------------------------------------------------------

OBSID [ character ]

createFilter( filter = list(removeIds = "y1==1") ) # remove subject who have at least one observation for y1

createFilter( filter = list(selectLines = "y1!=2") ) # select all lines corresponding to observations exepected those for y2

----------------------------------------------------------------------------------------

AMOUNT [ double ]

createFilter( filter = list(selectIds = "AMOUT==10") ) # select subjects who have a dose equals to 10

----------------------------------------------------------------------------------------

INFUSION RATE AND INFUSION DURATION [ double ]

createFilter( filter = list(selectIds = "RATE<10") ) # select subjects who have dose with a rate less than 10

----------------------------------------------------------------------------------------

COVARIATE [ character (categorical) | double (continuous) ]

createFilter( filter = list(selectIds = "SEX==M", selectIds = "WEIGHT<80") ) # select subjects who are men and whose weight is lower than 80kg

----------------------------------------------------------------------------------------

REGERSSOR [ double ]

createFilter( filter = list(selectLines = "REG>10") ) # select the lines where the regressor value is over 10

----------------------------------------------------------------------------------------

COMPLEMENT

createFilter(origin = "data_filtered", filter = "complement" )

## End(Not run)

#

deleteAdditionalCovariate("firstDoseAmount")\cr

deleteAdditionalCovariate("observationNumberPerIndividual_y1")

## End(Not run)

#

deleteFilter(name = "filter2")

## End(Not run)

#

initializeLixoftConnectors(software = "pkanalix")

FormattedDataPath = tempfile("formatted_data", fileext = ".csv")

formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"),

           formattedFile = FormattedDataPath,

           headerLines = c(1,2),

           headers = c(id="ID", time="TIME"),

           observations = list(header="CONC",

                               censoring = list(type="interval", tags = c("BLQ"), 

                                                limits=list(0,"LLOQ"))),

           treatments = list(times=0, amount=100))

colnames(read.csv(FormattedDataPath)) # to check column names of the generated file and tag them as desired

newProject(data = list(dataFile = FormattedDataPath, headerTypes = c("id","time","observation","contcov","contcov","catcov","ignore","amount","cens","limit")))

plotObservedData()

# demo merge_occ_ParentMetabolite.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/parent_metabolite_data.csv"),

           formattedFile = FormattedDataPath,

           headers = c(id="ID", time="TIME"),

           observations = list(list(header="PARENT",

                                    censoring = list(type="interval", tags = c("BLQ"), limits=list(0,0.01))),

                               list(header="METABOLITE")),

           observationSettings = list(distinguishWithObsId = FALSE),

           treatments = list(times=0, amount="DOSE"))

# demo merge_obsID_ParentMetabolite.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/parent_metabolite_data.csv"),

           formattedFile = FormattedDataPath,

           headers = c(id="ID", time="TIME"),

           observations = list(list(header="PARENT",

                                    censoring = list(type="interval", tags = c("BLQ"), limits=list(0,0.01))),

                               list(header="METABOLITE")),

           treatments = list(times=0, amount="DOSE"))

# demo DoseAndLOQ_byCategory.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"),

           formattedFile = FormattedDataPath,

           headerLines = c(1,2),

           headers = c(id="ID", time="TIME"),

           observations = list(header="CONC",

                               censoring = list(type="interval", tags = c("BLQ"), 

                                                limits=list(0,list(category="STUDY",

                                                                   values=list("SD_400mg"=0.01, "SD_500mg"=0.1, "SD_600mg"=0.1))))),

           treatments = list(times=0, amount=list(category="STUDY",

                                                  values=list("SD_400mg"=400, "SD_500mg"=500, "SD_600mg"=600))))

# demo DoseAndLOQ_fromData.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"),

           formattedFile = FormattedDataPath,

           headerLines = c(1,2),

           headers = c(id="ID", time="TIME"),

           observations = list(header="CONC",

                               censoring = list(type="interval", tags = c("BLQ"), 

                                                limits=list(0,"LLOQ"))),

           treatments = list(times=0, amount="STUDY"))

# demo DoseAndLOQ_manual.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_multiple_BLQ_tags_data.csv"),

           formattedFile = FormattedDataPath,

           headerLines = c(1,2),

           headers = c(id="ID", time="TIME"),

           observations = list(header="CONC",

                               censoring = list(list(type="interval", tags = c("BLQ1"), limits=list(0,0.06)),

                                                list(type="interval", tags = c("BLQ2"), limits=list(0,0.1)))),

           treatments = list(times=0, amount=600))

# demo Urine_LOQinObs.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/urine_LOQinObs_data.csv"),

           formattedFile = FormattedDataPath,

           headers = c(id="ID", start="START TIME", end="END TIME", volume="VOLUME"),

           observations = list(header="CONC", 

                               censoring=list(type="LLOQ", tags="<LOQ=1>", limits="CONC")),

           treatments = list(paste0(getDemoPath(),"/0.data_formatting/data/urine_data_doses.csv")))

# demo CreateOcc_AdmIdbyCategory.pkx

formatData(paste0(getDemoPath(),"/0.data_formatting/data/two_formulations_data.csv"),

           formattedFile = FormattedDataPath,

           linesToExclude = 1, headerLines = c(2,3),

           headers = c(id="ID", time="TIME", sort="FORM"),

           observations = list(header="CONC",

                               censoring=list(type="LLOQ", tags="BLQ", limits=0.06)),

           treatments = list(times=0, amount=600, admId=list(category="FORM", values=list("ref"=1,"test"=2))))

# MONOLIX EXAMPLES

initializeLixoftConnectors(software = "monolix")

FormattedDataPath = tempfile("formatted_data")

# demo doseIntervals_as_Occ.mlxtran

formatData(paste0(getDemoPath(),"/0.data_formatting/data/data_multidose.csv"),

           formattedFile = FormattedDataPath,

           headers = c(id="ID", time="TIME"),

           observations = list(header="CONC"),

           treatments = list(times=seq(0,by=12,length=7), amount=40),

           treatmentSettings = list(doseIntervalsAsOccasions = TRUE))

# demo warfarin_PKPDseq_project.mlxtran

formatData(paste0(getDemoPath(),"/0.data_formatting/data/warfarin_data.csv"),

           formattedFile = FormattedDataPath,

           headers = c(id="id", time="time"),

           additionalColumns = paste0(getDemoPath(),"/0.data_formatting/data/warfarinPK_regressors.txt"))

#

getAvailableData()

## End(Not run)

#

info = getCovariateInformation() # Monolix mode with latent covariates

info

  -> $name

     c("sex","wt","lcat")

  -> $type

     c(sex = "categorical", wt = "continuous", lcat = "latent")

  -> $range

     list(wt = c(55, 73.5))

  -> $categories

     c(sex = c("F", "M"))

  -> $modalityNumber

     c(lcat = 2)

  -> $covariate

     id   sex    wt

      1    M   66.7

      .    .      .

      N    F   59.0

## End(Not run)

#

initializeLixoftConnectors(software = "pkanalix")

loadProject(paste0(getDemoPath(),"/0.data_formatting/DoseAndLOQ_manual.pkx"))

getFormatting()

}

#

info = getObservationInformation()

info

  -> $name

     c("concentration")

  -> $type # [Monolix]

     c(concentration = "continuous")

  -> $detailedType # [Monolix]

     c(concentration = "continuous")

  -> $mapping # [Monolix]

     c(concentration = "CONC")

  -> $concentration

       id   time concentration

        1    0.5     0.0

        .    .      .

        N    9.0    10.8

## End(Not run)

#

## Not run: 

initializeLixoftConnectors("monolix")

project_name <- file.path(getDemoPath(), "6.PK_models", "6.3.multiple_doses", "ss1_project.mlxtran")

loadProject(project_name)

getTreatmentsInformation()

## End(Not run)

}

#

removeFilter()

## End(Not run)

#

renameAdditionalCovariate(oldName = "observationNumberPerIndividual_y1", newName = "nbObsForY1")

## End(Not run)

#

renameFilter("newFilter")\cr

renameFilter(oldName = "filter", newName = "newFilter")  

## End(Not run)

#

selectData(name = "filter1")

## End(Not run)

#

data = getCAData()

       ID time concentration censored 

  1     1  0.0          0.00   FALSE

  2     1  0.5          3.05   FALSE

  3     1  2.0          5.92   TRUE

## End(Not run)

#

data = getNCAData()

       ID time concentration censored 

  1     1  0.0          0.00   FALSE

  2     1  0.5          3.05   FALSE

  3     1  2.0          5.92   TRUE

## End(Not run)

#

initializeLixoftConnectors(software = "monolix", path = "/path/to/lixoftRuntime/")

## End(Not run)

#

  getDemoPath()

## End(Not run)

#

  project <- file.path(getDemoPath(), "1.creating_and_using_models",

                       "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran")

  loadProject(project)

  plotBivariateDataViewer(obs1 = "y1", obs2 = "y2")

  plotBivariateDataViewer(settings = list(lines = FALSE))

  # stratification

  plotBivariateDataViewer(obs1 = "y1", obs2 = "y2", stratify = list(individualSelection = list(indices = 10)))

  plotBivariateDataViewer(stratify = list(split = "age",

                                          filter = list("sex", "1"),

                                          groups = list(name = "age", definition = c(25))))

  plotBivariateDataViewer(stratify = list(color = "wt",

                                          groups = list(name = "wt", definition = 75)))

  plotBivariateDataViewer(stratify = list(split = c("age", "sex"),

                                          groups = list(name = "age", definition = 25)))

  # update plot settings or preferences

  plotBivariateDataViewer(preferences = list(obs = list(color = "#32CD32")))

#

  project <- file.path(getDemoPath(), "2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  # covariate distribution when only one covariate is specified

  plotCovariates(covariatesRows = "HT", settings = list(bins = 10))

  # scatter plot when both covariates are continuous

  plotCovariates(covariatesRows = "HT", covariatesColumns = "AGE", settings = list(spline = TRUE))

  plotCovariates(covariatesRows = "HT", covariatesColumns = c("AGE", "FORM"))

  # box plot when one covariate is categorical and the othe one is continuous

  preferences <- list(boxplot = list(fill = "#2075AE"), boxplotOutlier = list(shape = 3))

  plotCovariates(covariatesRows = "FORM", covariatesColumns = "AGE", preferences = preferences)

  # histogram when covariate on column is categorical

  plotCovariates(covariatesRows = "FORM", covariatesColumns = "SEQ",

                 settings = list(histogramColors = c("#5DC088", "#DBA92B")))

  plotCovariates(covariatesRows = "AGE", covariatesColumns = "SEQ",

                 settings = list(histogramColors = c("#5DC088", "#DBA92B")))

  # stratification

  plotCovariates(covariatesRows = "HT", covariatesColumns = "WT",

                 stratify = list(split = "AGE",

                                 filter = list("Period", 1),

                                 groups = list(name = "AGE", definition = 25)))

  preferences <- list(regressionLine = list(color = "#E5551B"))

  plotCovariates(covariatesRows = "AGE", covariatesColumns = "WT",

                 stratify = list(color = "HT",

                                 groups = list(name = "HT", definition = 181),

                                 colors = c("#2BB9DB", "#DD6BD2")),

                 preferences = preferences)

  plotCovariates(covariatesRows = "HT", covariatesColumns = "WT",

                 stratify = list(split = c("AGE", "SEQ"),

                                 groups = list(name = "AGE", definition = 25)))

  # Mulitple covariates

  plotCovariates()

  plotCovariates(covariatesRows = c("AGE", "SEQ", "HT"), covariatesColumns = c("AGE", "SEQ", "HT"))

  plotCovariates(stratify = list(filter = list("AGE", 2),

                                 groups = list(name = "AGE", definition = c(25, 30))))

#

  project <- paste0(getDemoPath(), "/2.case_studies/project_aPCSK9_SAD.pkx")

  loadProject(project)

  # by default, individual profiles and mean curve are displayed

  plotObservedData()

  # displaying dots and mean curve by dose group, merged on a single plot

  plotObservedData(settings=list(lines=F,

                            mean=T,

                            meanMethod="geometric",

                            ylog=T,

                            ylab="mAb concentration (ug/mL)"),

              stratify=list(split="DOSE_mg",

                            mergedSplits=T),

              preferences=list(observationStatistics=list(lineWidth=0.8),

                               obs=list(radius=2)))

  # coloring by ID, without mean curve

  plotObservedData(settings=list(mean=F,error=F),

              stratify = list(color=c("id")))

  # changing the settings to display only the mean curve with SE, with bin limits and dosing times

  plotObservedData(settings=list(dots=F,

                            lines=F,

                            mean=T,

                            error=T,

                            meanMethod="geometric",

                            errorMethod="standardError",

                            useCensored=T,

                            binLimits=T,

                            binsSettings=list(criteria="leastsquare", is.fixedNbBins=T, nbBins=20),

                            cens=F,

                            dosingTimes=T,

                            legend=T,

                            grid=F,

                            xlog=F,ylog=T,

                            xlab="Time since first dose (days)",

                            ylab="mAb concentration (ug/mL)",

                            xlim=c(0,96),

                            ylim=c(0.1,70),

                            fontsize=12,

                            units=F))

  # changing preferences for observations, censored observations and bin limits

  plotObservedData(settings=list(dots=T, lines=T, legend=T, dosingTimes=T, mean=F, error=F, ylog=T, cens=T),

              preferences=list(obs=list(color="#161617",

                                        radius=2,

                                        shape=18,

                                        lineWidth=0.2,

                                        lineType="dashed",

                                        legend="Observations"),

                               censObs=list(color="#cdced1",

                                            radius=2,

                                            shape=16,

                                            legend="Censored observations"),

                               dosingTimes=list(color="#fcba03",

                                                lineWidth=0.5,

                                                lineType="solid",

                                                legend="Time of doses")))

  # changing preferences for mean and bin limits

  plotObservedData(settings=list(dots=F, lines=F, legend=T, binLimits=T, grid=F),

              preferences=list(observationStatistics=list(color="#161617",

                                                          whiskersWidth=3,

                                                          lineWidth=0.7,

                                                          lineType="solid",

                                                          legend="mean and standard deviation over bins"),

                               binsValues=list(color="#cdced1",

                                               lineWidth=0.5,

                                               lineType="dashed",

                                               legend="bins")))

  # color and split by DOSE_mg but grouping two doses levels together

  plotObservedData(settings=list(mean=F,error=F,ylim=c(0,120)),

              stratify = list(groups=list(name="DOSE_mg",definition=list(c("150mg"), c("300mg","800mg"))),

                              color="DOSE_mg",

                              split="DOSE_mg"))

  # selecting only one individual

  plotObservedData(settings=list(mean=F,error=F),

                   stratify = list(individualSelection=list(indices=1)))

  plotObservedData(settings=list(mean=F,error=F),

                   stratify = list(individualSelection=list(ids="1")))

  #============= projects with several covariates to stratify

initializeLixoftConnectors(software = "pkanalix", force=T)

project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

loadProject(project)

# defining groups for AGE and HT, coloring by HT and filtering by AGE

plotObservedData(settings=list(mean=F,error=F),

            stratify = list(groups=list(list(name="AGE", definition=c(24, 34)),

                                        list(name="HT", definition=c(184.5))),

                            color="HT",

                            colors=c("#cdced1","#161617"),

                            filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotObservedData(settings=list(mean=F,error=F),

            stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

  #============= project with time-to-event data

initializeLixoftConnectors(software = "monolix", force=T)

project <- file.path(getDemoPath(), "/4.joint_models/4.2.continuous_noncontinuous/PKrtte_project.mlxtran")

loadProject(project)

# survival Kaplan-Meier curve

plotObservedData(obsName="Hemorrhaging")

# mean number of events

plotObservedData(obsName="Hemorrhaging",

                 settings=list(eventPlot="averageEventNumber"))

#============= project with categorical data

initializeLixoftConnectors(software = "monolix", force=T)

project <- file.path(getDemoPath(), "/4.joint_models/4.2.continuous_noncontinuous/warfarin_cat_project.mlxtran")

loadProject(project)

# display is the same as for continuous data by default

plotObservedData(obsName="Level")

# display as stacked or as grouped

plotObservedData(obsName="Level", settings=list(plot="stacked"))

plotObservedData(obsName="Level", settings=list(plot="grouped"))

# splitting by sex

plotObservedData(obsName="Level", 

                 settings=list(plot="stacked", ylim=c(0,30), legend=T),

                 stratify=list(split="sex"))

#============== project with multiple-dose to show timeAfterLastDose

initializeLixoftConnectors(software = "monolix", force=T)

project <- file.path(getDemoPath(), "/6.PK_models/6.3.multiple_doses/addl_project.mlxtran")

loadProject(project)

# with "time after first dose" by default

plotObservedData()

# with "time since previous dose"

plotObservedData(settings=list(timeAfterLastDose=T))

#============= project with regressor to show regressor on x-axis

initializeLixoftConnectors(software = "monolix", force=T)

project <- file.path(getDemoPath(), "/7.miscellaneous/7.1.regression_variables/reg3_warfarinPD_linearInterp_project.mlxtran")

loadProject(project)

# with "time" on x-axis by default

plotObservedData()

# with regressor "Cc" (concentration) on x-axis

plotObservedData(settings=list(xvariable="Cc"))

#============= project with nominal time on x-axis

initializeLixoftConnectors(software = "pkanalix", force=T)

project <- file.path(getDemoPath(), "/1.basic_examples/project_nominal_time.pkx")

loadProject(project)

# with "actual time" by default

plotObservedData()

# with nominal time on x-axis

plotObservedData(settings=list(xvariable="nominalTime"))

#

  project <- file.path(getDemoPath(), "2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runNCAEstimation()

  runBioequivalenceEstimation()

  plotBEConfidenceIntervals()

  plotBEConfidenceIntervals(parameters = "Cmax",

                            settings = list(legend = T))

#

project <- file.path(getDemoPath(), "/3.bioequivalence/project_crossover_bioequivalence.pkx")

loadProject(project)

runNCAEstimation()

runBioequivalenceEstimation()

# display by default

plotBESequenceByPeriod()

# select only a subplot of parameters used for BE analysis

plotBESequenceByPeriod(parameters = c("AUCINF_obs","Cmax"))

# changing the settings

plotBESequenceByPeriod(parameters = c("AUCINF_obs","Cmax"),

                       settings=list(dots=T,

                                     lines=T,

                                     error=F,

                                     legend=T,

                                     grid=F,

                                     ylog=T,

                                     ncol=1,

                                     units=F,

                                     fontsize=9,

                                     xlab="Sequence of periods"))

# changing the preferences

plotBESequenceByPeriod(parameters = c("AUCINF_obs","Cmax"),

                       settings=list(legend=T),

                       preferences = list(formulationDot=list(radius=4,

                                                              shape=18,

                                                              legend="Formulations tested"),

                                          observationStatistics=list(color="#202120",

                                                                     whiskersWidth=1,

                                                                     lineWidth=1,

                                                                     lineType="solid",

                                                                     legend="Standard deviation"),

                                          sequence=list(color="#202120",

                                                        lineWidth=1,

                                                        lineType="dashed",

                                                        legend="Sequences")))

# define groups of WT and split by WT

plotBESequenceByPeriod(parameters = c("AUCINF_obs","Cmax"),

                       stratify=list(groups=list(name="WT", definition=c(70)),

                                     split=c("WT")))

# define groups of AGE and WT and filter by AGE and WT

plotBESequenceByPeriod(parameters = c("AUCINF_obs","Cmax"),

                       stratify=list(groups=list(list(name="WT", definition=c(70)),

                                                 list(name="AGE", definition=c(24,34))),

                                     filter=list(list("AGE",c(1,3)),

                                                 list("WT",2))))

#

project <- file.path(getDemoPath(), "/3.bioequivalence/project_crossover_bioequivalence.pkx")

loadProject(project)

runNCAEstimation()

runBioequivalenceEstimation()

# display by default

plotBESubjectByFormulation()

# select only a subplot of parameters used for BE analysis

# if several test formulations are compared w.r.t a reference formulation, it is also possible to select which test formulation to display

plotBESubjectByFormulation(parameters = c("AUCINF_obs","Cmax"),

                           formulations = c("test"))

# changing the settings

plotBESubjectByFormulation(parameters = c("AUCINF_obs","Cmax"),

                           settings=list(dots=T,

                                         lines=T,

                                         legend=T,

                                         grid=F,

                                         ylog=T,

                                         units=F,

                                         fontsize=9))

# changing the preferences

plotBESubjectByFormulation(parameters = c("AUCINF_obs","Cmax"),

                           settings=list(legend=T),

                           preferences = list(formulationDot=list(color="#202120",

                                                                  radius=4,

                                                                  shape=18,

                                                                  legend="NCA parameter for each formulation"),

                                              formulationLine=list(color="#202120",

                                                                   lineWidth=0.5,

                                                                   lineType="dashed",

                                                                   legend="Individuals")))

# define groups of WT and split by WT

plotBESubjectByFormulation(parameters = c("AUCINF_obs","Cmax"),

                           stratify=list(groups=list(name="WT", definition=c(70)),

                                         split=c("WT")))

# define groups of AGE and WT and filter by AGE and WT

plotBESubjectByFormulation(parameters = c("AUCINF_obs","Cmax"),

                           stratify=list(groups=list(list(name="WT", definition=c(70)),

                                                     list(name="AGE", definition=c(24,34))),

                                         filter=list(list("AGE",c(1,3)),

                                                     list("WT",2))))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runCAEstimation()

# by default 12 individuals are displayed (with 2 occasions each in this example)

plotCAIndividualFits()

# loop to create several plots ("pages") with 6 individuals (2 occasions each) per page, using "indices" and "isRange"

nbIndiv <- 18

nbIndivPerPage <- 6

nbPages <- ceiling(nbIndiv/nbIndivPerPage)

for(i in 1:nbPages){

  plotCAIndividualFits(stratify = list(individualSelection=list(indices=c(nbIndivPerPage*(i-1)+1,nbIndivPerPage*i),

                                                                isRange=T)))

}

# selection of individuals based on id names

plotCAIndividualFits(stratify = list(individualSelection=list(ids=c("6","17"))))

plotCAIndividualFits(stratify = list(individualSelection=list(ids=c(6, 17))))

# changing the elements to display

plotCAIndividualFits(settings = list(obsDots=T, 

                                     obsLines=T, 

                                     cens=T,

                                     dosingTimes=T,

                                     splitOccasions=T, 

                                     ncol=5, 

                                     legend=T, 

                                     grid=F, 

                                     ylog=T,

                                     units=F, 

                                     fontsize=9,

                                     ylim=c(0.05,15),

                                     ylab="Theophylline concentration (ug/mL)"))

# get available preferences

getPlotPreferences("plotCAIndividualFits")

# change color, radius, shape, lineType, lineWidth and legend for all elements

plotCAIndividualFits(settings = list(legend=T, obsLines=T, dosingTimes=T),

                     stratify = list(individualSelection=list(ids=c(1,2,3,4))),

                     preferences = list(obs=list(color="#161617",

                                                 radius=2,

                                                 shape=18,

                                                 lineWidth=0.5,

                                                 lineType="dashed",

                                                 legend="Measured concentrations"),

                                        censObsIntervals=list(color="#cdced1",

                                                              opacity=1,

                                                              lineType="solid",

                                                              lineWidth=1,

                                                              legend="BLQ observations"),

                                        dosingTimes=list(color="#ff793f",

                                                         lineWidth=1,

                                                         lineType="solid",

                                                         legend="Time of doses"),

                                        indivFits=list(color="#00a4c6",

                                                       lineWidth=1,

                                                       lineType="solid",

                                                       legend="Individual predictions")))

# define groups for AGE and color by AGE and FORM

plotCAIndividualFits(settings=list(legend=T),

                     stratify = list(groups=list(name="AGE", definition=c(24, 34)),

                                     color=c("AGE","FORM"),

                                     individualSelection=list(ids=c(1,2,3,4))))

# filter to keep only individuals with sequence "RT" (first among "RT" and "TR")

plotCAIndividualFits(stratify = list(filter=list(list("SEQ","RT"))))

plotCAIndividualFits(stratify = list(filter=list(list("SEQ",1))))

#

loadProject(project)

setCASettings(blqMethod="LOQ") #not recommended but allows to show plot settings related to censored data

runCAEstimation()

# plot with default options

plotCAObservationsVsPredictions()

# changing the elements to display

plotCAObservationsVsPredictions(settings=list(spline=T,

                                              useCensored=T,

                                              cens=T,

                                              identityLine=F,

                                              legend=T,

                                              grid=F,

                                              xlog=T,

                                              ylog=T,

                                              xlim=c(0.01,20),

                                              ylim=c(0.01,20),

                                              fontsize=9,

                                              xlab=c('Individual CA predictions'),

                                              ylab=c('Theophylline measurements (obs)')))

# coloring by FORM and Period using a greyscale

plotCAObservationsVsPredictions(settings = list(legend=T),

                                stratify = list(color=c("FORM", "Period"),

                                                colors=c("#cdced1","#97989c","#4c4d4f","#161617")))

# coloring by FORM and filtering to keep only SEQ=TR

plotCAObservationsVsPredictions(settings = list(legend=T),

                                stratify = list(color=c("FORM"),

                                                filter=list("SEQ","TR")))

# defining groups for AGE and HT, coloring by AGE and splitting by HT

plotCAObservationsVsPredictions(settings = list(legend=T, cens=F),

                                stratify = list(groups=list(list(name="AGE", definition=c(24, 34)), 

                                                            list(name="HT", definition=180)),

                                                color="AGE",

                                                colors=c("#00a4c6","#64bc48","#ff793f"),

                                                split="HT"))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runCAEstimation()

# by default only the 4 first CA parameters are shown

plotCAParametersCorrelation()

# choosing to display only a subset of possible covariates and CA parameters (same param on rows and columns)

plotCAParametersCorrelation(parametersRows = c("Cl","V"))

# selecting different parameters on rows and columns

plotCAParametersCorrelation(parametersRows = c("Cl","V"),

                            parametersColumns = c("Tlag","ka"))

# changing the settings to choose which elements to display

plotCAParametersCorrelation(settings = list(regressionLine=F,

                                            spline=T,

                                            legend=T,

                                            grid=F,

                                            units=F,

                                            fontsize=8))

# changing the preferences (appearance of the elements)

plotCAParametersCorrelation(parametersRows = c("Cl","V"),

                            settings=list(legend=T, spline=T),

                            preferences = list(obs=list(color="#202120",

                                                        radius=2,

                                                        shape=18,

                                                        legend="CA parameter values"),

                                               regressionLine=list(color="#202120",

                                                                   lineWidth=1,

                                                                   lineType="dashed",

                                                                   legend="Linear regression line"),

                                               spline=list(color="#97989c",

                                                           lineWidth=1,

                                                           lineType="solid",

                                                           legend="Spline regression line")))

# split each plot into two subplot for FORM=ref and FORM=test, vertically (ncol=1)

plotCAParametersCorrelation(parametersRows = c("ka","Cl","V"),

                            settings=list(ncol=1),

                            stratify = list(split=c("FORM")))

# define groups for AGE and HT, color by HT and filter by AGE

plotCAParametersCorrelation(settings=list(legend=T),

                            parametersRows = c("Cl","V"),

                            stratify = list(groups=list(list(name="AGE", definition=c(30, 35)),

                                                        list(name="HT", definition=c(184.5))),

                                            color="HT",

                                            colors=c("#cdced1","#161617"),

                                            filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotCAParametersCorrelation(settings=list(legend=T),

                            parametersRows = c("Cl","V"),

                            stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runCAEstimation()

# plot a single parameter, as pdf or cdf

plotCAParametersDistribution(parameters = "V", settings = list(plot = "pdf"))

plotCAParametersDistribution(parameters = "Cl", settings = list(plot = "cdf"))

# with a single parameter, a ggplot object if returned and additional ggplot elements can be added

library(ggplot2)

plotCAParametersDistribution(parameters = "V")+geom_vline(xintercept=52)

# display 4 parameters in a 2x2 matrix (ncol=2)

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings = list(ncol=2))

# changing the settings to choose which elements to display

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings = list(ncol=2,

                                             units=F,

                                             legend=T,

                                             grid=F,

                                             fontsize=9))

# changing the preferences (appearance of the elements) when using plot="cdf"

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings = list(ncol=2, legend=T, plot="cdf"),

                             preferences = list(empirical=list(color="#161617",

                                                               lineWidth=1,

                                                               lineType="solid",

                                                               legend="cumulative density distribution")))

# changing the preferences (appearance of the elements) when using plot="pdf"

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings = list(ncol=2, legend=T, plot="pdf"),

                             preferences = list(histogramBar=list(fill="#cdced1",

                                                                  opacity=0.7,

                                                                  stroke="#161617",

                                                                  strokeWidth=1,

                                                                  legend="probability density distribution")))

# split each plot into two subplots for FORM=ref and FORM=test

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings = list(ncol=2),

                             stratify = list(split=c("FORM")))

# define groups for AGE and HT, filter by AGE and split by HT

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings=list(ncol=2),

                             stratify = list(groups=list(list(name="AGE", definition=c(30, 35)),

                                                         list(name="HT", definition=c(184.5))),

                                             filter=list("AGE",c(1,3)),

                                             split="HT"))

# filter to keep only second sequence (TR) and Period=1

plotCAParametersDistribution(parameters = c('V','Cl','ka','Tlag'),

                             settings=list(ncol=2),

                             stratify = list(filter=list(list("SEQ",2),list("Period","1"))))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runCAEstimation()

# choosing to display only a subset of possible covariates and CA parameters

plotCAParametersVsCovariates(parameters = c("Cl","V"),

                             covariates = c("WT","FORM"))

# changing the settings to choose which elements to display

plotCAParametersVsCovariates(settings = list(regressionLine=F,

                                             spline=T,

                                             boxplotData="spread",

                                             legend=T,

                                             grid=F,

                                             units=F,

                                             fontsize=8))

# changing the preferences (appearance of the elements)

plotCAParametersVsCovariates(parameters = c("Cl","V"),

                             covariates = c("WT","FORM"),

                             settings=list(legend=T, spline=T, boxplotData="spread"),

                             preferences = list(boxplot=list(fill="#FBFBFB",

                                                             opacity=0.5,

                                                             legend=""),

                                                boxplotOutlier=list(color="#D71313",

                                                                    size=1.5,

                                                                    shape=2),

                                                boxplotValues=list(color="#1642f2",

                                                                   radius=1.5,

                                                                   shape=17,

                                                                   opacity=0.3,

                                                                   legend="NCA parameter values"),

                                                obs=list(color="#1642f2",

                                                         radius=2,

                                                         shape=17,

                                                         legend="NCA parameter values"),

                                                regressionLine=list(color="#202120",

                                                                    lineWidth=1,

                                                                    lineType="dashed",

                                                                    legend="Linear regression line"),

                                                spline=list(color="#97989c",

                                                            lineWidth=1,

                                                            lineType="solid",

                                                            legend="Spline regression line")))

# split each plot into two subplot for FORM=ref and FORM=test, vertically (ncol=1)

plotCAParametersVsCovariates(parameters = c("Cl","V"),

                             covariates = c("WT","AGE"),

                             settings=list(ncol=1),

                             stratify = list(split=c("FORM")))

# define groups for AGE and HT, color by HT and filter by AGE

plotCAParametersVsCovariates(settings=list(legend=T),

                             parameters = c("Cl","V"),

                             covariates = c("WT","FORM"),

                             stratify = list(groups=list(list(name="AGE", definition=c(30, 35)),

                                                         list(name="HT", definition=c(184.5))),

                                             color="HT",

                                             colors=c("#cdced1","#161617"),

                                             filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotCAParametersVsCovariates(settings=list(legend=T),

                             parameters = c("Cl","V"),

                             covariates = c("WT","FORM"),

                             stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

#

  project <- paste0(getDemoPath(), "/2.case_studies/project_aPCSK9_SAD.pkx")

  loadProject(project)

  setNCASettings(blqMethodBeforeTmax="missing",blqMethodAfterTmax="LOQ")

  runNCAEstimation()

  # by default, individual profiles and mean curve are displayed

  plotNCAData()

  # displaying dots and mean curve by dose group, merged on a single plot

  plotNCAData(settings=list(lines=F,

                            mean=T,

                            meanMethod="geometric", 

                            ylog=T),

              stratify=list(split="DOSE_mg",

                            mergedSplits=T),

              preferences=list(observationStatistics=list(lineWidth=0.8),

                               obs=list(radius=2)))

  # coloring by ID, without mean curve

  plotNCAData(settings=list(mean=F,error=F),

              stratify = list(color=c("id")))

  # changing the settings to display only the mean curve with SE, with bin limits and dosing times

  plotNCAData(settings=list(dots=F,

                            lines=F,

                            mean=T,

                            error=T,

                            meanMethod="geometric",

                            errorMethod="standardError",

                            useCensored=T,

                            binLimits=T,

                            binsSettings=list(criteria="leastsquare", is.fixedNbBins=T, nbBins=20),

                            cens=F,

                            dosingTimes=T,

                            legend=T,

                            grid=F,

                            xlog=F,ylog=T,

                            xlab="Time since first dose (days)",

                            ylab="mAb concentration (ug/mL)",

                            xlim=c(0,96),

                            ylim=c(0.1,70),

                            fontsize=12,

                            units=F))

  # changing preferences for observations, censored observations and bin limits

  plotNCAData(settings=list(dots=T, lines=T, legend=T, dosingTimes=T, mean=F, error=F, ylog=T, cens=T),

              preferences=list(obs=list(color="#161617",

                                        radius=2,

                                        shape=18,

                                        lineWidth=0.2,

                                        lineType="dashed",

                                        legend="Observations"),

                               censObs=list(color="#cdced1",

                                            radius=2,

                                            shape=16,

                                            legend="Censored observations"),

                               dosingTimes=list(color="#fcba03",

                                                lineWidth=0.5,

                                                lineType="solid",

                                                legend="Time of doses")))

  # changing preferences for mean and bin limits

  plotNCAData(settings=list(dots=F, lines=F, legend=T, binLimits=T, grid=F),

              preferences=list(observationStatistics=list(color="#161617",

                                                          whiskersWidth=3,

                                                          lineWidth=0.7,

                                                          lineType="solid",

                                                          legend="mean and standard deviation over bins"),

                               binsValues=list(color="#cdced1",

                                               lineWidth=0.5,

                                               lineType="dashed",

                                               legend="bins")))

  # color and split by DOSE_mg but grouping two doses levels together

  plotNCAData(settings=list(mean=F,error=F,ylim=c(0,120)),

              stratify = list(groups=list(name="DOSE_mg",definition=list(c("150mg"), c("300mg","800mg"))),

                              color="DOSE_mg",

                              split="DOSE_mg"))

  #============= projects with several covariates

project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

loadProject(project)

runNCAEstimation()

# defining groups for AGE and HT, coloring by HT and filtering by AGE

plotNCAData(settings=list(mean=F,error=F),

            stratify = list(groups=list(list(name="AGE", definition=c(24, 34)),

                                        list(name="HT", definition=c(184.5))),

                            color="HT",

                            colors=c("#cdced1","#161617"),

                            filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotNCAData(settings=list(mean=F,error=F),

            stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

  #============= project with sparse calculations

  project <- paste0(getDemoPath(), "/1.basic_examples/project_sparse.pkx")

  loadProject(project)

  runNCAEstimation()

  # mean profiles are already calculated for each stratification group used for the sparse calculations

  # stratify argument cannot be used

  plotNCAData()

  # displaying only the averaged profiles, without individual dots and without error bars

  plotNCAData(settings=list(dots=F,

                            error=F))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runNCAEstimation()

  # by default 12 individuals are displayed (with 2 occasions each in this example) 

  plotNCAIndividualFits()

  # loop to create several plots ("pages") with 6 individuals (2 occasions each) per page, using "indices" and "isRange"

  nbIndiv <- 18

  nbIndivPerPage <- 6

  nbPages <- ceiling(nbIndiv/nbIndivPerPage)

  for(i in 1:nbPages){    

    plotNCAIndividualFits(stratify = list(individualSelection=list(indices=c(nbIndivPerPage*(i-1)+1,nbIndivPerPage*i),

                                                                   isRange=T)))

  }

  # selection of individuals based on id names

  plotNCAIndividualFits(stratify = list(individualSelection=list(ids=c("6","17"))))

  plotNCAIndividualFits(stratify = list(individualSelection=list(ids=c(6, 17))))

# plot only the observations (lambda_z=F) with dots, lines and dosing times. The two occasions are overlayed (splitOccasions=F)

# all observations are displayed in the same way without distinguishing those use for lambda_z (obsUnusedColor="colored")

# coloring by formulation with user-defined colors

plotNCAIndividualFits(settings = list(lambda_z=F, obsDots=T, obsLines=T, dosingTimes=T,

                                      splitOccasions=F, ncol=3, obsUnusedColor="colored",

                                      legend=T, grid=F, ylog=T,

                                      units=F, ylab="Theophylline concentration (ug/mL)"),

                      stratify = list(color=c("FORM"),

                                      colors=c("#00a4c6","#ff793f")))

  # get available preferences

  getPlotPreferences("plotNCAIndividualFits")    

  # change color, radius, shape, lineType, lineWidth and legend for all elements

plotNCAIndividualFits(settings = list(legend=T),

                      preferences = list(obs=list(color="#161617",

                                                  radius=2,

                                                  shape=18,

                                                  legend="Points included in lambda_z"),

                                         censObs=list(color="#161617",

                                                      radius=2,

                                                      shape=5,

                                                      legend="BLQ points"),

                                         obsUnused=list(color="#cdced1",

                                                        radius=2,

                                                        shape=18,

                                                        legend="Points NOT included in lambda_z"),

                                         lambda_z=list(color="#1642f2",

                                                       lineWidth=1,

                                                       lineType="solid",

                                                       legend="Lambda_z regression")))                           

  #==== stratification (not available for sparse data)

  # define groups for AGE and color by AGE and FORM

plotNCAIndividualFits(settings = list(obsDots=T,legend=T,obsUnusedColor="colored", obsLines=T),

                      stratify = list(groups=list(name="AGE", definition=c(30, 35)), 

                                      color=c("AGE","FORM")))

# filter to keep only individuals with sequence "RT" (first among "RT" and "TR")

plotNCAIndividualFits(stratify = list(filter=list(list("SEQ","RT"))))

plotNCAIndividualFits(stratify = list(filter=list(list("SEQ",1))))                                                  

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runNCAEstimation()

# by default only the 4 first NCA parameters are shown

plotNCAParametersCorrelation()

# choosing to display only a subset of possible covariates and NCA parameters (same param on rows and columns)

plotNCAParametersCorrelation(parametersRows = c("Cmax","Lambda_z","AUCINF_obs"))

# selecting different parameters on rows and columns

plotNCAParametersCorrelation(parametersRows = c("AUCINF_obs","Cmax"),

                             parametersColumns = c("Lambda_z","Tmax"))

# changing the settings to choose which elements to display

plotNCAParametersCorrelation(settings = list(regressionLine=F,

                                             spline=T,

                                             legend=T,

                                             grid=F,

                                             units=F,

                                             fontsize=8))

# changing the preferences (appearance of the elements)

plotNCAParametersCorrelation(parametersRows = c("Cmax","Lambda_z"),

                             settings=list(legend=T, spline=T),

                             preferences = list(obs=list(color="#202120",

                                                         radius=2,

                                                         shape=18,

                                                         legend="NCA parameter values"),

                                                regressionLine=list(color="#202120",

                                                                    lineWidth=1,

                                                                    lineType="dashed",

                                                                    legend="Linear regression line"),

                                                spline=list(color="#97989c",

                                                            lineWidth=1,

                                                            lineType="solid",

                                                            legend="Spline regression line")))

# split each plot into two subplot for FORM=ref and FORM=test, vertically (ncol=1)

plotNCAParametersCorrelation(parametersRows = c("Cmax","Lambda_z","AUCINF_obs"),

                             settings=list(ncol=1),

                             stratify = list(split=c("FORM")))

# define groups for AGE and HT, color by HT and filter by AGE

plotNCAParametersCorrelation(settings=list(legend=T),

                             parametersRows = c("Cmax","Lambda_z"),

                             stratify = list(groups=list(list(name="AGE", definition=c(30, 35)),

                                                         list(name="HT", definition=c(184.5))),

                                             color="HT",

                                             colors=c("#cdced1","#161617"),

                                             filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotNCAParametersCorrelation(settings=list(legend=T),

                             parametersRows = c("Cmax","Lambda_z"),

                             stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runNCAEstimation()

# plot a single parameter, as pdf or cdf

plotNCAParametersDistribution(parameters = "AUCINF_obs", settings = list(plot = "pdf"))

plotNCAParametersDistribution(parameters = "Lambda_z", settings = list(plot = "cdf"))

# with a single parameter, a ggplot object if returned and additional ggplot elements can be added

library(ggplot2)

plotNCAParametersDistribution(parameters = "AUCINF_obs")+geom_vline(xintercept=140)

# display only 4 parameters in a 2x2 matrix (ncol=2)

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings = list(ncol=2))

# changing the settings to choose which elements to display

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings = list(ncol=2,

                                              units=F,

                                              legend=T,

                                              grid=F,

                                              fontsize=9))

# changing the preferences (appearance of the elements) when using plot="cdf"

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                             settings = list(ncol=2, legend=T, plot="cdf"),

                             preferences = list(empirical=list(color="#161617",

                                                               lineWidth=1,

                                                               lineType="solid",

                                                               legend="cumulative density distribution")))

# changing the preferences (appearance of the elements) when using plot="pdf"

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings = list(ncol=2, legend=T, plot="pdf"),

                              preferences = list(histogramBar=list(fill="#cdced1",

                                                                   opacity=0.7,

                                                                   stroke="#161617",

                                                                   strokeWidth=1,

                                                                   legend="probability density distribution")))

# split each plot into two subplots for FORM=ref and FORM=test

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings = list(ncol=2),

                              stratify = list(split=c("FORM")))

# define groups for AGE and HT, filter by AGE and split by HT

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings=list(ncol=2),

                              stratify = list(groups=list(list(name="AGE", definition=c(30, 35)),

                                                          list(name="HT", definition=c(184.5))),

                                              filter=list("AGE",c(1,3)),

                                              split="HT"))

# filter to keep only second sequence (TR) and Period=1

plotNCAParametersDistribution(parameters = c('AUCINF_obs', 'AUClast', 'Cl_F_obs', 'Clast'),

                              settings=list(ncol=2),

                              stratify = list(filter=list(list("SEQ",2),list("Period","1"))))

#

  project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx")

  loadProject(project)

  runNCAEstimation()

# choosing to display only a subset of possible covariates and NCA parameters  

plotNCAParametersVsCovariates(parameters = c("Cmax","Lambda_z"),

                              covariates = c("WT","FORM"))

# changing the settings to choose which elements to display

plotNCAParametersVsCovariates(settings = list(regressionLine=F,

                                              spline=T,

                                              boxplotData="aligned",

                                              legend=T,

                                              grid=F,

                                              units=F,

                                              fontsize=8))

# changing the preferences (appearance of the elements)

plotNCAParametersVsCovariates(parameters = c("Cmax","Lambda_z"),

                              covariates = c("WT","FORM"),

                              settings=list(legend=T, spline=T, boxplotData="spread"),

                              preferences = list(boxplot=list(fill="#FBFBFB",

                                                              opacity=0.5,

                                                              legend=""),

                                                 boxplotOutlier=list(color="#D71313",

                                                                     size=1.5,

                                                                     shape=2),

                                                 boxplotValues=list(color="#1642f2",

                                                                    radius=1.5,

                                                                    shape=17,

                                                                    opacity=0.3,

                                                                    legend="NCA parameter values"),

                                                 obs=list(color="#1642f2",

                                                          radius=2,

                                                          shape=17,

                                                          legend="NCA parameter values"),

                                                 regressionLine=list(color="#202120",

                                                                     lineWidth=1,

                                                                     lineType="dashed",

                                                                     legend="Linear regression line"),

                                                 spline=list(color="#97989c",

                                                             lineWidth=1,

                                                             lineType="solid",

                                                             legend="Spline regression line")))

# split each plot into two subplot for FORM=ref and FORM=test, vertically (ncol=1)

plotNCAParametersVsCovariates(parameters = c("Cmax","Lambda_z","AUCINF_obs"),

                              covariates = c("WT","AGE"),

                              settings=list(ncol=1),

                              stratify = list(split=c("FORM")))                                                             

# define groups for AGE and HT, color by HT and filter by AGE

plotNCAParametersVsCovariates(settings=list(legend=T),

                              parameters = c("Cmax","Lambda_z"),

                              covariates = c("WT","FORM"),

                              stratify = list(groups=list(list(name="AGE", definition=c(30, 35)), 

                                                          list(name="HT", definition=c(184.5))),

                                              color="HT",

                                              colors=c("#cdced1","#161617"),

                                              filter=list("AGE",c(1,3))))

# filter to keep only second sequence (TR) and FORM=test

plotNCAParametersVsCovariates(settings=list(legend=T),

                              parameters = c("Cmax","Lambda_z"),

                              covariates = c("WT","FORM"),

                              stratify = list(filter=list(list("SEQ",2),list("FORM","test"))))

#

  preferences <- getPlotPreferences(update = list(

    obs = list(color = "red", legend = "Observations"),

    obsCens = list(color = rgb(70, 130, 180, maxColorValue = 255))

  ))

  # preferences that are used by default in the plots

  preferences <- getPlotPreferences()

  # preferences that are used by default in plotObservedData

  preferences <- getPlotPreferences(plotName = "plotObservedData")

## End(Not run)

#

  getPlotPreferences()$obs[c("color", "legend")]

  update = list(obs = list(color = "green", legend = "Observation"))

  setPlotPreferences(update = update)

  getPlotPreferences()$obs[c("color", "legend")]

  resetPlotPreferences()

  getPlotPreferences()$obs[c("color", "legend")]

## End(Not run)

#

  getPlotPreferences()$obs[c("color", "legend")]

  update = list(obs = list(color = "green", legend = "Observation"))

  setPlotPreferences(update = update)

  getPlotPreferences()$obs[c("color", "legend")]

## End(Not run)

#

[PKanalix only]

exportProject(settings = list(targetSoftware = "monolix", filesNextToProject = F))

exportProject(settings = list(targetSoftware = "monolix", filesNextToProject = F, exportedUnusedCovariates = list(all = FALSE, name = c("sex", "weight"))))

[Monolix only]

exportProject(settings = list(targetSoftware = "simulx", filesNextToProject = T, dataFilePath = "data.txt", dataFileType = "vpc"))

exportProject(settings = list(targetSoftware = "simulx", filesNextToProject = F, dataFilePath = "/path/to/data/data.txt"))

[Simulx only]

exportProject(settings = list(targetSoftware = "pkanalix", dataFilePath = "data.txt", modelFileName = "model.txt"))

exportProject(settings = list(targetSoftware = "pkanalix", dataFilePath = "/path/to/data/data.txt"))

## End(Not run)

# Working example to export a Monolix project to Simulx. The resulting .smlx file can be opened from Simulx GUI.

initializeLixoftConnectors(software = "monolix", force = TRUE)

loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran"))

runScenario()

exportProject(settings = list(targetSoftware = "simulx"), force = TRUE)

saveProject("importFromMonolix.smlx")

#

data = getData()

data

-> $dataFile

     "/path/to/data/file.txt"

   $header

     c("ID","TIME","CONC","SEX","OCC")

   $headerTypes

     c("ID","TIME","OBSERVATION","CATEGORICAL COVARIATE","IGNORE")

   $observationNames

     c("concentration")

   $observationTypes

     c(concentration = "continuous")

   $sheet

     "sheet1"

## End(Not run)

#

getLibraryModelContent("oral1_1cpt_kaVCl")

model <- getLibraryModelContent(filename = "lib:oral1_1cpt_kaVCl.txt", print = FALSE)

## End(Not run)

#

getLibraryModelName(library = "pk", filters = list(administration = "oral", delay = "lagTime", absorption = "firstOrder", distribution = "1compartment", elimination = "linear", parametrization = "clearance"))

# returns "lib:oral1_1cpt_TlagkaVCl.txt"

getLibraryModelName("pd", list(response = "turnover", drugAction = "productionStimulation"))

# returns c("lib:turn_input_Emax.txt", "lib:turn_input_gammaEmax.txt")

## End(Not run)

#

f = getMapping()

f$mapping

  -> list( list(obsId = "1", modelOutput = "Cc", observationName = "concentration", type = "continuous"),

           list(obsId = "2", modelOutput = "Level", type = "discrete") )

f$freeData

  -> list( list(obsId = "3", type = "event") )

f$freePredictions

  -> list( list(modelOutput = "Effect", type = "continuous") )

## End(Not run)

#

getStructuralModel() => "/path/to/model/inclusion/modelFile.txt"

## End(Not run)

# Get the name and see the content of the model used in warfarin demo project

initializeLixoftConnectors("monolix", force = TRUE)

loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran"))

libModelName <- getStructuralModel()

getLibraryModelContent(libModelName)

# Get the name of the model file used in Simulx

initializeLixoftConnectors("simulx", force = TRUE)

project_name <- file.path(getDemoPath(), "1.overview", "newProject_TMDDmodel.smlx")

loadProject(project_name)

getStructuralModel()

# Get the name of the model file imported to Simulx

initializeLixoftConnectors("monolix", force = TRUE)

project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran")

loadProject(project_name)

getStructuralModel()

initializeLixoftConnectors("simulx", force = TRUE)

importProject(project_name)

getStructuralModel()

#

initializeLixoftConnectors(software = "simulx", force = TRUE)

importProject("/path/to/project/file.mlxtran") 

importProject("/path/to/project/file.pkx") 

initializeLixoftConnectors(software = "monolix", force = TRUE)

importProject("/path/to/project/file.pkx") 

initializeLixoftConnectors(software = "pkanalix", force = TRUE)

importProject("/path/to/project/file.mlxtran") 

## End(Not run)

# working example to import a Monolix demo project into Simulx. The resulting .smlx file can be opened from Simulx GUI.

initializeLixoftConnectors(software = "monolix", force = TRUE)

MonolixDemoPath = file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran")

initializeLixoftConnectors(software = "simulx", force = TRUE)

importProject(MonolixDemoPath)

saveProject("importFromMonolix.smlx")

#

project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran")

loadProject(project_name)

isProjectLoaded()

initializeLixoftConnectors("pkanalix")

isProjectLoaded()

#

loadProject("/path/to/project/file.mlxtran") for Linux platform

loadProject("C:/Users/path/to/project/file.mlxtran") for Windows platform

## End(Not run)

# Load a Monolix project

initializeLixoftConnectors("monolix")

project_name <- file.path(getDemoPath(), "8.case_studies", "hiv_project.mlxtran")

loadProject(project_name)

# Load a PKanalix project

initializeLixoftConnectors("pkanalix")

project_name <- file.path(getDemoPath(), "1.basic_examples", "project_censoring.pkx")

loadProject(project_name)

# Load a Simulx project

initializeLixoftConnectors("simulx")

project_name <- file.path(getDemoPath(), "2.models", "longitudinal.smlx")

loadProject(project_name)

#

initializeLixoftConnectors("monolix")

data_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "data", "warfarin_data.csv")

newProject(data = list(dataFile = data_file, 

                       headerTypes = c("id", "time", "amount", "observation", "obsid", "contcov", "catcov", "contcov")),

           modelFile = "lib:oral1_1cpt_IndirectModelInhibitionKin_TlagkaVClR0koutImaxIC50.txt",

           mapping = list(list(obsId = "1",

                               modelOutput = "Cc",

                               observationName = "y1"),

                          list(obsId = "2",

                               modelOutput = "R",

                               observationName = "y2")))

# Create a new PKanalix project

initializeLixoftConnectors("pkanalix")

data_file <- file.path(getDemoPath(), "1.basic_examples", "data", "data_BLQ.csv")

newProject(data = list(dataFile = data_file,

                       headerTypes = c("id", "time", "amount", "observation", "cens", "catcov")))

# Create a new Simulx project

initializeLixoftConnectors("simulx")

newProject(modelFile = "lib:oral1_1cpt_IndirectModelInhibitionKin_TlagkaVClR0koutImaxIC50.txt")

#

[PKanalix only]

saveProject("/path/to/project/file.pkx") # save a copy of the model

[Monolix only]

saveProject("/path/to/project/file.mlxtran") # save a copy of the model

[Simulx only]

saveProject("/path/to/project/file.smlx") # save a copy of the model

[Monolix - PKanalix - Simulx] 

saveProject() # update current model

## End(Not run)

# Load, change and save a PKanalix project under a new name

initializeLixoftConnectors("pkanalix")

project_name <- file.path(getDemoPath(), "1.basic_examples", "project_censoring.pkx")

loadProject(project_name)

setNCASettings(blqMethodAfterTmax = "missing")

saveProject("~/changed_project.pkx")

# Load, change and save a Monolix project under a new name

initializeLixoftConnectors("monolix")

project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran")

loadProject(project_name)

addContinuousTransformedCovariate(tWt = "3*exp(wt)")

saveProject("~/changed_project.mlxtran")

# Load, change and save a Simulx project under a new name

initializeLixoftConnectors("simulx")

project_name <- file.path(getDemoPath(), "2.models", "longitudinal.smlx")

loadProject(project_name)

defineTreatmentElement(name = "trt", element = list(data = data.frame(time = 0, amount = 100)))

saveProject("~/changed_project.smlx")

#

setData(dataFile = "/path/to/data/file.txt", 

        headerTypes = c("IGNORE", "OBSERVATION"), observationTypes = "continuous")

setData(dataFile = "/path/to/data/file.txt", 

        headerTypes = c("IGNORE", "OBSERVATION", "YTYPE"), 

       observationTypes = list(Concentration = "continuous", Level = "discrete"))

setData(dataFile = "/path/to/data/file.xlsx", sheet = "sheet2",

        headerTypes = c("IGNORE", "OBSERVATION", "YTYPE"), 

       observationTypes = list(Concentration = "continuous", Level = "discrete"))

## End(Not run)

#

[Monolix] setMapping(list(list(obsId = "1", modelOutput = "Cc", observationName = "concentration"), list(obsId = "2", modelOutput = "Level")))

[PKanalix] setMapping(list(list(obsId = "1", modelOutput = "Cc"), list(obsId = "2", modelOutput = "Level")))

## End(Not run)

#

setStructuralModel("/path/to/model/file.txt")

setStructuralModel("'lib:oral1_2cpt_kaClV1QV2.txt'")

# working example to set a model from the library:

initializeLixoftConnectors("monolix",force = TRUE)

loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran"))

#check model currently loaded:

getStructuralModel()

#get the name for a model from the library with 2 compartments:

LibModel2cpt = getLibraryModelName(library = "pk", filters = list(administration = "oral", delay = "lagTime", absorption = "firstOrder", distribution = "2compartments", elimination = "linear", parametrization = "clearance"))

#check model content:

getLibraryModelContent(LibModel2cpt)

#set this new model in the project:

setStructuralModel(LibModel2cpt)

# check that the project has now the new model instead of the previous one:

getStructuralModel()

## End(Not run)

#

shareProject("/path/to/archive.zip")

## End(Not run)

#

getPreferences() # retrieve a list of all the general settings

getPreferences("imageFormat","exportCharts") 

# retrieve only the imageFormat and exportCharts settings values

## End(Not run)

#

getProjectSettings() # retrieve a list of all the project settings

## End(Not run)

#

setPreferences(exportCharts = FALSE, delimiter = ",")

## End(Not run)

#

setProjectSettings(directory = "/path/to/export/directory", seed = 12345)

## End(Not run)

#

generateReport()

generateReport(templateFile = "/path/to/template.docx")

generateReport(templateFile = "/path/to/template.docx", tablesStyle = "Plain Table 1", watermark = list(text = "watermark", fontSize = 15))

## End(Not run)

# Working example to generate a default report ###

initializeLixoftConnectors("monolix")

loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran"))

runScenario()

reportPath = tempfile("report", fileext = ".docx")

generateReport(reportFile = list(nextToProject = FALSE, path = reportPath))

file.show(reportPath)

# Working example to generate a report with a custom template###

# Note that only tables get replaced. It is not possible to add plots to a report via connectors, but it can be done in the GUI.

initializeLixoftConnectors("pkanalix")

loadProject(file.path(getDemoPath(),"2.case_studies","project_aPCSK9_SAD.pkx"))

runScenario()

reportPath = tempfile("report", fileext = ".docx")

generateReport(templateFile = file.path(getDemoPath(),"2.case_studies","report_templates","PK_report_template_aPCSK9.docx"), reportFile = list(nextToProject = FALSE, path = reportPath))

file.show(reportPath)

#

bioeqResults = getBioequivalenceResults() # retrieve all the results values.

bioeqResults = getBioequivalenceResults("anova", "confidenceIntervals") # retrieve anova and confidence intervals results.

## End(Not run) 

#

getCACost()

 -> data.frame( Cost = -2.2, -2LL = 15.12, AIC = 17.54, BIC = 18.45 )

## End(Not run)            

#

indivParams = getCAIndividualParameters() # retrieve all the available individual parameters values.

indivParams = getCAIndividualParameters("ka", "V") # retrieve ka and V values for all individuals.

  $parameters

      id  ka    V

      1   0.8  1.2

      .   ...  ...

      N   0.4  2.2

## End(Not run)

#

indivParams = getCAParameterStatistics() 

# retrieve all the available parameter values.

indivParams = getCAParameterStatistics(parameters = c("ka", "V")) 

# retrieve ka and V values for all individuals.

  $parameters

  parameter         min         Q1    median        Q3       max      mean         SD          SE       CV   geoMean    geoSD

         ka  0.05742669 0.08886395 0.1186787 0.1495961 0.1983748 0.1221367 0.03898449 0.007957675 31.91873 0.1159751 1.398436

          V    7.859237   13.51599  23.00674  30.73677  43.39608  22.95211   10.99187    2.243707 47.89047  20.23981 1.704716

#' statistics = getCAParameterStatistics(stratification = list(groups = list(name = "WEIGHT", definition = c(65, 70, 72)), state = list(split = "WEIGHT")))

# retrieve the values of all the available parameters split by WEIGHT.

## End(Not run)

#

getCAResultsStratification()

  $groups

    list(

      list( name = "WEIGHT",

            definition = c(70),

            type = "continuous",

            range = c(65,85) ),

      list( name = "TRT",

            definition = list(c("a","b"), "c")

            type = "categorical",

            categories = c("a","b","c") )

    )

  $state

    $split

      "WEIGHT"

    $filter

      list(list("WEIGHT", c(1,3), list("TRT", c(1)))

## End(Not run)

#

indivParams = getNCAIndividualParameters() 

# retrieve the values of all the available parameters.

indivParams = getNCAIndividualParameters("Tmax","Clast") 

# retrieve only the values of Tmax and Clast for all individuals.

   $parameters

      id  Tmax Clast

      1   0.8  1.2

      .   ...  ...

      N   0.4  2.2

   $information

          CDISC

     Tmax  TMAX

     Clast CLST    

## End(Not run)

#

loadProject(paste0(getDemoPath(),"/1.basic_examples/project_accumulationRatio.pkx"))

runNCAEstimation()

getNCAIndividualRatios()

#

statistics = getNCAParameterStatistics()

# retrieve the values of all the available parameters.

statistics = getNCAParameterStatistics(parameters = c("Tmax","Clast"))

# retrieve only the values of Tmax and Clast for all individuals.

  $statistics

  parameter     min      Q1  median       Q3     max     mean         SD         SE       CV Ntot Nobs Nmiss  geoMean    geoSD

       Tmax     2.5     2.5    2.75        3       3     2.75  0.3535534       0.25 12.85649    2    2     0 2.738613   1.1376

      Clast 0.76903 0.76903 0.85836  0.94769 0.94769  0.85836  0.1263317    0.08933  14.7178    2    2     0 0.853699  1.15918

  $information

           units CDISC

  Tmax         h  Tmax

  Clast mg.mL^-1 Clast 

statistics = getNCAParameterStatistics(stratification = list(groups = list(name = "WEIGHT", definition = c(65.5, 72)), state = list(split = "WEIGHT")))

# retrieve the values of all the available parameters splitted by WEIGHT.

## End(Not run)

#

loadProject(paste0(getDemoPath(),"/1.basic_examples/project_accumulationRatio.pkx"))

runNCAEstimation()

getNCARatioStatistics()

#

getNCAResultsStratification()

  $groups

    list(

      list( name = "WEIGHT",

            definition = c(70),

            type = "continuous",

            range = c(65,85) ),

      list( name = "TRT",

            definition = list(c("a","b"), "c")

            type = "categorical",

            categories = c("a","b","c") )

    )

  $state

    $split

      "WEIGHT"

    $filter

      list("Span", list("TRT", c(1)))

## End(Not run)

#

pointsIncluded = getPointsIncludedForLambdaZ()

       ID time concentration BLQ includedForLambdaZ

  1     0  0.0          0.00   0                  0

  2     0  0.5          3.05   0                  1

  3     0  2.0          5.92   1                  1

## End(Not run)

#

setCAResultsStratification(split = "SEX")

setCAResultsStratification(split = c("SEX", "WEIGHT"))

setCAResultsStratification(filter = list("SEX", 1))

setCAResultsStratification(filter = list(list("SEX", 1), list("WEIGHT", c(1,3))))

setCAResultsStratification(split = "WEIGHT", filter = list(list("TRT", c(1,2))),

groups = list(list(name = "WEIGHT", definition = c(65,5, 72)), list(name = "TRT", definition = list(c("a","b"), "c", c("d","e")))))

s = getCAResultsStratification()

setCAResultsStratification(state = s$state, groups = s$groups)

## End(Not run)

#

setNCAResultsStratification(split = "SEX")

setNCAResultsStratification(split = c("SEX", "WEIGHT"))

setNCAResultsStratification(filter = "Span")

setNCAResultsStratification(filter = list("Span", list("SEX", 1)))

setNCAResultsStratification(split = "WEIGHT", filter = list(list("TRT", c(1,2))),

groups = list(list(name = "WEIGHT", definition = c(65,5, 72)), list(name = "TRT", definition = list(c("a","b"), "c", c("d","e")))))

s = getNCAResultsStratification()

setNCAResultsStratification(state = s$state, groups = s$groups)

## End(Not run)

#

computeChartsData() # Monolix - PKanalix - Simulx

computeChartsData(plot = "vpc", output = "y1") # Monolix

## End(Not run)

#

lastRunInfo = getLastRunStatus()

lastRunInfo$status

 -> TRUE

lastRunInfo$report

 -> ""

## End(Not run)

#

[MONOLIX]

scenario = getScenario()

scenario

 -> $tasks 

    populationParameterEstimation conditionalDistributionSampling conditionalModeEstimation standardErrorEstimation logLikelihoodEstimation plots

    TRUE                          TRUE							 TRUE                      FALSE                   FALSE                   FALSE 

    $linearization = T

    $plotList = "outputplot", "vpc"

[PKANALIX]

scenario = getScenario()

scenario

    nca     be 

   TRUE  FALSE

[SIMULX]

scenario = getScenario()

scenario

  simulation  endpoints 

        TRUE      FALSE

## End(Not run) 

#

runScenario()

## End(Not run)

#

[MONOLIX]

scenario = getScenario()

scenario$tasks = c(populationParameterEstimation = T, conditionalModeEstimation = T, conditionalDistributionSampling = T)

setScenario(scenario)

[PKANALIX]

scenario = getScenario()

scenario = c(nca = T, be = F)

setScenario(scenario)

[SIMULX]

scenario = getScenario()

scenario = c(simulation = T, endpoints = F)

setScenario(scenario)

## End(Not run)

#

autoinitvalues <- getCAParametersByAutoInit()

setCAInitialValues(initialValues = autoinitvalues)

## End(Not run)

#

runNCAEstimation()

## End(Not run)

#

runCAEstimation()

## End(Not run)

#

runEstimation()

## End(Not run)

#

runNCAEstimation()

## End(Not run)

#

loadProject(paste0(getDemoPath(),"/1.basic_examples/project_covariates.pkx"))

createCustomNCAParameter(name="CLperkg", 

                         formula = "Cl_F_obs/WEIGHT", 

                         alias = "CL/kg", 

                         unit = "VOLUME.TIME^-1.KG^1",

                         addToPreferences = FALSE)

#

loadProject(paste0(getDemoPath(),"/2.case_studies/project_Theo_extravasc_SD.pkx"))

createNCARatio(name="AUCratio", variable="FORM", reference = "ref", test = "test", parameter = "AUCINF_obs")

#

loadProject(file.path(getDemoPath(), "1.basic_examples", "project_accumulationRatio.pkx"))

deleteNCARatio(name = "accumulationRatio")

getNCARatios()

#

getBioequivalenceSettings() # retrieve a list of all the bioequivalence methodology settings

getBioequivalenceSettings("level","bioequivalenceLimits") # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

#

getCASettings() # retrieve a list of all the CA methodology settings

getCASettings("weightingca","blqmethod") # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

#

getDataSettings() # retrieve a list of all the NCA methodology settings

getDataSettings("urinevolume") # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

#

loadProject(paste0(getDemoPath(),"/1.basic_examples/project_accumulationRatio.pkx"))

getNCARatios()

#

getNCASettings() # retrieve a list of all the NCA methodology settings

getNCASettings("lambdaRule","integralMethod") # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

#

setBioequivalenceSettings(level = 90, bioequivalencelimits = c(85, 115)) # set the settings whose name has been passed in argument

setBioequivalenceSettings(computedbioequivalenceparameters = data.frame(parameters = c("Cmax", "Tmax"), logtransform = c(TRUE, FALSE)))

setBioequivalenceSettings(linearmodelfactors = list(id="SUBJ", period="OCC", formulation="FORM", reference="ref", sequence="SEQ", additional=c("Group","Phase")))

## End(Not run)

#

  setCAInitialValues(list(Cl=list(value=0.4, constraint = "none"), V=list(value=0.5, constraint="positive"), ka=list(value=0.04, constraint="bounded", limits=c(0, 1))))

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#

setCASettings(weightingCA = "uniform", blqMethod = "zero") # set the settings whose name has been passed in argument

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#

setDataSettings("datatype" = list("Y" ="plasma")) # set the settings whose name has been passed in argument

setDataSettings("units"=list(dose="ng",time="h",volume="mL", grading=""))

setDataSettings("scalings"=list(dose=0.001, time=24))

## End(Not run)

#

setNCASettings(integralMethod = "LinLogTrapLinLogInterp", weightingnca = "uniform") # set the settings whose name has been passed in argument

setNCASettings(administrationType = list("1"="extravascular")) # set the administration id "1" to extravascular

setNCASettings(startTimeNotBefore = list(TRUE, 15)) # set the estimation of the lambda_z with points with time over 15

setNCASettings(timeValuesPerId = list('1'=c(4, 6, 8, 30), '4'=c(8, 12, 18, 24, 30))) # set the points to use for the lambda_z to time={4, 6, 8, 30} for id '1' and ime={8, 12, 18, 24, 30} for id '4' 

setNCASettings(timeValuesPerId = NULL) # set the points to use for the lambda_z to the default rule

setNCASettings(sparse=TRUE, sparseStratification=list("DGROUP", "STUDY"), sparseCensoring=list(rule = list(criterion = "N_BLQ >= 4", target = "individualValues", rules = c("missing", "loq")))) # set sparse NCA with stratication by STUDY and DGROUP. At each time point and in each group, if there are less than 4 censored samples they are considered as missing (ignored), otherwise the mean value is replaced by the largest LOQ.

setNCASettings(sparse=T, sparseCensoring=list(rule = "zero", checkMeanSamples = list(loq = 3500, blqBeforeTmax = "zero", blqAfterTmax = "missing"))) # set sparse NCA. At each time point, censored samples are replaced by zero to calculate mean samples. If calculated mean samples are smaller than 3500 they are replaced by zero before Tmax and ignored after Tmax.

## End(Not run)