Create VPC plot for fraction BLQ values

plotBLQVPC_IQRdataVPC.Rd

Determines CI for the fraction of BLQ values across simulated trials and the fraction of BLQ values for the observation and creates a corresponding VPC plot.

plotBLQVPC_IQRdataVPC(
  dataVPC,
  stratifyBy = NULL,
  periodBy = NULL,
  FLAGstratifyByPeriod = FALSE,
  filename = NULL,
  FLAGuseTAD = FALSE,
  FLAGdataPlotOnly = FALSE,
  FLAGplotBins = FALSE,
  FLAGplotN = FALSE,
  FLAGsmooth = FALSE,
  smoothDFfact = 0.5,
  title = NULL,
  subtitle = NULL,
  BIN.column = NULL,
  BIN.breaks = NULL,
  BIN.groupsize = NULL,
  BIN.lambda = 1,
  BIN.resolution = 0.1,
  CIlevel = 95,
  alphaDataPoints = 0.4,
  nrow = 1,
  ncol = 1
)

Arguments

dataVPC

Named list with simulated (sim) and potentially typical predictions for observed (obs) data

stratifyBy

Column name used to stratify VPC plots

periodBy

Colum name identifying different periods (time-varying categorical covariates) Time courses are binned and plotted separately. Whether they are plotted in the same panel is set by FLAGstratifyByPeriod

FLAGstratifyByPeriod

Flag whether period are plotted in separate panels

filename

Filename for export of the VPC to a PDF. If NULL object including plotting data is returned.

FLAGuseTAD

logical. If TRUE then time after previous dose (TAD) used for x-axis. If TRUE then

FLAGdataPlotOnly

logical. If TRUE then no simulation is done and only the observed fraction of BLQ data is plotted. This allows to find suitable settings for the binning parameters BIN.groupsize and BIN.lambda.

FLAGplotBins

Flag whether to plot the bin boundaries as vertical lines

FLAGplotN

Flag whether to display number of subjects in panel

FLAGsmooth

Flag whether CI of simulated data should be smoothed

smoothDFfact

Factor to multiply the number of unique time points determining the degrees of freedom for smoothing

title

Character string to be plotted as plot title (on each page)

subtitle

Character string to be plotted as plot subtitle (on each page)

BIN.column

Column name of integer column assigning the observations to time bins. Defaults to NULL such that bins are automatically generated or the user-provided BIN.breaks are used.

BIN.breaks

Numerical vector with bin borders that are used for the observation times. Defaults to NULL such that bins are automatically generated. This input is ignored if BIN.column is provided.

BIN.groupsize

Smallest expected group size for the binning. By default the round(0.5 x number of subjects) in the dataset is used.

BIN.lambda

Penalization of intra-group variance, set to 1 to have more groups and set to 0 to get less but larger groups.

BIN.resolution

Gaps between groups of data points greater than resolution lead to separation of groups

CIlevel

Confidence interval level (in percent)

alphaDataPoints

Alpha transparency level for data points

nrow

Number of panel rows for plot layout

ncol

Number of panel columns for plot layout

Value

If no filename is given, ggplot object with additional plotting information

Details

Input dataVPC is the output of vpc_IQRnlmeProject.

See also

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProject(), as_IQRnlmeProjectMulti(), bootstrap_IQRnlmeProject(), compareModels_IQRnlmeProjectMulti(), convertETAINDIVPRED_IQRnlmeProject(), covariateEffect_IQRnlmeProject(), data_IQRest(), dosing_IQRest(), duplicate_IQRnlmeProject(), eigenvalues_IQRnlmeProject(), exportVirtualSubjects_IQRnlmeProject(), getData_IQRnlmeProject(), getETAs_IQRnlmeProject(), getEst_IQRnlmeProject(), getHeader_IQRnlmeProject(), getIndivParameters_IQRnlmeProject(), getIndivPredictions_IQRnlmeProject(), getModel_IQRnlmeProject(), getObsPred_IQRnlmeProject(), getPopParameters_IQRnlmeProject(), getResults_IQRnlmeProject(), getResults_IQRnlmeProjectMulti(), hasrun_IQRnlmeProject(), hasrun_IQRnlmeProjectMulti(), informationContent_IQRnlmeProject(), is_IQRnlmeEst(), is_IQRnlmeProject(), is_IQRnlmeProjectMulti(), is_MONOLIX_IQRnlmeProject(), is_NLMIXR_IQRnlmeProject(), is_NONMEM_IQRnlmeProject(), modelSpec_IQRest(), outlier_IQRnlmeProject(), plot.IQRnlmeProject(), plot.IQRnlmeProjectMulti(), plotConvergence_IQRnlmeProject(), plotETACOV_IQRnlmeProject(), plotETA_IQRnlmeProject(), plotGOF_IQRnlmeProject(), plotINDIVSIM_IQRnlmeProject(), plotINDIV_IQRnlmeProject(), plotMEANSIM_IQRnlmeProject(), plotVPC_IQRdataVPC(), pred_IQRnlmeProject(), print.IQRnlmeEst(), print.IQRnlmeProject(), print.IQRnlmeProjectMulti(), print_modelSpec(), run_IQRnlmeProject(), run_IQRnlmeProjectMulti(), sample_IQRnlmeProject(), scm_IQRnlmeProject(), summary.IQRnlmeProject(), summary.IQRnlmeProjectMulti(), summaryComments_IQRnlmeProjectMulti(), summaryCorrelations_IQRnlmeProjectMulti(), summaryCovariates_IQRnlmeProjectMulti(), summaryParameters_IQRnlmeProjectMulti(), vpc_IQRnlmeProject()

Other VPC: extractBins_VPCplot(), filterMutateDataVPC(), plotVPC_IQRdataVPC(), plotVPC_IQRtteProject(), vpc_IQRnlmeProject()

Author

Anne Kuemmel, IntiQuan