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plotObsVsPred

Source: R/plot-obs-vs-pred.R
plotObsVsPred.Rd

Producing observed vs predicted plots

Usage

plotObsVsPred(
  data,
  metaData = NULL,
  dataMapping = NULL,
  plotConfiguration = NULL,
  foldDistance = NULL,
  smoother = NULL,
  plotObject = NULL
)

Arguments

data

A data.frame to use for plot.

metaData

A named list of information about data such as the dimension and unit of its variables.

dataMapping

A ObsVsPredDataMapping object mapping x, y and aesthetic groups to their variable names of data.

plotConfiguration

An optional ObsVsPredConfiguration object defining labels, grid, background and watermark.

foldDistance

Numeric values of fold distance lines to display in log plots. This argument is internally translated into lines field of dataMapping. Caution: this argument is meant for log scaled plots and since fold distance is a ratio it is expected positive. In particular, line of identity corresponds to a foldDistance of 1.

smoother

Optional name of smoother function:

  • "loess" for loess regression

  • "lm" for linear regression

plotObject

An optional ggplot object on which to add the plot layer

Value

A ggplot object

See also

Other molecule plots: plotBoxWhisker(), plotCumulativeTimeProfile(), plotDDIRatio(), plotGrid(), plotHistogram(), plotObservedTimeProfile(), plotPKRatio(), plotPieChart(), plotQQ(), plotResVsPred(), plotResVsTime(), plotSimulatedTimeProfile(), plotTimeProfile(), plotTornado()

Examples

# Produce Obs vs Pred plot
obsVsPredData <- data.frame(x = c(1, 2, 1, 2, 3), y = c(5, 0.2, 2, 3, 4))

plotObsVsPred(data = obsVsPredData, dataMapping = ObsVsPredDataMapping$new(x = "x", y = "y"))


# Produce Obs vs Pred plot with linear regression
plotObsVsPred(
  data = obsVsPredData,
  dataMapping = ObsVsPredDataMapping$new(x = "x", y = "y"),
  smoother = "lm"
)


# Produce Obs vs Pred plot with user-defined fold distance lines
plotObsVsPred(
  data = obsVsPredData,
  dataMapping = ObsVsPredDataMapping$new(x = "x", y = "y"),
  plotConfiguration = ObsVsPredPlotConfiguration$new(
    xScale = Scaling$log, xAxisLimits = c(0.05, 50),
    yScale = Scaling$log, yAxisLimits = c(0.05, 50)
  ),
  foldDistance = c(1, 10)
)