fitPlot: generates a ggplot object of the fit table
In BenBruyneel/BBPersonalR: BBPersonalR

fitPlot

R Documentation

generates a ggplot object of the fit table

Description

generates a ggplot object of the fit table

Usage

fitPlot(
  fitTable,
  predictorColumn = 1,
  responseColumn = 2,
  weights = NULL,
  fit = fitLM(fitTable, predictorColumn = predictorColumn, responseColumn =
    responseColumn, weights = weights),
  title = "",
  figureNo = 1,
  caption = paste(c("Figure ", figureNo, ": Calibration Curve"), collapse = ""),
  autoScalePredictor = FALSE,
  predictorLimits = c(0, "N/A"),
  predictorLabel = NULL,
  predictorOob = 1,
  autoScaleResponse = FALSE,
  responseLimits = c(0, "N/A"),
  responseLabel = NULL,
  responseOob = 1,
  regressionColor = "red",
  regressionLineType = 1,
  regressionWidth = 1,
  regressionAlpha = 1,
  pointsColor = "Black",
  pointsFill = "black",
  pointsShape = 21,
  pointsSize = 2,
  pointsAlpha = 1,
  showConfidence = TRUE,
  confidenceLevel = 0.95,
  confidenceColor = "green",
  confidenceAlpha = 0.2,
  showPrediction = FALSE,
  predictionLevel = 0.95,
  predictionColor = "blue",
  predictionAlpha = 0.05,
  rotateGraph = FALSE,
  predictorOptions = "1",
  responseOptions = "1"
)

Arguments

`fitTable`	data.frame containing the data to be fitted
`predictorColumn`	can be integer (column number) or character (column name)
`responseColumn`	can be integer (column number) or character (column name)
`weights`	default = NULL, otherwise must be integer to give weights to the predictor values (see also function weightsVector) or a numeric vector with length = number of rows in calibrationTable. Note: if a vector of weights if provided, the prediction to the minimal/maximal values on the x- & y-axis is not possible
`fit`	model object (eg coming from lm() or fitLM())
`title`	title for the graph, character vector
`figureNo`	number of the figure, used for the caption ("Figure ..." )
`caption`	caption for the graph, character vector
`autoScalePredictor`	if TRUE then default autoscaling of the x-axis takes place, most other settings delaing with scaling of the x-axis the are then ignored
`predictorLimits`	2 element numeric vector with the minimum and maximum value for the x-axis. If an element is a character vector or something that cannot be converted to a numeric value, then NA is used which leads to minimum/maximum value
`predictorLabel`	name of the x-axis, character vector
`predictorOob`	integer vector, indicating how to deal with out of bounds datapoints. 1 = censor scales::oob_censor is used, 2 = infinite, scales::oob_squish_infinite is used. Note: x-axis only
`autoScaleResponse`	if TRUE then default autoscaling of the y-axis takes place,most other settings delaing with scaling of the y-axis the are then ignored
`responseLimits`	2 element numeric vector with the minimum and maximum value for the y-axis. If an element is a character vector or something that cannot be converted to a numeric value, then NA is used which leads to minimum/maximum value
`responseLabel`	name of the y-axis, character vector
`responseOob`	integer vector, indicating how to deal with out of bounds datapoints. 1 = censor scales::oob_censor is used, 2 = infinite, scales::oob_squish_infinite is used. Note: y-axis only
`regressionColor`	color of the regression line
`regressionLineType`	appearance of the regression line: 0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash
`regressionWidth`	width of the regression line (default 1)
`regressionAlpha`	alpha ('seethrough' value) of the regressionline
`pointsColor`	(border) color of the datapoints
`pointsFill`	fill color, note: only some shapes have a fill color
`pointsShape`	shape of the datapoints (default = 21), see vignette ggplot2::ggplot2-specs
`pointsSize`	size of the datapoints
`pointsAlpha`	alpha ('seethrough' value) of the datapoints
`showConfidence`	defines if confidence level needs to be shown (usually shown as a shaded area around the line)
`confidenceLevel`	defines the confidence level (range 0-1)
`confidenceColor`	color of the confidence 'area'
`confidenceAlpha`	alpha ('seethrough' value) of the confidence 'area'
`showPrediction`	defines if prediction level needs to be shown (usually shown as a shaded area around the line)
`predictionLevel`	defines the prediction level (range 0-1)
`predictionColor`	color of the prediction 'area'
`predictionAlpha`	alpha ('seethrough' value) of the prediction 'area'
`rotateGraph`	if TRUE, then x-axis and y-axis are swapped, essentially resulting in a graph rotated 90 degrees
`predictorOptions`	x-axis options character vector, "1" normal scale, "2" reversed scale, "3" logarithmic scale (log10)
`responseOptions`	y-axis options character vector, "1" normal scale, "2" reversed scale,"3" logarithmic scale (log10)