patPCA: This function transforms the individual color pattern rasters...
In patternize: Quantification of Color Pattern Variation

patPCA

R Documentation

This function transforms the individual color pattern rasters as obtained by the main patternize functions to a dataframe of 0 and 1 values that can be used for Principal Component Analysis (`prcomp`). This function also allows to plot the analysis including a visualization of the shape changes along the axis. Pixel values are predicted by multiplying the rotation matrix (eigenvectors) with a vector that has the same length as the number of rows in the rotation matrix and in which all values are set to zero except for the PC value for which we want to predict the pixel values.

Description

This function transforms the individual color pattern rasters as obtained by the main patternize functions to a dataframe of 0 and 1 values that can be used for Principal Component Analysis (prcomp). This function also allows to plot the analysis including a visualization of the shape changes along the axis. Pixel values are predicted by multiplying the rotation matrix (eigenvectors) with a vector that has the same length as the number of rows in the rotation matrix and in which all values are set to zero except for the PC value for which we want to predict the pixel values.

Usage

patPCA(
  rList,
  popList,
  colList,
  symbolList = NULL,
  rListPredict = NULL,
  popListPredict = NULL,
  colListPredict = NULL,
  pcaListPredict = NULL,
  pcaPopListPredict = NULL,
  pcaColPredict = "red",
  symbolListPredict = NULL,
  plot = FALSE,
  plotType = "points",
  plotChanges = FALSE,
  PCx = 1,
  PCy = 2,
  plotCartoon = FALSE,
  refShape = NULL,
  outline = NULL,
  lines = NULL,
  landList = NULL,
  adjustCoords = FALSE,
  crop = c(0, 0, 0, 0),
  flipRaster = NULL,
  flipOutline = NULL,
  imageList = NULL,
  cartoonID = NULL,
  refImage = NULL,
  colpalette = NULL,
  normalized = NULL,
  cartoonOrder = "above",
  lineOrder = "above",
  cartoonCol = "gray",
  cartoonFill = NULL,
  plotLandmarks = FALSE,
  landCol = "black",
  zlim = c(-1, 1),
  legendTitle = "Predicted",
  xlab = "",
  ylab = "",
  main = "",
  ...
)

Arguments

`rList`	List of raster objects.
`popList`	List of vectors including sampleIDs for each population.
`colList`	List of colors for each population.
`symbolList`	List with graphical plotting symbols (default = NULL).
`rListPredict`	List of raster objects to predict into PCA space (default = NULL).
`popListPredict`	List of vectors including sampleIDs for each set of predict samples (default = NULL). Note to that this also has to be a list if only one population is included.
`colListPredict`	List of colors for each set of predict samples (default = NULL).
`pcaListPredict`	Points to plot within PCA space.
`pcaPopListPredict`	List of population symbols for plotting additional PCA values.
`pcaColPredict`	Color for additional PCA values.
`symbolListPredict`	List with graphical plotting symbols for predict sets (default = NULL).
`plot`	Whether to plot the PCA analysis (default = FALSE).
`plotType`	Plot 'points' or sample 'labels' (default = 'points')
`plotChanges`	Wether to include plots of the changes along the PC axis (default = FALSE).
`PCx`	PC axis to be presented for x-axis (default PC1).
`PCy`	PC axis to be presented for y-axis (default PC2).
`plotCartoon`	Whether to plot a cartoon. This cartoon should be drawn on one of the samples used in the analysis.
`refShape`	This can be 'target' in case the reference shape is a single sample (for registration analysis) or 'mean' if the images were transformed to a mean shape (only for meanshape when using landmark transformation)
`outline`	xy coordinates that define outline.
`lines`	list of files with xy coordinates of line objects to be added to cartoon.
`landList`	Landmark landmarkList.
`adjustCoords`	Adjust landmark coordinates.
`crop`	Vector c(xmin, xmax, ymin, ymax) that specifies the pixel coordinates to crop the original image used in landmark or registration analysis.
`flipRaster`	Whether to flip raster along xy axis (in case there is an inconsistency between raster and outline coordinates).
`flipOutline`	Whether to flip plot along x, y or xy axis.
`imageList`	List of image should be given if one wants to flip the outline or adjust landmark coordinates.
`cartoonID`	ID of the sample for which the cartoon was drawn.
`refImage`	Image (RasterStack) used for target. Use raster::stack('filename').
`colpalette`	Vector of colors for color palette (default = c("white","lightblue","blue","green", "yellow","red"))
`normalized`	Set this to true in case the summed rasters are already devided by the sample number.
`cartoonOrder`	Whether to plot the cartoon outline 'above' or 'under' the pattern raster (default = 'above'). Set to 'under' for filled outlines.
`lineOrder`	Whether to plot the cartoon lines 'above' or 'under' the pattern raster (default = 'above').
`cartoonCol`	Outline and line color for cartoon (deafault = 'gray').
`cartoonFill`	Fill color for outline of cartoon (default = NULL).
`plotLandmarks`	Whether to plot the landmarks from the target image or mean shape landmarks (default = FALSE).
`landCol`	Color for plotting landmarks (default = 'black').
`zlim`	z-axis limit (default = c(0,1))
`legendTitle`	Title of the raster legend (default = 'Proportion')
`xlab`	Optional x-axis label.
`ylab`	Optional y-axis label.
`main`	Optional main title.
`...`	additional arguments for PCA plot function.

Value

If plot = TRUE: List including a [1] dataframe of the binary raster values that can be used for principle component analysis, [2] a dataframe of sample IDs and specified population colors and [3] prcomp results. If plot = FALSE: prcomp result.

Examples

data(rasterList_lanRGB)

pop1 <- c('BC0077','BC0071')
pop2 <- c('BC0050','BC0049','BC0004')
popList <- list(pop1, pop2)
colList <- c("red", "blue")

pcaOut <- patPCA(rasterList_lanRGB, popList, colList, plot = TRUE)

patternize documentation built on Aug. 22, 2023, 5:07 p.m.