screeplot {PCAtools} | R Documentation

## Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.

### Description

Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.

### Usage

    screeplot(
      pcaobj,
      components = getComponents(pcaobj),
      xlim = NULL,
      ylim = c(0, 100),
      xlab = "Principal component",
      xlabAngle = 90,
      xlabhjust = 0.5,
      xlabvjust = 0.5,
      ylab = "Explained variation (%)",
      ylabAngle = 0,
      ylabhjust = 0.5,
      ylabvjust = 0.5,
      axisLabSize = 16,
      title = "SCREE plot",
      subtitle = "",
      caption = "",
      titleLabSize = 16,
      subtitleLabSize = 12,
      captionLabSize = 12,
      colBar = "dodgerblue",
      drawCumulativeSumLine = TRUE,
      colCumulativeSumLine = "red2",
      sizeCumulativeSumLine = 1.5,
      drawCumulativeSumPoints = TRUE,
      colCumulativeSumPoints = "red2",
      sizeCumulativeSumPoints = 2,
      hline = NULL,
      hlineType = "longdash",
      hlineCol = "black",
      hlineWidth = 0.4,
      vline = NULL,
      vlineType = "longdash",
      vlineCol = "black",
      vlineWidth = 0.4,
      gridlines.major = TRUE,
      gridlines.minor = TRUE,
      borderWidth = 0.8,
      borderColour = "black",
      returnPlot = TRUE
    )

### Arguments

`pcaobj` | Object of class 'pca' created by pca().
`components` | The principal components to be included in the plot.
`xlim` | Limits of the x-axis.
`ylim` | Limits of the y-axis.
`xlab` | Label for x-axis.
`xlabAngle` | Rotation angle of x-axis labels.
`xlabhjust` | Horizontal adjustment of x-axis labels.
`xlabvjust` | Vertical adjustment of x-axis labels.
`ylab` | Label for y-axis.
`ylabAngle` | Rotation angle of y-axis labels.
`ylabhjust` | Horizontal adjustment of y-axis labels.
`ylabvjust` | Vertical adjustment of y-axis labels.
`axisLabSize` | Size of x- and y-axis labels.
`title` | Plot title.
`subtitle` | Plot subtitle.
`caption` | Plot caption.
`titleLabSize` | Size of plot title.
`subtitleLabSize` | Size of plot subtitle.
`captionLabSize` | Size of plot caption.
`colBar` | Colour of the vertical bars.
`drawCumulativeSumLine` | Logical, indicating whether or not to overlay plot with a cumulative explained variance line.
`colCumulativeSumLine` | Colour of cumulative explained variance line.
`sizeCumulativeSumLine` | Size of cumulative explained variance line.
`drawCumulativeSumPoints` | Logical, indicating whether or not to draw the cumulative explained variance points.
`colCumulativeSumPoints` | Colour of cumulative explained variance points.
`sizeCumulativeSumPoints` | Size of cumulative explained variance points.
`hline` | Draw one or more horizontal lines passing through this/these values on y-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
`hlineType` | Line type for hline ('blank', 'solid', 'dashed', 'dotted', 'dotdash', 'longdash', 'twodash').
`hlineCol` | Colour of hline.
`hlineWidth` | Width of hline.
`vline` | Draw one or more vertical lines passing through this/these values on x-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
`vlineType` | Line type for vline ('blank', 'solid', 'dashed', 'dotted', 'dotdash', 'longdash', 'twodash').
`vlineCol` | Colour of vline.
`vlineWidth` | Width of vline.
`gridlines.major` | Logical, indicating whether or not to draw major gridlines.
`gridlines.minor` | Logical, indicating whether or not to draw minor gridlines.
`borderWidth` | Width of the border on the x and y axes.
`borderColour` | Colour of the border on the x and y axes.
`returnPlot` | Logical, indicating whether or not to return the plot object.

### Details

Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.

### Value

A `ggplot2` object.

### Author(s)

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>

### Examples

      options(scipen=10)
      options(digits=6)

      col <- 20
      row <- 20000
      mat1 <- matrix(
        rexp(col*row, rate = 0.1),
        ncol = col)
      rownames(mat1) <- paste0('gene', 1:nrow(mat1))
      colnames(mat1) <- paste0('sample', 1:ncol(mat1))

      mat2 <- matrix(
        rexp(col*row, rate = 0.1),
        ncol = col)
      rownames(mat2) <- paste0('gene', 1:nrow(mat2))
      colnames(mat2) <- paste0('sample', (ncol(mat1)+1):(ncol(mat1)+ncol(mat2)))

      mat <- cbind(mat1, mat2)

      metadata <- data.frame(row.names = colnames(mat))
      metadata$Group <- rep(NA, ncol(mat))
      metadata$Group[seq(1,40,2)] <- 'A'
      metadata$Group[seq(2,40,2)] <- 'B'
      metadata$CRP <- sample.int(100, size=ncol(mat), replace=TRUE)
      metadata$ESR <- sample.int(100, size=ncol(mat), replace=TRUE)

      p <- pca(mat, metadata = metadata, removeVar = 0.1)

      screeplot(p)

      screeplot(p, hline = 80)

[Package PCAtools version 2.6.0 Index]