screeplot {PCAtools} | R Documentation |
Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.
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 )
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. |
Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.
A ggplot2
object.
Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
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)