Loading plots on a 2D scatter plot

Draws a 2D loading plot of each effect matrix provided in lmpPcaEffects outputs. As a wrapper of the plotScatter function, it allows the visualization of effect loading matrices for two components at a time with all options available in plotScatter.

Usage

lmpLoading2dPlot(
  resLmpPcaEffects,
  effectNames = NULL,
  axes = c(1, 2),
  addRownames = FALSE,
  pl_n = 10,
  metadata = NULL,
  drawOrigin = TRUE,
  ...
)

Arguments

resLmpPcaEffects

A list corresponding to the output value of lmpPcaEffects.

effectNames

Names of the effects to be plotted. If NULL, all the effects are plotted.

axes

A numerical vector with the 2 Principal Components axes to be drawn.

addRownames

Boolean indicating if the labels should be plotted. By default, uses the column names of the outcome matrix but it can be manually specified with the points_labs argument from plotScatter.

pl_n

The number of labels that should be plotted, based on the distance measure \(d\) (see Details).

metadata

A nxk "free encoded" data.frame corresponding to design in plotScatter.

drawOrigin

if TRUE, draws horizontal and vertical intercepts at (0,0) based on the plotScatter function.

...

Additional arguments to be passed to plotScatter.

Value

A list of loading plots (ggplot).

Details

lmpLoading2dPlot is a wrapper of plotScatter. See ?plotScatter for more information on the additional arguments.

The distance measure \(d\) that is used to rank the variables is based on the following formula: $$d = \sqrt(P_{ab}^2*\lambda_{ab}^2)$$ where \(a\) and \(b\) are two selected Principal Components, \(P_{ab}\) represents their loadings and \(\lambda_{ab}\) their singular values.

Examples

data("UCH")
resLmpModelMatrix <- lmpModelMatrix(UCH)
resLmpEffectMatrices <- lmpEffectMatrices(resLmpModelMatrix)
resASCA <- lmpPcaEffects(resLmpEffectMatrices)

lmpLoading2dPlot(resASCA, effectNames = "Hippurate")


# adding color, shape and labels to points
id_hip <- c(seq(126, 156), seq(362, 375))
peaks <- rep("other", ncol(UCH$outcomes))
peaks[id_hip] <- "hip"
metadata <- data.frame(peaks)

lmpLoading2dPlot(resASCA,
  effectNames = "Hippurate",
  metadata = metadata, addRownames = TRUE, color = "peaks",
  shape = "peaks"
)
#> Warning: at least one variable used as color or shape is not a factor and will be converted as such


# changing max.overlaps of ggrepel
options(ggrepel.max.overlaps = 30)
lmpLoading2dPlot(resASCA,
  effectNames = "Hippurate",
  metadata = metadata, addRownames = TRUE, color = "peaks",
  shape = "peaks", pl_n = 20
)
#> Warning: at least one variable used as color or shape is not a factor and will be converted as such
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps