Creates heatmap and barmap visualizations of PCA loadings for easy interpretation of component structure. Heatmaps show all loadings as a color-coded matrix, while barmaps display loading patterns as bar charts across components.
Usage
pcaloadingheatmap(
data,
vars,
ncomp = 4,
cutoff = 0.5,
center = TRUE,
scale = TRUE,
textvalues = TRUE,
starvalues = FALSE,
textsize = 2,
plotlegend = TRUE,
plotcutoff = TRUE,
gradientcolor = TRUE,
colorlow = "steelblue1",
colormid = "white",
colorhigh = "firebrick1",
plotwidth = 600,
plotheight = 450
)Arguments
- data
The data as a data frame.
- vars
Continuous variables to include in Principal Component Analysis. Select at least 3 numeric variables.
- ncomp
Number of principal components to display (1 to 10).
- cutoff
Threshold for marking loadings as significant (0 to 1). Loadings with |loading| >= cutoff will be marked with stars if star_values is enabled.
- center
Center variables before PCA.
- scale
Scale variables before PCA.
- textvalues
Display numeric loading values in heatmap/barmap cells.
- starvalues
Display stars (*) for loadings >= cutoff threshold. Only relevant if textvalues is FALSE.
- textsize
Font size for text in plots.
- plotlegend
Display legend in barmap plot.
- plotcutoff
Display horizontal lines at ±cutoff threshold in barmap.
- gradientcolor
Use gradient colors proportional to loading values. If FALSE, uses binary colors for positive/negative.
- colorlow
Color for negative loadings.
- colormid
Midpoint color for zero loading.
- colorhigh
Color for positive loadings.
- plotwidth
Width of plots in pixels.
- plotheight
Height of plots in pixels.
Value
A results object containing:
results$todo | a html | ||||
results$heatmap | Heatmap visualization of PCA loadings across components | ||||
results$barmap | Barmap visualization of PCA loadings showing magnitude and direction |
Details
Both visualizations support optional significance indicators (stars) for loadings above a specified cutoff threshold. These publication-ready plots help identify which variables contribute most strongly to each component.