Visualising PCA or OPLS loadings for NMR data

Visualising PCA or OPLS loadings for NMR data

plotload(
  mod,
  shift = c(0, 10),
  pc = 1,
  type = "Backscaled",
  title = NULL,
  r_scale = FALSE
)

Arguments

mod	PCA or OPLS model generated via metabom8 package functions.
shift	ppm region to visualise.
pc	index of principal component to visualise, set to 1 if input model is OPLS
type	Type of loadings visualisation, either 'Statistical reconstruction' or 'Backscaled' (see Details).
title	Plot title.
r_scale	logical, adjust limits of color gradient to 0 and 1 (only applies for type stat reconstruction)

Value

ggplot2 plot object

Details

OPLS loadinsg visualisatoin for NMR data: If type='Statistical reconstruction' the function calculates the covariance (y axis) and Pearson's correlation (colouring) of the predictive OPLS scores with each X variable (x axis is ppm variable). If type='Backscaled' the OPLS loadings are backscaled with X feature standard deviations. Results are plotted over ppm, coloured according to OPLS model weights. Often, the latter method visualises model importance more robust due to the presence of false positive correlations. PCA: Function always calculates the statistical recostruction.

NA

References

Cloarec, O., et al. (2005). Evaluation of the Orthogonal Projection on Latent Structure Model Limitations Caused by Chemical Shift Variability and Improved Visualization of Biomarker Changes in 1H NMR Spectroscopic Metabonomic Studies. Analytical Chemistry 77.2, 517-26.

See also

plotload_cat

Other NMR: alignSegment(), bline(), excise1d(), get.idx(), lw(), matspec(), noise.est(), normErectic(), ppick(), read1d_raw(), read1d(), specload(), spec(), stocsy1d_metabom8-class, stocsy(), storm()

Author

Torben Kimhofer tkimhofer@gmail.com

torben.kimhofer@murdoch.edu.au

Examples

data(covid)
model=pca(X)
#> Too many number of components, setting pc to 2
plotload(model, pc=1)