PLSDA {structToolbox} | R Documentation |
PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable and applies a threshold to the predicted values in order to predict group membership for new samples.
PLSDA(number_components = 2, factor_name, ...)
number_components |
(numeric, integer) The number of PLS components. The default is |
factor_name |
(character) The name of a sample-meta column to use. |
... |
Additional slots and values passed to |
This object makes use of functionality from the following packages:
pls
A PLSDA
object with the following output
slots:
scores | (data.frame) |
loadings | (data.frame) |
yhat | (data.frame) |
design_matrix | (data.frame) |
y | (data.frame) |
reg_coeff | (data.frame) |
probability | (data.frame) |
vip | (data.frame) |
pls_model | (list) |
pred | (data.frame) |
threshold | (numeric) |
Liland K, Mevik B, Wehrens R (2021). pls: Partial Least Squares and Principal Component Regression. R package version 2.8-0, https://CRAN.R-project.org/package=pls.
Perez NF, Ferre J, Boque R (2009). “Calculation of the reliability of classification in discriminant partial least-squares binary classification.” Chemometrics and Intelligent Laboratory Systems, 95(2), 122-128.
Barker M, Rayens W (2003). “Partial least squares for discrimination.” Journal of Chemometrics, 17(3), 166-173.
M = PLSDA('number_components'=2,factor_name='Species')