PLSR {structToolbox} | R Documentation |
PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable.
PLSR(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 PLSR
object with the following output
slots:
scores | (data.frame) |
loadings | (data.frame) |
y | (data.frame) |
yhat | (data.frame) |
reg_coeff | (data.frame) |
vip | (data.frame) |
pls_model | (list) |
pred | (data.frame) |
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.
M = PLSR(factor_name='run_order')