Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
library(dplyr)
web %>%
action %>%
filter(message == 'Welcome')
Eigenvalues and eigenvectors are used in several concepts of statistical inference and modelling. It can be useful for dimension reduction, decomposition of variance-covariance matrices, so on. For this reason, we provide basic details about eigenvectors and eigenvalues and their close relationship with linear transformations.
test Linear transformations and change of basis are widely used in statistics, for this reason I briefly describe the definition of these concepts and how they are related. Linear Transformation Letting \(V\) and \(W\) be vector spaces, a function \(f: V \rightarrow W\) is a linear transformation if the additivity and scalar multiplication properties are hold for any two vectors \(\mathbf{u}, \mathbf{v} \in V\) and a constant \(c\): \[f(\mathbf{u}+\mathbf{v}) = f(\mathbf{u}) + f(\mathbf{v})\] \[f(c\mathbf{v}) = cf(\mathbf{v}).
linear function change of basis vector spaces