BIOL2022 L20 Fishing expiditions: PCA and CCA

PCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].

CCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].

The term representing the amount of original variance explained by a new derived variable is:

The term representing weights showing how much each original variable contributes to each newly derived variable is:

The following information about PCA is True or False: First principal component – the vector on which the most data variation can be projected. Second principal component – vector perpendicular to the first, chosen so it contains as much of the remaining variation as possible.

The following information about PCA is True or False: First principal component – the vector on which the most data variation can be projected. Second principal component – Second best possible vector, chosen to account as much variation as possible, but less good fit than the First.

When to use PCA: You have a set of ‘p’ [blank_start]____________[blank_end] variables. You want to repackage their variance into ‘m’ components. You want ‘m’ to be [blank_start]____[blank_end] ‘p’. Each component could/should/might explain different things.

Covariance or Correlation Matrix. If units of x and y are different, use a [blank_start]____________[blank_end] matrix (as it standardises the units). If units of x and y are the same (e.g. temperature) or with similar orders of magnitude, use a [blank_start]____________[blank_end] matrix (although you may need to standardise units).

In regards to the scree plot, a component with eigenvalue < 1 captured less than what?

Rotations, orthogonal vs oblique. Varimax is an example of [blank_start]____________[blank_end], meaning it [blank_start]________[blank_end] allow for factors to correlate.