You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Let's say we have two sets of nodes, A and B, and let R be the |A| x |B| matrix that gives relatedness between A and B.
Given a vector w indexed by B, genetic_relatedness_vector gives us Rw, a A-vector.
Currently, using the nodes argument, we can set A to be anything.
By setting appropriate entries of w to zero we can let B be a subset of the vectors, but we don't have the ability to let B include non-sample nodes.
However, if we'd like to do PCA on non-sample nodes, we need to be able to do this. I think this is straightforward if centre=False, which is already a requirement for passing in the nodes argument; the daunting thing is figuring out what to call the new argument we need.
Note that we do need to be able to have A and B different, for other applications.
The text was updated successfully, but these errors were encountered:
Let's say we have two sets of nodes,
A
andB
, and letR
be the|A| x |B|
matrix that gives relatedness betweenA
andB
.Given a vector
w
indexed byB
,genetic_relatedness_vector
gives usRw
, aA
-vector.Currently, using the
nodes
argument, we can setA
to be anything.By setting appropriate entries of
w
to zero we can letB
be a subset of the vectors, but we don't have the ability to letB
include non-sample nodes.However, if we'd like to do PCA on non-sample nodes, we need to be able to do this. I think this is straightforward if
centre=False
, which is already a requirement for passing in thenodes
argument; the daunting thing is figuring out what to call the new argument we need.Note that we do need to be able to have
A
andB
different, for other applications.The text was updated successfully, but these errors were encountered: