Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

(doc) explaining methods to break ties for spatial markov #217

Merged
merged 1 commit into from
Jun 24, 2024
Merged

(doc) explaining methods to break ties for spatial markov #217

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion giddy/markov.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -740,7 +740,14 @@ class Spatial_Markov:
(4) `Spatial_Markov` also accepts discrete time series and calculates
categorical spatial lags on which several transition probability matrices
are conditioned.
are conditioned. The categorical spatial lag is defined as the most common
categories of neighboring observations, weighted by their weight strengths.
Please note that occasionally there are ties for the "most common categories"
in the calculation of the categorical spatial lag. To break the tie, the
category of the focal observation is included with its neighbors. If this
does not resolve the tie, a winner is chosen randomly.
Let's still use the US state income time series to demonstrate. We first
discretize them into categories and then pass them to `Spatial_Markov`.
Expand Down
Loading