{"action":"created","issue":{"url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/66","repository_url":"https://api.github.com/repos/bsmurphy/PyKrige","labels_url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/66/labels{/name}","comments_url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/66/comments","events_url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/66/events","html_url":"https://github.com/bsmurphy/PyKrige/issues/66","id":239418432,"number":66,"title":"Variance prediction in regression kriging","user":{"login":"alexisrosuel","id":19517275,"avatar_url":"https://avatars2.githubusercontent.com/u/19517275?v=3","gravatar_id":"","url":"https://api.github.com/users/alexisrosuel","html_url":"https://github.com/alexisrosuel","followers_url":"https://api.github.com/users/alexisrosuel/followers","following_url":"https://api.github.com/users/alexisrosuel/following{/other_user}","gists_url":"https://api.github.com/users/alexisrosuel/gists{/gist_id}","starred_url":"https://api.github.com/users/alexisrosuel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alexisrosuel/subscriptions","organizations_url":"https://api.github.com/users/alexisrosuel/orgs","repos_url":"https://api.github.com/users/alexisrosuel/repos","events_url":"https://api.github.com/users/alexisrosuel/events{/privacy}","received_events_url":"https://api.github.com/users/alexisrosuel/received_events","type":"User","site_admin":false},"labels":[{"id":134120573,"url":"https://api.github.com/repos/bsmurphy/PyKrige/labels/enhancement","name":"enhancement","color":"84b6eb","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2017-06-29T09:30:02Z","updated_at":"2017-07-10T17:09:35Z","closed_at":null,"body":"That would be useful to return not only the point estimate in regression kriging, but also the variance ? So that the user can then compute a confidence interval of each prediction...\r\n\r\nI think it would not be too difficult : \r\n- regression model based on scikit doesn't return this. So let's not consider it for the moment\r\n- the kriging model **does** return it ! So we could just use it directly in rk\r\n\r\nIt's not perfect, since we will not catch all the variance, but it's better than nothing...\r\n\r\nWhat do you think ?"},"comment":{"url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/comments/314171735","html_url":"https://github.com/bsmurphy/PyKrige/issues/66#issuecomment-314171735","issue_url":"https://api.github.com/repos/bsmurphy/PyKrige/issues/66","id":314171735,"user":{"login":"rth","id":630936,"avatar_url":"https://avatars3.githubusercontent.com/u/630936?v=3","gravatar_id":"","url":"https://api.github.com/users/rth","html_url":"https://github.com/rth","followers_url":"https://api.github.com/users/rth/followers","following_url":"https://api.github.com/users/rth/following{/other_user}","gists_url":"https://api.github.com/users/rth/gists{/gist_id}","starred_url":"https://api.github.com/users/rth/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rth/subscriptions","organizations_url":"https://api.github.com/users/rth/orgs","repos_url":"https://api.github.com/users/rth/repos","events_url":"https://api.github.com/users/rth/events{/privacy}","received_events_url":"https://api.github.com/users/rth/received_events","type":"User","site_admin":false},"created_at":"2017-07-10T17:09:35Z","updated_at":"2017-07-10T17:09:35Z","body":"> I think of the gaussian process in sklearn is like ...\r\n\r\n@basaks Looks like a documentation PR waiting to happen :) Maybe we could do a  `doc/faq.md` (or something similar) ? Some tips on how to choose the variogram model (and between the kriging models)  would be quite useful too IMO .."}}
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