{"action":"created","issue":{"url":"https://api.github.com/repos/leifeld/btergm/issues/10","repository_url":"https://api.github.com/repos/leifeld/btergm","labels_url":"https://api.github.com/repos/leifeld/btergm/issues/10/labels{/name}","comments_url":"https://api.github.com/repos/leifeld/btergm/issues/10/comments","events_url":"https://api.github.com/repos/leifeld/btergm/issues/10/events","html_url":"https://github.com/leifeld/btergm/issues/10","id":381874478,"node_id":"MDU6SXNzdWUzODE4NzQ0Nzg=","number":10,"title":"Problems with too little variation","user":{"login":"reuning","id":6343832,"node_id":"MDQ6VXNlcjYzNDM4MzI=","avatar_url":"https://avatars1.githubusercontent.com/u/6343832?v=4","gravatar_id":"","url":"https://api.github.com/users/reuning","html_url":"https://github.com/reuning","followers_url":"https://api.github.com/users/reuning/followers","following_url":"https://api.github.com/users/reuning/following{/other_user}","gists_url":"https://api.github.com/users/reuning/gists{/gist_id}","starred_url":"https://api.github.com/users/reuning/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/reuning/subscriptions","organizations_url":"https://api.github.com/users/reuning/orgs","repos_url":"https://api.github.com/users/reuning/repos","events_url":"https://api.github.com/users/reuning/events{/privacy}","received_events_url":"https://api.github.com/users/reuning/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-11-17T15:48:21Z","updated_at":"2018-11-17T16:50:00Z","closed_at":null,"author_association":"NONE","body":"I'm trying to run a model with a stepwise time covariate. I'm curious about how a parameter changes before and after a certain time period. I estimated the model fine but I get the combined warning/error when trying to call it: \r\n```\r\nEstimates and 95% confidence intervals:\r\nError in boot::boot.ci(object@boot, conf = level, type = type, index = x) : \r\n  't' must of length 500\r\nIn addition: Warning message:\r\nIn .local(object, parm, level, ...) :\r\n  Too little variation in the model. 174 replications (34.8%) are dropped from CI estimation\r\n```\r\n\r\nI'm not sure if this is a bug or a feature. In that, I don't know how concerned I should be with there being an issue with too little variation, as in, does this reflect a problematic model or just a problem in sampling. "},"comment":{"url":"https://api.github.com/repos/leifeld/btergm/issues/comments/439630654","html_url":"https://github.com/leifeld/btergm/issues/10#issuecomment-439630654","issue_url":"https://api.github.com/repos/leifeld/btergm/issues/10","id":439630654,"node_id":"MDEyOklzc3VlQ29tbWVudDQzOTYzMDY1NA==","user":{"login":"leifeld","id":5451293,"node_id":"MDQ6VXNlcjU0NTEyOTM=","avatar_url":"https://avatars3.githubusercontent.com/u/5451293?v=4","gravatar_id":"","url":"https://api.github.com/users/leifeld","html_url":"https://github.com/leifeld","followers_url":"https://api.github.com/users/leifeld/followers","following_url":"https://api.github.com/users/leifeld/following{/other_user}","gists_url":"https://api.github.com/users/leifeld/gists{/gist_id}","starred_url":"https://api.github.com/users/leifeld/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/leifeld/subscriptions","organizations_url":"https://api.github.com/users/leifeld/orgs","repos_url":"https://api.github.com/users/leifeld/repos","events_url":"https://api.github.com/users/leifeld/events{/privacy}","received_events_url":"https://api.github.com/users/leifeld/received_events","type":"User","site_admin":false},"created_at":"2018-11-17T16:50:00Z","updated_at":"2018-11-17T16:50:00Z","author_association":"OWNER","body":"That may actually explain the error message because there may be a collinearity between the first and the second term. You only need to include the second term with `minimum = 6`. This means a list of matrices is included where the first five time points are 0 and the remaining ones 1, and an interaction effect between that and your `absdiff` term is included. Additionally, you also need to include the absdiff list manually as a main effect using an `edgecov` model term for correct interpretability."}}
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