{"action":"opened","issue":{"url":"https://api.github.com/repos/florianhartig/DHARMa/issues/85","repository_url":"https://api.github.com/repos/florianhartig/DHARMa","labels_url":"https://api.github.com/repos/florianhartig/DHARMa/issues/85/labels{/name}","comments_url":"https://api.github.com/repos/florianhartig/DHARMa/issues/85/comments","events_url":"https://api.github.com/repos/florianhartig/DHARMa/issues/85/events","html_url":"https://github.com/florianhartig/DHARMa/issues/85","id":361689690,"node_id":"MDU6SXNzdWUzNjE2ODk2OTA=","number":85,"title":"Beta Mixed Effect Model - Interpretation Residual Plots and Tests","user":{"login":"MelanieDou","id":42808540,"node_id":"MDQ6VXNlcjQyODA4NTQw","avatar_url":"https://avatars2.githubusercontent.com/u/42808540?v=4","gravatar_id":"","url":"https://api.github.com/users/MelanieDou","html_url":"https://github.com/MelanieDou","followers_url":"https://api.github.com/users/MelanieDou/followers","following_url":"https://api.github.com/users/MelanieDou/following{/other_user}","gists_url":"https://api.github.com/users/MelanieDou/gists{/gist_id}","starred_url":"https://api.github.com/users/MelanieDou/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/MelanieDou/subscriptions","organizations_url":"https://api.github.com/users/MelanieDou/orgs","repos_url":"https://api.github.com/users/MelanieDou/repos","events_url":"https://api.github.com/users/MelanieDou/events{/privacy}","received_events_url":"https://api.github.com/users/MelanieDou/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-09-19T10:50:22Z","updated_at":"2018-09-19T10:50:22Z","closed_at":null,"author_association":"NONE","body":"Good afternoon,\r\n\r\nI fitted a beta mixed effect model to a dataset of 10235 observations and then used your very useful DHARMa package to validate it. I got the following outcome\r\n```\r\nform_final <- fraction ~ logMigrantMW + logKow.calc. + ETOH_Equivalency + pH_numeric +  \r\n    Crystallinity... + Hansen_Solubility_total_MPa + temp_K +  \r\n    logTime.hrs + logKow.calc.:ETOH_Equivalency + temp_K:logTime.hrs +  \r\n    (1 | Migrant_Name) + (1 | PolymerCAS.DisplayName) + (1 |  \r\n    Food.NewName) + (1 | CitationID) + (1 | AnalMethod)\r\nfinal_model <- glmmTMB(formula = form_final,data=database,family=beta_family(link=\"logit\"),REML=T)\r\nsimulationOutput <- simulateResiduals(fittedModel = final_model, n = 250)\r\nplot(simulationOutput)\r\n\r\ntestUniformity(simulationOutput)\r\n\tOne-sample Kolmogorov-Smirnov test\r\ndata:  simulationOutput$scaledResiduals\r\nD = 0.024578, p-value = 8.523e-06\r\nalternative hypothesis: two-sided\r\n\r\ntestDispersion(simulationOutput)\r\nDHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated\r\n\r\ndata:  simulationOutput\r\nratioObsSim = 0.49398, p-value < 2.2e-16\r\nalternative hypothesis: two.sided\r\n```\r\n![plot_simulatedresiduals](https://user-images.githubusercontent.com/42808540/45748860-2e764d80-bc0a-11e8-926f-6599f57ba2d3.png)\r\n\r\nNow my question: Am I really having a problem with overdispersion and non-uniformity? Or is it related to the comment on the vignette to the DHARMa package: \"Hence, the p-value alone is not a good indicator of the extent to which your residuals deviate from assumptions. Specifically, if you have a lot of data points, residual diagnostics will nearly inevitably become significant, because having a perfectly fitting model is very unlikely. That, however, doesn't neccessarily mean that you need to change your model\"?\r\n\r\nI am looking forward to reading what you think,\r\nThank you in advance very much,\r\nMélanie"}}
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