{"action":"created","issue":{"url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/138","repository_url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland","labels_url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/138/labels{/name}","comments_url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/138/comments","events_url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/138/events","html_url":"https://github.com/IALSA/IALSA-2015-Portland/issues/138","id":168203280,"number":138,"title":"2016-07-28-Thursday-Study-specific-tables","user":{"login":"andkov","id":2608073,"avatar_url":"https://avatars.githubusercontent.com/u/2608073?v=3","gravatar_id":"","url":"https://api.github.com/users/andkov","html_url":"https://github.com/andkov","followers_url":"https://api.github.com/users/andkov/followers","following_url":"https://api.github.com/users/andkov/following{/other_user}","gists_url":"https://api.github.com/users/andkov/gists{/gist_id}","starred_url":"https://api.github.com/users/andkov/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/andkov/subscriptions","organizations_url":"https://api.github.com/users/andkov/orgs","repos_url":"https://api.github.com/users/andkov/repos","events_url":"https://api.github.com/users/andkov/events{/privacy}","received_events_url":"https://api.github.com/users/andkov/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2016-07-28T21:38:59Z","updated_at":"2016-08-16T16:09:09Z","closed_at":null,"body":""},"comment":{"url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/comments/240151591","html_url":"https://github.com/IALSA/IALSA-2015-Portland/issues/138#issuecomment-240151591","issue_url":"https://api.github.com/repos/IALSA/IALSA-2015-Portland/issues/138","id":240151591,"user":{"login":"andkov","id":2608073,"avatar_url":"https://avatars.githubusercontent.com/u/2608073?v=3","gravatar_id":"","url":"https://api.github.com/users/andkov","html_url":"https://github.com/andkov","followers_url":"https://api.github.com/users/andkov/followers","following_url":"https://api.github.com/users/andkov/following{/other_user}","gists_url":"https://api.github.com/users/andkov/gists{/gist_id}","starred_url":"https://api.github.com/users/andkov/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/andkov/subscriptions","organizations_url":"https://api.github.com/users/andkov/orgs","repos_url":"https://api.github.com/users/andkov/repos","events_url":"https://api.github.com/users/andkov/events{/privacy}","received_events_url":"https://api.github.com/users/andkov/received_events","type":"User","site_admin":false},"created_at":"2016-08-16T16:09:08Z","updated_at":"2016-08-16T16:09:08Z","body":"Enhanced model look-up function  is now available along with the environment script ( ). The link and description of use has been added to README\r\n\r\n```r\r\n> single_model <- pull_one_model(\r\n+   d           = catalog,\r\n+   study_name_ = \"map\",\r\n+   subgroup_   = \"female\",\r\n+   process_a_  = \"grip\",\r\n+   process_b_  = \"symbol\",\r\n+   model_type_ = \"aehplus\",\r\n+   pretty_     = T\r\n+ )\r\n# A tibble: 41 x 4\r\n           type process                       label                dense\r\n          <chr>   <chr>                       <chr>                <chr>\r\n1  Fixed Effect       a Intercept                    43.68(1.26),  p<.01\r\n2  Fixed Effect       a Slope                        -0.41(0.32),  p=.20\r\n3  Fixed Effect       a Intercept * age              -0.59(0.05),  p<.01\r\n4  Fixed Effect       a Intercept * education         0.38(0.13),  p<.01\r\n5  Fixed Effect       a Intercept * height           39.45(4.24),  p<.01\r\n6  Fixed Effect       a Intercept * smoking           0.37(0.67),  p=.58\r\n7  Fixed Effect       a Intercept * cardio            0.56(1.40),  p=.69\r\n8  Fixed Effect       a Intercept * diabetes         -0.04(1.21),  p=.97\r\n9  Fixed Effect       a Slope * age                  -0.02(0.01),  p=.06\r\n10 Fixed Effect       a Slope * education            -0.07(0.03),  p=.04\r\n11 Fixed Effect       a Slope * height               -1.13(1.18),  p=.34\r\n12 Fixed Effect       a Slope * smoking              -0.01(0.17),  p=.97\r\n13 Fixed Effect       a Slope * cardio                0.13(0.29),  p=.67\r\n14 Fixed Effect       a Slope * diabetes             -0.29(0.28),  p=.30\r\n15 Fixed Effect       b Intercept                    35.43(1.05),  p<.01\r\n16 Fixed Effect       b Slope                         0.52(0.22),  p=.02\r\n17 Fixed Effect       b Intercept * age              -0.37(0.04),  p<.01\r\n18 Fixed Effect       b Intercept * education         1.02(0.10),  p<.01\r\n19 Fixed Effect       b Intercept * height           14.69(3.92),  p<.01\r\n20 Fixed Effect       b Intercept * smoking          -0.77(0.56),  p=.17\r\n21 Fixed Effect       b Intercept * cardio           -1.33(1.19),  p=.26\r\n22 Fixed Effect       b Intercept * diabetes         -3.06(1.01),  p<.01\r\n23 Fixed Effect       b Slope * age                  -0.06(0.01),  p<.01\r\n24 Fixed Effect       b Slope * education            -0.05(0.02),  p=.05\r\n25 Fixed Effect       b Slope * height                2.15(1.08),  p=.05\r\n26 Fixed Effect       b Slope * smoking              -0.17(0.14),  p=.23\r\n27 Fixed Effect       b Slope * cardio                0.14(0.26),  p=.59\r\n28 Fixed Effect       b Slope * diabetes             -0.27(0.27),  p=.32\r\n29     Variance      aa Intercept                    95.67(6.03),  p<.01\r\n30     Variance      aa Slope                         0.79(0.33),  p=.02\r\n31     Variance       a Residual                     19.96(1.10),  p<.01\r\n32     Variance      bb Intercept                    65.55(4.59),  p<.01\r\n33     Variance      bb Slope                         0.98(0.31),  p<.01\r\n34     Variance       b Residual                     23.68(1.34),  p<.01\r\n35   Covariance      ab Intercept(a) - Intercept(b)   6.24(3.52),  p=.08\r\n36   Covariance      ab Slope(a) - Slope(b)          -0.06(0.21),  p=.79\r\n37   Covariance      ab Intercept(a) - Slope(b)       2.09(1.02),  p=.04\r\n38   Covariance      ab Slope(a) - Intercept(b)       0.67(0.90),  p=.46\r\n39         <NA>    <NA>                           N                1,010\r\n40         <NA>    <NA>                         AIC               53,411\r\n41         <NA>    <NA>                         BIC               53,745\r\n```"}}
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