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收起看板(549)
1F推: university edition已經於今年4/30停止服務，改成SAS On07/10 18:10
2F→: Demand for Academics.07/10 18:10
3F推: 介面跟university edition一樣，但完全線上，不用裝軟07/10 18:13
4F→: 體也不用執行VMWare. 不過介面跟正版不同，得稍微適應一07/10 18:13
5F→: 下。07/10 18:13
8F→: 先不論共線性的問題，基本上拿迴歸和GLM來分析時間序列03/18 03:27
9F→: 資料，最後的殘差檢定幾乎都不會過，因為資料極大可能早03/18 03:28
10F→: 已經違反了獨立性假設。你應該要改用mixed model或者是03/18 03:28
11F→: nonlinear model03/18 03:29
9F推: baseball reference01/01 15:47
4F→: 數據裡面沒有0，如果有很多0的話我早就直接用ZIP了10/17 13:35
5F→: 樣本數也無法增加，因為樣本是美國50個州10/17 13:36
8F→: OK，我來研究看看robust和mixture poisson model怎麼做10/18 06:28
9F→: 貌似這兩個模型都是台灣人發明的樣子，希望能找到可以10/18 06:29
10F→: 跑的R Package啊10/18 06:29
12F→: 我用bootstrap做過，結果一樣10/25 12:36
3F推: 所有統計軟體預設都是用complete case analysis, 所以每07/06 06:18
4F→: 個樣本只要有一個變數有missing, 整個樣本都會被砍掉。07/06 06:18
5F→: 以你的例子來看，missing rate就是80%起跳，當然可能會07/06 06:18
6F→: 大幅降低power, 但還是要看總共放了多少變數進模型。07/06 06:18
28F推: SAS被淘汰只是時間的問題，我笑了。07/06 06:14
3F推: 不同的smoothing function有不同的basis function. 你可06/13 03:34
4F→: 以不用太執著basis function長得如何，只要整條smoothin06/13 03:34
5F→: g function在GAM裡面配飾的好即可。06/13 03:34
6F推: 出現2的情況可考慮generalized linear mixed model(但06/13 03:31
7F→: 還是以Poisson model當基底）。3的情況仍舊可用Poisson06/13 03:31
8F→: model.06/13 03:31
1F推: 看情況，擔心的話做個cross-validation看看。05/19 12:37
4F推: 你的命題當然可以跑統計，但這不是基礎生物統計學可以05/19 12:35
5F→: 簡單處理的。為什麼我知道，因為你的命題正是我在研究05/19 12:35
6F→: 的題目，所以我知道沒那麼簡單。05/19 12:35