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                ## 12.6 貝葉斯系數 ![](https://img.kancloud.cn/0e/6a/0e6aea3541f9a7c8764b1d14572d718b_327x44.jpg) 我們在關于貝葉斯統計的前一章中討論了貝葉斯因子——你可能記得它代表了兩種假設下數據的可能性比:貝葉斯因子在某種程度上類似于 p 值和影響大小,也就是說它們的解釋是某種意義上的。t 主觀。他們的解釋有各種各樣的指導方針——這是來自 Kass 和 Raftery(1995)的一個: | 高爐 | 解釋 | | --- | --- | | 1 到 3 | 不值得一提 | | 3 至 20 | 積極的 | | 20 至 150 | 堅強的 | | 150 及以上 | 非常強壯 | 我們可以使用 BayesFactor 包中的`contingencyTableBF()`函數計算警察搜索數據的 Bayes 因子: ```r # compute Bayes factor # using independent multinomial sampling plan in which row totals (driver race) # are fixed bf <- contingencyTableBF(as.matrix(summaryDf2wayTable), sampleType = "indepMulti", fixedMargin = "cols" ) bf ``` ```r ## Bayes factor analysis ## -------------- ## [1] Non-indep. (a=1) : 1.8e+142 ±0% ## ## Against denominator: ## Null, independence, a = 1 ## --- ## Bayes factor type: BFcontingencyTable, independent multinomial ``` 這表明,在這個數據集中,支持駕駛員種族和警察搜索之間關系的證據非常有力。
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