如何在 R 中執行 Spearman 相關性檢驗時避免“無法計算帶有重複值的精確 p 值”的警告?
當變數不是連續的,但可以進行排序時,我們不使用皮爾遜相關係數來查詢線性關係,在這種情況下,斯皮爾曼相關係數就會派上用場。由於斯皮爾曼相關係數考慮了值的秩,因此相關性檢驗會忽略相同的秩以查詢 p 值,從而導致“無法計算帶有重複值的精確 p 值”的警告。這可以透過在 cor.test 函式內部使用 exact = FALSE 來避免。
示例
考慮以下向量並執行斯皮爾曼相關性檢驗以檢查它們之間的關係:
x1<-rpois(20,2) y1<-rpois(20,5) cor.test(x1,y1,method="spearman")
輸出
Spearman's rank correlation rho data: x1 and y1 S = 1401.7, p-value = 0.8214 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.05390585 Warning message: In cor.test.default(x1, y1, method = "spearman") : Cannot compute exact p-value with ties
這裡,我們得到了關於重複值的警告,這可以透過使用 exact=FALSE 來避免,如下所示:
示例
cor.test(x1,y1,method="spearman",exact=FALSE)
輸出
Spearman's rank correlation rho data: x1 and y1 S = 1401.7, p-value = 0.8214 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.05390585
讓我們再看一些例子:
示例
x2<-sample(1:100,500,replace=TRUE) y2<-sample(1:50,500,replace=TRUE) cor.test(x2,y2,method="spearman")
輸出
Spearman's rank correlation rho data: x2 and y2 S = 20110148, p-value = 0.4387 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.03470902 Warning message: In cor.test.default(x2, y2, method = "spearman") : Cannot compute exact p-value with ties
示例
cor.test(x2,y2,method="spearman",exact=FALSE)
輸出
Spearman's rank correlation rho data: x2 and y2 S = 20110148, p-value = 0.4387 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.03470902
示例
x3<-sample(101:110,5000,replace=TRUE) y3<-sample(501:510,5000,replace=TRUE) cor.test(x3,y3,method="spearman")
輸出
Spearman's rank correlation rho data: x3 and y3 S = 2.0642e+10, p-value = 0.5155 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.009199129 Warning message: In cor.test.default(x3, y3, method = "spearman") : Cannot compute exact p-value with ties
示例
cor.test(x3,y3,method="spearman",exact=FALSE)
輸出
Spearman's rank correlation rho data: x3 and y3 S = 2.0642e+10, p-value = 0.5155 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.009199129
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