{"id":690,"date":"2021-02-21T15:49:44","date_gmt":"2021-02-21T07:49:44","guid":{"rendered":"http:\/\/www.wuchangsong.com\/?p=690"},"modified":"2021-05-16T17:51:22","modified_gmt":"2021-05-16T09:51:22","slug":"%e6%95%b0%e6%8d%ae%e7%9b%b8%e5%85%b3%e6%80%a7%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"http:\/\/www.wuchangsong.com\/?p=690","title":{"rendered":"\u6570\u636e\u76f8\u5173\u6027\u5206\u6790"},"content":{"rendered":"

\u7b80\u4ecb<\/strong>\uff1a\u8bc4\u4ef7\u4e24\u7ec4\u6570\u636e\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6709\u76ae\u5c14\u68ee\uff08pearson\uff09\u76f8\u5173\u7cfb\u6570\uff0c\u65af\u76ae\u5c14\u66fc\uff08spearman\uff09\u76f8\u5173\u7cfb\u6570\u548c\u80af\u5fb7\u5c14\uff08kendall\uff09\u76f8\u5173\u7cfb\u6570\u3002\u5728\u8fd9\u4e09\u5927\u76f8\u5173\u7cfb\u6570\u4e2d\uff0cspearman\u548ckendall\u5c5e\u4e8e\u7b49\u7ea7\u76f8\u5173\u7cfb\u6570\u4ea6\u79f0\u4e3a\u201c\u79e9\u76f8\u5173\u7cfb\u6570\u201d\uff0c\u662f\u53cd\u6620\u7b49\u7ea7\u76f8\u5173\u7a0b\u5ea6\u7684\u7edf\u8ba1\u5206\u6790\u6307\u6807\u3002\u76f8\u5173\u7cfb\u6570\u7684\u7edd\u5bf9\u503c\u8d8a\u5927\uff0c\u76f8\u5173\u6027\u8d8a\u5f3a\uff0c\u76f8\u5173\u7cfb\u6570\u8d8a\u63a5\u8fd1\u4e8e1\u6216-1\uff0c\u76f8\u5173\u5ea6\u8d8a\u5f3a\uff0c\u76f8\u5173\u7cfb\u6570\u8d8a\u63a5\u8fd1\u4e8e0\uff0c\u76f8\u5173\u5ea6\u8d8a\u5f31\u3002<\/p>\n

Pearson\uff08\u76ae\u5c14\u900a\uff09\u76f8\u5173\u7cfb\u6570\uff1a<\/strong>\u76ae\u5c14\u900a\u76f8\u5173\u4e5f\u79f0\u4e3a\u79ef\u5dee\u76f8\u5173\uff08\u6216\u79ef\u77e9\u76f8\u5173\uff09\u662f\u82f1\u56fd\u7edf\u8ba1\u5b66\u5bb6\u76ae\u5c14\u900a\u4e8e20\u4e16\u7eaa\u63d0\u51fa\u7684\u4e00\u79cd\u8ba1\u7b97\u76f4\u7ebf\u76f8\u5173\u7684\u65b9\u6cd5\u3002\u9002\u7528\u4e8e\uff1a<\/p>\n

(1)\u3001\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u662f\u7ebf\u6027\u5173\u7cfb\uff0c\u90fd\u662f\u8fde\u7eed\u6570\u636e\u3002<\/p>\n

(2)\u3001\u4e24\u4e2a\u53d8\u91cf\u7684\u603b\u4f53\u662f\u6b63\u6001\u5206\u5e03\uff0c\u6216\u63a5\u8fd1\u6b63\u6001\u7684\u5355\u5cf0\u5206\u5e03\u3002<\/p>\n

(3)\u3001\u4e24\u4e2a\u53d8\u91cf\u7684\u89c2\u6d4b\u503c\u662f\u6210\u5bf9\u7684\uff0c\u6bcf\u5bf9\u89c2\u6d4b\u503c\u4e4b\u95f4\u76f8\u4e92\u72ec\u7acb\u3002<\/p>\n

Spearman Rank\uff08\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\uff09\u76f8\u5173\u7cfb\u6570<\/strong>\uff1a\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u7cfb\u6570\u4ee5Charles Spearman\u547d\u540d\uff0c\u5e76\u7ecf\u5e38\u7528\u5e0c\u814a\u5b57\u6bcd\u03c1\uff08rho\uff09\u8868\u793a\u5176\u503c\u3002\u53c8\u79f0\u79e9\u76f8\u5173\u7cfb\u6570\uff0c\u662f\u5229\u7528\u4e24\u53d8\u91cf\u7684\u79e9\u6b21\u5927\u5c0f\u4f5c\u7ebf\u6027\u76f8\u5173\u5206\u6790\uff0c\u5bf9\u539f\u59cb\u53d8\u91cf\u7684\u5206\u5e03\u4e0d\u4f5c\u8981\u6c42\uff0c\u5c5e\u4e8e\u975e\u53c2\u6570\u7edf\u8ba1\u65b9\u6cd5\uff0c\u9002\u7528\u8303\u56f4\u8981\u5e7f\u4e9b\u3002\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u662f\u6839\u636e\u7b49\u7ea7\u8d44\u6599\u7814\u7a76\u4e24\u4e2a\u53d8\u91cf\u95f4\u76f8\u5173\u5173\u7cfb\u7684\u65b9\u6cd5\u3002\u5b83\u662f\u4f9d\u636e\u4e24\u5217\u6210\u5bf9\u7b49\u7ea7\u7684\u5404\u5bf9\u7b49\u7ea7\u6570\u4e4b\u5dee\u6765\u8fdb\u884c\u8ba1\u7b97\u7684\uff0c\u6240\u4ee5\u53c8\u79f0\u4e3a\u201c\u7b49\u7ea7\u5dee\u6570\u6cd5\u201d\u3002\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u5bf9\u6570\u636e\u6761\u4ef6\u7684\u8981\u6c42\u6ca1\u6709\u79ef\u5dee\u76f8\u5173\u7cfb\u6570\u4e25\u683c\uff0c\u53ea\u8981\u4e24\u4e2a\u53d8\u91cf\u7684\u89c2\u6d4b\u503c\u662f\u6210\u5bf9\u7684\u7b49\u7ea7\u8bc4\u5b9a\u8d44\u6599\uff0c\u6216\u8005\u662f\u7531\u8fde\u7eed\u53d8\u91cf\u89c2\u6d4b\u8d44\u6599\u8f6c\u5316\u5f97\u5230\u7684\u7b49\u7ea7\u8d44\u6599\uff0c\u4e0d\u8bba\u4e24\u4e2a\u53d8\u91cf\u7684\u603b\u4f53\u5206\u5e03\u5f62\u6001\u3001\u6837\u672c\u5bb9\u91cf\u7684\u5927\u5c0f\u5982\u4f55\uff0c\u90fd\u53ef\u4ee5\u7528\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u6765\u8fdb\u884c\u7814\u7a76\u3002\u5bf9\u4e8e\u670d\u4ecePearson \u76f8\u5173\u7cfb\u6570\u7684\u6570\u636e\u4ea6\u53ef\u8ba1\u7b97Spearman \u76f8\u5173\u7cfb\u6570\uff0c\u4f46\u7edf\u8ba1\u6548\u80fd\u8981\u4f4e\u4e00\u4e9b\u3002Pearson \u76f8\u5173\u7cfb\u6570\u7684\u8ba1\u7b97\u516c\u5f0f\u53ef\u4ee5\u5b8c\u5168\u5957\u7528 Spearman \u76f8\u5173\u7cfb\u6570\u8ba1\u7b97\u516c\u5f0f\uff0c\u4f46\u516c\u5f0f\u4e2d\u7684x \u548cy \u7528\u76f8\u5e94\u7684\u79e9\u6b21\u4ee3\u66ff\u5373\u53ef\u3002<\/p>\n

Kendall Rank\uff08\u80af\u5fb7\u5c14\u7b49\u7ea7\uff09\u76f8\u5173\u7cfb\u6570<\/strong>\uff1a\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u80af\u5fb7\u5c14\u76f8\u5173\u7cfb\u6570\u662f\u4ee5Maurice Kendall\u547d\u540d\u7684\uff0c\u5e76\u7ecf\u5e38\u7528\u5e0c\u814a\u5b57\u6bcd\u03c4\uff08tau\uff09\u8868\u793a\u5176\u503c\u3002\u7528\u4e8e\u53cd\u6620\u5206\u7c7b\u53d8\u91cf\u76f8\u5173\u6027\u7684\u6307\u6807\uff0c\u9002\u7528\u4e8e\u4e24\u4e2a\u5206\u7c7b\u53d8\u91cf\u5747\u4e3a\u6709\u5e8f\u5206\u7c7b\u7684\u60c5\u51b5\u3002\u5bf9\u76f8\u5173\u7684\u6709\u5e8f\u53d8\u91cf\u8fdb\u884c\u975e\u53c2\u6570\u76f8\u5173\u68c0\u9a8c\uff1b \u53d6\u503c\u8303\u56f4\u5728-1-1 \u4e4b\u95f4\uff0c\u6b64\u68c0\u9a8c\u9002\u5408\u4e8e\u6b63\u65b9\u5f62\u8868\u683c\uff1b\u80af\u5fb7\u5c14(Kendall)W \u7cfb\u6570\u53c8\u79f0\u548c\u8c10\u7cfb\u6570\uff0c\u662f\u8868\u793a\u591a\u5217\u7b49\u7ea7\u53d8\u91cf\u76f8\u5173\u7a0b\u5ea6\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u9002\u7528\u8fd9\u79cd\u65b9\u6cd5\u7684\u6570\u636e\u8d44\u6599\u4e00\u822c\u662f\u91c7\u7528\u7b49\u7ea7\u8bc4\u5b9a\u7684\u65b9\u6cd5\u6536\u96c6\u7684\uff0c\u5373\u8ba9K \u4e2a \u8bc4\u59d4\uff08\u88ab\u8bd5\uff09\u8bc4\u5b9aN \u4ef6\u4e8b\u7269\uff0c\u62161 \u4e2a\u8bc4\u59d4\uff08\u88ab\u8bd5\uff09\u5148\u540eK \u6b21\u8bc4\u5b9aN \u4ef6\u4e8b\u7269\u3002\u7b49\u7ea7\u8bc4\u5b9a\u6cd5\u6bcf\u4e2a\u8bc4\u4ef7\u8005\u5bf9N \u4ef6\u4e8b\u7269\u6392\u51fa\u4e00\u4e2a\u7b49\u7ea7\u987a\u5e8f\uff0c\u6700\u5c0f\u7684\u7b49\u7ea7\u5e8f\u6570\u4e3a1 \uff0c\u6700\u5927\u7684\u4e3aN \uff0c\u82e5\u5e76\u5217\u7b49\u7ea7\u65f6\uff0c\u5219\u5e73\u5206\u5171\u540c\u5e94\u8be5\u5360\u636e\u7684\u7b49\u7ea7\uff0c\u5982\uff0c\u5e73\u65f6\u6240\u8bf4\u7684\u4e24\u4e2a\u5e76\u5217\u7b2c\u4e00\u540d\uff0c\u4ed6\u4eec\u5e94\u8be5\u5360\u636e1 \uff0c2 \u540d\uff0c\u6240\u4ee5\u5b83\u4eec\u7684\u7b49\u7ea7\u5e94\u662f1.5, \u53c8\u5982\u4e00\u4e2a\u7b2c\u4e00 \u540d\uff0c\u4e24\u4e2a\u5e76\u5217\u7b2c\u4e8c\u540d\uff0c\u4e09\u4e2a\u5e76\u5217\u7b2c\u4e09\u540d\uff0c\u5219\u5b83\u4eec\u5bf9\u5e94\u7684\u7b49\u7ea7\u5e94\u8be5\u662f1,2.5,2.5,5,5,5, \u8fd9\u91cc2.5 \u662f2,3 \u7684\u5e73\u5747\uff0c5 \u662f4,5,6 \u7684\u5e73\u5747\u3002<\/p>\n

R\u5305\u8ba1\u7b97\u76f8\u5173\u6027\u7cfb\u6570<\/strong>\uff1a<\/p>\n

library(ggplot2)\nlibrary(ggpubr)\ndata <- read.table(\"111.xls\",sep=\"\\t\",header = TRUE)#\u5217\u662f\u7279\u5f81\uff08\u4e24\u4e2a\u7279\u5f81\uff09\uff0c\u884c\u662f\u7269\u79cd\u540d\nggplot(data=data, aes(x=genome, y=Tes))+geom_point(color=\"red\")+stat_smooth(method=\"lm\")+stat_cor(data=data, method = \"pearson\")+theme_classic()\n#stat_cor(data=dat, method = \"pearson\")\u610f\u4e3a\u7528pearson\u76f8\u5173\u8fdb\u884c\u76f8\u5173\u6027\u5206\u6790\uff0c\u53ef\u4ee5\u81ea\u884c\u66f4\u6539\u65b9\u6cd5\n#stat_smooth\u662f\u753b\u62df\u5408\u66f2\u7ebf\u7684\u51fd\u6570\n#se=FALSE\u610f\u601d\u4e3a\u4e0d\u753b\u51fa\u7f6e\u4fe1\u533a\u95f4\n#data=\u540e\u8ddf\u9700\u8981\u753b\u56fe\u7684\u6570\u636e\u7684\u6587\u4ef6\u540d\n#X=\u540e\u8ddf\u4f5c\u4e3aX\u8f74\u7684\u6570\u636e\u7684\u90a3\u4e00\u5217\u7684\u5217\u540d\n#Y=\u540e\u8ddf\u4f5c\u4e3aY\u8f74\u7684\u6570\u636e\u7684\u90a3\u4e00\u5217\u7684\u5217\u540d\n#geom_point\u51fd\u6570\u662f\u4e2a\u6027\u5316\u8bbe\u7f6e\u6563\u70b9\u56fe\u70b9\u7684\u5f62\u72b6\uff0c\u989c\u8272\uff0c\u5927\u5c0f\u7b49\uff0c\u6b64\u5904\u53ea\u8bbe\u7f6e\u4e86\u989c\u8272\uff0c\u6709\u9700\u8981\u53ef\u81ea\u884c\u52a0\u5165\n#theme_classic() x,y\u8f74\u5b9e\u7ebf\u5316\nlibrary(corrplot)\nlibrary(pheatmap)\ncorr <- cor(data, method = 'pearson')\npheatmap(corr)\ncorrplot(corr, type = 'upper', tl.col = 'black', order = 'hclust', tl.srt = 45, addCoef.col = 'white')\n<\/pre>\n","protected":false},"excerpt":{"rendered":"
\u7b80\u4ecb\uff1a\u8bc4\u4ef7\u4e24\u7ec4\u6570\u636e\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6709\u76ae\u5c14\u68ee\uff08pearson\uff09\u76f8\u5173\u7cfb\u6570\uff0c\u65af\u76ae\u5c14\u66fc\uff08spearman\uff09\u76f8\u5173\u7cfb\u6570\u548c\u80af\u5fb7\u5c14\uff08kendall\uff09\u76f8\u5173\u7cfb\u6570\u3002\u5728\u8fd9\u4e09\u5927\u76f8\u5173\u7cfb\u6570\u4e2d\uff0cspearman\u548ckendall\u5c5e\u4e8e\u7b49\u7ea7\u76f8\u5173\u7cfb\u6570\u4ea6\u79f0\u4e3a\u201c\u79e9\u76f8\u5173\u7cfb\u6570\u201d\uff0c\u662f\u53cd\u6620\u7b49\u7ea7\u76f8\u5173\u7a0b\u5ea6\u7684\u7edf\u8ba1\u5206\u6790\u6307\u6807\u3002\u76f8\u5173\u7cfb\u6570\u7684\u7edd\u5bf9\u503c\u8d8a\u5927\uff0c\u76f8\u5173\u6027\u8d8a\u5f3a\uff0c\u76f8\u5173\u7cfb\u6570\u8d8a\u63a5\u8fd1\u4e8e1\u6216-1\uff0c\u76f8\u5173\u5ea6\u8d8a\u5f3a\uff0c\u76f8\u5173\u7cfb\u6570\u8d8a\u63a5\u8fd1\u4e8e0\uff0c\u76f8\u5173\u5ea6\u8d8a\u5f31\u3002 Pearson\uff08\u76ae\u5c14\u900a\uff09\u76f8\u5173\u7cfb\u6570\uff1a\u76ae\u5c14\u900a\u76f8\u5173\u4e5f\u79f0\u4e3a\u79ef\u5dee\u76f8\u5173\uff08\u6216\u79ef\u77e9\u76f8\u5173\uff09\u662f\u82f1\u56fd\u7edf\u8ba1\u5b66\u5bb6\u76ae\u5c14\u900a\u4e8e20\u4e16\u7eaa\u63d0\u51fa\u7684\u4e00\u79cd\u8ba1\u7b97\u76f4\u7ebf\u76f8\u5173\u7684\u65b9\u6cd5\u3002\u9002\u7528\u4e8e\uff1a (1)\u3001\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u662f\u7ebf\u6027\u5173\u7cfb\uff0c\u90fd\u662f\u8fde\u7eed\u6570\u636e\u3002 (2)\u3001\u4e24\u4e2a\u53d8\u91cf\u7684\u603b\u4f53\u662f\u6b63\u6001\u5206\u5e03\uff0c\u6216\u63a5\u8fd1\u6b63\u6001\u7684\u5355\u5cf0\u5206\u5e03\u3002 (3)\u3001\u4e24\u4e2a\u53d8\u91cf\u7684\u89c2\u6d4b\u503c\u662f\u6210\u5bf9\u7684\uff0c\u6bcf\u5bf9\u89c2\u6d4b\u503c\u4e4b\u95f4\u76f8\u4e92\u72ec\u7acb\u3002 Spearman Rank\uff08\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\uff09\u76f8\u5173\u7cfb\u6570\uff1a\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u7cfb\u6570\u4ee5Charles 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\u7684\u5e73\u5747\u3002 R\u5305\u8ba1\u7b97\u76f8\u5173\u6027\u7cfb\u6570\uff1a library(ggplot2) library(ggpubr) data <- read.table(“111.xls”,sep=”\\t”,header = TRUE)#\u5217\u662f\u7279\u5f81\uff08\u4e24\u4e2a\u7279\u5f81\uff09\uff0c\u884c\u662f\u7269\u79cd\u540d ggplot(data=data, aes(x=genome, y=Tes))+geom_point(color=”red”)+stat_smooth(method=”lm”)+stat_cor(data=data, method = “pearson”)+theme_classic() #stat_cor(data=dat, method 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