{"id":3349,"date":"2023-07-17T23:22:52","date_gmt":"2023-07-17T23:22:52","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e4%b8%89%e8%b7%af%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90python\/"},"modified":"2023-07-17T23:22:52","modified_gmt":"2023-07-17T23:22:52","slug":"%e4%b8%89%e8%b7%af%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e4%b8%89%e8%b7%af%e6%96%b9%e5%b7%ae%e5%88%86%e6%9e%90python\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790"},"content":{"rendered":"

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\u4e09\u5411\u65b9\u5dee\u5206\u6790<\/strong>\u7528\u4e8e\u786e\u5b9a\u5206\u5e03\u5728\u4e09\u4e2a\u56e0\u7d20\u4e2d\u7684\u4e09\u4e2a\u6216\u66f4\u591a\u72ec\u7acb\u7ec4\u7684\u5e73\u5747\u503c\u4e4b\u95f4\u662f\u5426\u5b58\u5728\u7edf\u8ba1\u663e\u7740\u5dee\u5f02\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\u3002<\/span><\/p>\n

\u793a\u4f8b\uff1aPython \u4e2d\u7684\u4e09\u5411\u65b9\u5dee\u5206\u6790<\/strong><\/span><\/h2>\n

\u5047\u8bbe\u7814\u7a76\u4eba\u5458\u60f3\u8981\u786e\u5b9a\u4e24\u79cd\u8bad\u7ec3\u8ba1\u5212\u662f\u5426\u4f1a\u5bfc\u81f4\u5927\u5b66\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u8df3\u8dc3\u9ad8\u5ea6\u5e73\u5747\u63d0\u9ad8\u4e0d\u540c\u3002<\/span><\/p>\n

\u7814\u7a76\u4eba\u5458\u6000\u7591\u6027\u522b\u548c\u5206\u533a\uff08\u5206\u533a I \u6216\u5206\u533a II\uff09\u4e5f\u53ef\u80fd\u5f71\u54cd\u8df3\u8dc3\u9ad8\u5ea6\uff0c\u8fd9\u5c31\u662f\u4ed6\u4e5f\u6536\u96c6\u8fd9\u4e9b\u56e0\u7d20\u6570\u636e\u7684\u539f\u56e0\u3002<\/span><\/p>\n

\u4ed6\u7684\u76ee\u6807\u662f\u8fdb\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff0c\u4ee5\u786e\u5b9a\u8bad\u7ec3\u8ba1\u5212\u3001\u6027\u522b\u548c\u5212\u5206\u5982\u4f55\u5f71\u54cd\u8df3\u8dc3\u9ad8\u5ea6\u3002<\/span><\/p>\n

\u4f7f\u7528\u4ee5\u4e0b\u6b65\u9aa4\u5728 Python \u4e2d\u6267\u884c\u6b64\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff1a<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/p>\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a pandas DataFrame \u6765\u4fdd\u5b58\u6570\u636e\uff1a<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' program<\/span> ': np.repeat<\/span> ([1,2],20),\n                   ' gender<\/span> ': np. tile<\/span> (np. repeat<\/span> (['M', 'F'], 10), 2),\n                   ' division<\/span> ': np. tile<\/span> (np. repeat<\/span> ([1, 2], 5), 4),\n                   ' height<\/span> ': [7, 7, 8, 8, 7, 6, 6, 5, 6, 5,\n                              5, 5, 4, 5, 4, 3, 3, 4, 3, 3,\n                              6, 6, 5, 4, 5, 4, 5, 4, 4, 3,\n                              2, 2, 1, 4, 4, 2, 1, 1, 2, 1]})\n\n#view first ten rows of DataFrame \n<\/span>df[:10]\n\n\tprogram gender division height\n0 1 M 1 7\n1 1 M 1 7\n2 1 M 1 8\n3 1 M 1 8\n4 1 M 1 7\n5 1 M 2 6\n6 1 M 2 6\n7 1 M 2 5\n8 1 M 2 6\n9 1 M 2 5\n<\/strong><\/span><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u6267\u884c\u4e09\u56e0\u7d20\u65b9\u5dee\u5206\u6790<\/strong><\/span><\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528statsmodels<\/strong>\u5e93\u4e2d\u7684anova_lm()<\/strong>\u51fd\u6570\u6765\u6267\u884c\u4e09\u5411\u65b9\u5dee\u5206\u6790\uff1a<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\nfrom<\/span> statsmodels. formula<\/span> . api<\/span> import<\/span> ols\n\n#perform three-way ANOVA\n<\/span>model = ols(\"\"\"height ~ C(program) + C(gender) + C(division) +\n               C(program):C(gender) + C(program):C(division) + C(gender):C(division) +\n               C(program):C(gender):C(division)\"\"\", data=df) .fit<\/span> ()\n\nsm. stats<\/span> . anova_lm<\/span> (model, typ= 2<\/span> )\n\n\t                          sum_sq df F PR(>F)\nC(program) 3.610000e+01 1.0 6.563636e+01 2.983934e-09\nC(gender) 6.760000e+01 1.0 1.229091e+02 1.714432e-12\nC(division) 1.960000e+01 1.0 3.563636e+01 1.185218e-06\nC(program):C(gender) 2.621672e-30 1.0 4.766677e-30 1.000000e+00\nC(program):C(division) 4.000000e-01 1.0 7.272727e-01 4.001069e-01\nC(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01\nC(program):C(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01\nResidual 1.760000e+01 32.0 NaN NaN<\/strong><\/span><\/pre>\n
\u7b2c 3 \u6b65\uff1a\u89e3\u91ca\u7ed3\u679c<\/strong><\/span><\/p>\n
Pr(>F)<\/strong>\u5217\u663e\u793a\u6bcf\u4e2a\u5355\u72ec\u56e0\u5b50\u7684 p \u503c\u4ee5\u53ca\u56e0\u5b50\u4e4b\u95f4\u7684\u4ea4\u4e92\u4f5c\u7528\u3002<\/span><\/p>\n
\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0c\u4e09\u4e2a\u56e0\u7d20\u4e4b\u95f4\u7684\u4ea4\u4e92\u4f5c\u7528\u5747\u4e0d\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u770b\u5230\uff0c\u4e09\u4e2a\u56e0\u7d20\uff08\u9879\u76ee\u3001\u6027\u522b\u548c\u90e8\u95e8\uff09\u5747\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0cp \u503c\u5982\u4e0b\uff1a<\/span><\/p>\n