{"id":1299,"date":"2024-02-13T11:41:37","date_gmt":"2024-02-13T03:41:37","guid":{"rendered":"https:\/\/cabit.top\/?p=1299"},"modified":"2024-05-17T14:19:18","modified_gmt":"2024-05-17T06:19:18","slug":"whats-new-in-v29-2901-2902","status":"publish","type":"post","link":"https:\/\/cabit.top\/?p=1299","title":{"rendered":"SPSS V29 \uff0c29.0.1 \u548c 29.0.2\u4e2d\u7684\u65b0\u589e\u5185\u5bb9"},"content":{"rendered":"\n

<\/p>\n\n\n\n

IBM SPSS Statistics 29.0.2<\/a><\/h2>\n\n\n\n

Python \u6269\u5c55<\/strong><\/p>\n\n\n\n

\u73b0\u5728\uff0c\u5728 Extension Hub \u4e2d\u63d0\u4f9b\u4e86\u4e09\u4e2a\u65b0\u7684 Python \u6269\u5c55:<\/p>\n\n\n\n

    \n
  1. SPSSINC_TRANS<\/li>\n\n\n\n
  2. SPSSINC_MODIFY_TABLES<\/li>\n\n\n\n
  3. STATS_IMBALANCED<\/li>\n<\/ol>\n\n\n\n

    \u4e00\u822c\u589e\u5f3a\u529f\u80fd<\/strong>\u94fe\u63a5\u5230\u6b22\u8fce\u5bf9\u8bdd\u6846\u4e0a\u7684 IBM watson.ai<\/strong> \u9875\u9762\u3002\u4ece 4.1.2 \u5347\u7ea7\u5230 \u6765\u81ea 5.2.1 \u7684 Apache POI <\/strong>\u4ee5\u89e3\u51b3\u5b89\u5168\u6027\u548c\u6f0f\u6d1e\u95ee\u9898\u3002\u4e0d\u63a8\u8350\u7684\u529f\u80fd<\/strong>\u7528\u6237\u53cd\u9988<\/strong>\u4ece\u7248\u672c 29.0.2 \u5f00\u59cb\uff0c Medallia \u5c06\u65e0\u6cd5\u63d0\u4f9b\u53cd\u9988\u3002<\/p>\n\n\n\n

    IBM SPSS Statistics 29.0.1<\/a><\/h2>\n\n\n\n

    \u6570\u636e\u7f16\u8f91\u5668<\/strong>\u65b0\u7684 “\u6982\u8ff0” \u9009\u9879\u5361<\/strong> \u63d0\u4f9b\u6709\u5173\u6570\u636e\u96c6\u6216\u6587\u4ef6\u4e2d\u6570\u636e\u7684\u7279\u5f81\u7684\u4fe1\u606f\uff0c\u5176\u4e2d\u5305\u542b\u53d8\u91cf\u7c7b\u578b\uff0c\u6d4b\u91cf\u7ea7\u522b\u548c\u7f3a\u5931\u6570\u636e\u7684\u6458\u8981\uff0c\u5e76\u5141\u8bb8\u6839\u636e\u6d4b\u91cf\u7ea7\u522b\u5b9a\u4e49\u5411\u4e0b\u94bb\u53d6\u5230\u5177\u6709\u76f8\u5e94\u56fe\u8868\u548c\u6458\u8981\u7edf\u8ba1\u4fe1\u606f\u7684\u5404\u4e2a\u53d8\u91cf\u3002\u5206\u6790\u8fc7\u7a0b<\/strong>\u91cd\u590d\u4e8b\u4ef6\u6570\u636e\u7684\u53c2\u6570\u5316\u751f\u5b58\u56de\u5f52\u6a21\u578b<\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u751f\u5b58<\/strong> > \u53c2\u6570\u5316\u5171\u4eab\u8106\u5f31\u6a21\u578b<\/strong> \u4ee5\u901a\u8fc7\u5408\u5e76\u5171\u4eab\u8106\u5f31\u9879\u6765\u4f30\u7b97\u91cd\u590d\u4e8b\u4ef6\u6570\u636e\u7684\u53c2\u6570\u751f\u5b58\u6a21\u578b\u3002 \u6b64\u672f\u8bed\u88ab\u89c6\u4e3a\u4e00\u4e2a\u968f\u673a\u7ec4\u4ef6\uff0c\u4ee5\u8003\u8651\u7531\u4e8e\u5355\u4e2a\u6216\u7ec4\u7ea7\u522b\u7684\u53ef\u53d8\u6027\u800c\u4ea7\u751f\u7684\u672a\u89c2\u5bdf\u5230\u7684\u5f71\u54cd\u3002\u7ebf\u6027\u56de\u5f52\u4e2d\u7684 PRESS (\u9884\u6d4b\u6b8b\u5dee\u5e73\u65b9\u548c) \u7edf\u8ba1\u4fe1\u606f<\/strong><\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027<\/strong> \u548c \u7edf\u8ba1<\/strong> \u6309\u94ae\u4ee5\u83b7\u53d6\u9884\u6d4b\u6b8b\u5dee\u5e73\u65b9\u548c (\u603b\u7edf<\/strong>) \u7edf\u8ba1\uff0c\u8fd9\u662f\u7528\u4e8e\u8bc4\u4f30\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u5e38\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7c7b\u578b\u7edf\u8ba1\u3002Youden ‘s Index for ROC (\u63a5\u6536\u65b9\u8fd0\u7b97\u7b26\u7279\u5f81) \u66f2\u7ebf<\/strong><\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u5206\u7c7b<\/strong> > ROC \u5206\u6790<\/strong> \u548c \u663e\u793a<\/strong> \u6309\u94ae\u4ee5\u8bf7\u6c42\u6cbf ROC \u66f2\u7ebf\u7684\u6bcf\u4e2a\u70b9\u7684 Youden ‘s index<\/strong> \u3002 Youden \u7684\u6307\u6570\u662f\u6bcf\u4e2a\u5207\u70b9\u654f\u611f\u5ea6\u548c\u7279\u5f02\u6027\u7684\u6d41\u884c\u5355\u6570\u6c47\u603b\u3002\u4f30\u7b97\u6df7\u5408\u6a21\u578b\u65f6\u5c06\u968f\u673a\u6548\u5e94\u9884\u6d4b (EBLUPs) \u5bfc\u51fa\u5230\u6570\u636e\u96c6\u6216\u6587\u4ef6<\/strong><\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u6df7\u5408\u6a21\u578b<\/strong> > \u7ebf\u6027<\/strong> \uff0c\u5f53\u4e00\u4e2a\u6216\u591a\u4e2a\u968f\u673a\u89c4\u8303\u8bf7\u6c42\u663e\u793a\u968f\u673a\u6548\u5e94\u9884\u6d4b\u65f6\uff0c\u9009\u62e9 \u5bfc\u51fa<\/strong> \u6309\u94ae\u4ee5\u4f7f\u7528 EBLUPs \u521b\u5efa\u65b0\u7684\u6570\u636e\u96c6\u6216\u6587\u4ef6\u3002<\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790> \u6df7\u5408\u6a21\u578b> \u5e7f\u4e49\u7ebf\u6027<\/strong> \uff0c\u5f53\u4e00\u4e2a\u6216\u591a\u4e2a\u968f\u673a\u89c4\u8303\u8bf7\u6c42\u663e\u793a\u968f\u673a\u6548\u5e94\u9884\u6d4b\u65f6\uff0c\u5728 “\u6a21\u578b\u9009\u9879” \u9009\u9879\u5361\u4e0a\uff0c\u9009\u62e9 \u5bfc\u51fa<\/strong> \u9879\u4ee5\u4f7f\u7528 EBLUPs \u5bfc\u51fa\u521b\u5efa\u65b0\u6570\u636e\u96c6\u6216\u6587\u4ef6\u3002\u5bf9\u5bf9\u8bdd\u6846\u51fd\u6570\u7684\u589e\u5f3a\u529f\u80fd<\/strong>\u767e\u5206\u4f4d\u6570<\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u63cf\u8ff0\u7edf\u8ba1<\/strong> > \u767e\u5206\u4f4d\u6570<\/strong> \u4ee5\u8bbf\u95ee\u4e00\u4e2a\u65b0\u5bf9\u8bdd\u6846\uff0c\u8be5\u5bf9\u8bdd\u6846\u901a\u8fc7\u4f7f\u7528\u4e94\u79cd\u53ef\u7528\u4f30\u8ba1\u65b9\u6cd5\u4e2d\u7684\u4efb\u4f55\u65b9\u6cd5\u4ee5\u53ca bootstrap \u7f6e\u4fe1\u533a\u95f4\uff0c\u5168\u9762\u8bbf\u95ee \u4ee5\u5bf9<\/strong> \u8fc7\u7a0b\u4e2d\u7684\u6240\u6709\u767e\u5206\u4f4d\u6570\u51fd\u6570 (\u5305\u62ec\u56db\u5206\u4f4d\u6570\u6216\u5b9a\u5236\u767e\u5206\u4f4d\u6570\u7684\u6307\u5b9a)\u3002<\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u63cf\u8ff0\u7edf\u8ba1<\/strong> > \u6d4f\u89c8<\/strong>\uff0c\u7136\u540e\u5355\u51fb \u7edf\u8ba1<\/strong> \u6309\u94ae\u4ee5\u5b8c\u5168\u8bbf\u95ee\u4e0e\u72ec\u7acb \u767e\u5206\u4f4d\u6570<\/strong> \u5bf9\u8bdd\u6846\u76f8\u540c\u7684\u51fd\u6570\u3002\u7ebf\u6027\u56de\u5f52<\/strong><\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027<\/strong>\uff0c\u7136\u540e\u5355\u51fb \u7edf\u8ba1\u4fe1\u606f<\/strong> \u6309\u94ae\u4ee5\u8bbf\u95ee \u9009\u62e9\u6761\u4ef6<\/strong> \u590d\u9009\u6846\u4ee5\u8bbf\u95ee SELECTION<\/strong> \u5173\u952e\u5b57\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027<\/strong>\uff0c\u7136\u540e\u5355\u51fb \u9009\u9879<\/strong> \u6309\u94ae\uff0c\u4ee5\u4fbf\u80fd\u591f\u4e3a CRITERIA<\/strong> \u5b50\u547d\u4ee4\u6307\u5b9a TOLERANCE<\/strong> \u5173\u952e\u5b57\u7ea7\u522b\uff0c\u7528\u4e8e\u5904\u7406\u8868\u73b0\u51fa\u63a5\u8fd1\u5171\u7ebf\u6027\u7684\u53d8\u91cf\u3002Cox \u56de\u5f52<\/strong><\/p>\n\n\n\n

    \u5355\u51fb \u5206\u6790<\/strong> > \u751f\u5b58<\/strong> > Cox w\/Time-Dep Cov<\/strong> \u4ee5\u8bbf\u95ee\u91cd\u65b0\u8bbe\u8ba1\u7684\u5bf9\u8bdd\u6846\uff0c\u8be5\u5bf9\u8bdd\u6846\u73b0\u5728\u5141\u8bb8\u6307\u5b9a\/\u8ba1\u7b97 Cox \u56de\u5f52\u6a21\u578b\u7684\u591a\u4e2a\u4f9d\u8d56\u4e8e\u65f6\u95f4\u7684\u534f\u53d8\u91cf\u3002\u547d\u4ee4\u8bed\u6cd5SURVREG RECURRENT<\/strong><\/p>\n\n\n\n

    \u901a\u8fc7\u5408\u5e76\u5171\u4eab\u7684\u5f31\u9879\uff0c\u4f30\u7b97\u91cd\u590d\u4e8b\u4ef6\u6570\u636e\u7684\u53c2\u6570\u751f\u5b58\u6a21\u578b\u3002 \u6b64\u672f\u8bed\u88ab\u89c6\u4e3a\u4e00\u4e2a\u968f\u673a\u7ec4\u4ef6\uff0c\u4ee5\u8003\u8651\u7531\u4e8e\u5355\u4e2a\u6216\u7ec4\u7ea7\u522b\u7684\u53ef\u53d8\u6027\u800c\u4ea7\u751f\u7684\u672a\u89c2\u5bdf\u5230\u7684\u5f71\u54cd\u3002ROC ANALYSIS<\/strong><\/p>\n\n\n\n

    PRINT<\/strong> \u5b50\u547d\u4ee4\u7684 COordATES=ROC<\/strong> \u5173\u952e\u5b57\u4e3a Youden \u7684 “\u7d22\u5f15” \u63d0\u4f9b\u4e86 YOUDEN<\/strong> \u9009\u9879\uff0c\u5728 ROC \u66f2\u7ebf\u4e0a\u7684\u6bcf\u4e2a\u53ef\u80fd\u7684\u5206\u5272\u70b9\u5c06\u654f\u611f\u5ea6\u548c\u7279\u5f02\u6027\u7ec4\u5408\u5230\u5355\u4e2a\u5c90\u89c6\u5ea6\u91cf\u4e2d\u3002MIXED<\/strong><\/p>\n\n\n\n

    \u6dfb\u52a0\u5e26\u6709 EBLUPS<\/strong> \u5173\u952e\u5b57\u7684 OUTFILE<\/strong> \u5b50\u547d\u4ee4\uff0c\u4ee5\u5c06 EBLUPs \u6216\u968f\u673a\u6548\u5e94\u53c2\u6570\u9884\u6d4b\u5bfc\u51fa\u5230\u6570\u636e\u96c6\u6216 .sav<\/em> \u6587\u4ef6\u3002 \u5982\u679c\u901a\u8fc7 SOLUTION<\/strong> \u5173\u952e\u5b57\u5728 RANDOM<\/strong> \u5b50\u547d\u4ee4\u4e0a\u8bf7\u6c42\u4e86\u591a\u7ec4 EBLUPs \uff0c\u90a3\u4e48\u53ef\u4ee5\u5c06 FILE_\u5355\u72ec<\/strong> \u5173\u952e\u5b57\u4e0e TRUE<\/strong> \u6216 FALSE<\/strong> \u914d\u5408\u4f7f\u7528\uff0c\u4ee5\u5c06\u9884\u6d4b\u4fdd\u5b58\u5728\u4e00\u4e2a\u6216\u591a\u4e2a\u6570\u636e\u96c6\u6216\u6587\u4ef6\u4e2d\u3002GENLINMIXED<\/p>\n\n\n\n

    \u5c06 EBLUPS<\/strong> \u5173\u952e\u5b57\u6dfb\u52a0\u5230 OUTFILE<\/strong> \u5b50\u547d\u4ee4\u4ee5\u5c06 EBLUPs \u6216\u968f\u673a\u6548\u5e94\u53c2\u6570\u9884\u6d4b\u5bfc\u51fa\u5230\u6570\u636e\u96c6\u6216 .sav<\/em> \u6587\u4ef6\u3002 \u5982\u679c\u901a\u8fc7 SOLUTION<\/strong> \u5173\u952e\u5b57\u5728 RANDOM<\/strong> \u5b50\u547d\u4ee4\u4e0a\u8bf7\u6c42\u4e86\u591a\u7ec4 EBLUPs \uff0c\u90a3\u4e48\u53ef\u4ee5\u5c06 FILE_\u5355\u72ec<\/strong> \u5173\u952e\u5b57\u4e0e TRUE<\/strong> \u6216 FALSE<\/strong> \u914d\u5408\u4f7f\u7528\uff0c\u4ee5\u5c06\u9884\u6d4b\u4fdd\u5b58\u5728\u4e00\u4e2a\u6216\u591a\u4e2a\u6570\u636e\u96c6\u6216\u6587\u4ef6\u4e2d\u3002\u4e00\u822c\u589e\u5f3a\u529f\u80fd<\/strong>\u8f93\u51fa\u4fee\u6539<\/strong><\/p>\n\n\n\n

    \u8f93\u51fa\u4fee\u6539<\/strong> \u589e\u5f3a\u529f\u80fd\u63d0\u4f9b\u4e86\u53f3\u952e\u5355\u51fb\u8f93\u51fa\u900f\u89c6\u8868\u4e2d\u7684 “\u4fee\u6539\u8f93\u51fa” \u5feb\u6377\u65b9\u5f0f\uff0c\u4ee5\u5feb\u901f\u8bbf\u95ee\u591a\u4e2a\u516c\u5171\u529f\u80fd\uff0c\u4f8b\u5982\u8f6c\u7f6e\uff0c\u6392\u5e8f\u5217\uff0c\u9690\u85cf\u5217\u4ee5\u53ca\u7a81\u51fa\u663e\u793a\u5217\u4e2d\u7684\u5355\u5143\u683c\u3002”\u641c\u7d22” \u5bf9\u8bdd\u6846<\/p>\n\n\n\n

    “\u641c\u7d22” \u5bf9\u8bdd\u6846<\/strong> \u7684\u589e\u5f3a\u529f\u80fd\u5305\u62ec\u641c\u7d22\u53d8\u91cf\uff0c\u641c\u7d22 IBM SPSS \u793e\u533a\uff0c\u641c\u7d22 IBM SPSS YouTube \u901a\u9053\u7b49\u3002\u4e0d\u63a8\u8350\u7684\u529f\u80fd<\/strong>\u4ece dBase \u6587\u4ef6\u5bfc\u5165\u5e76\u5bfc\u51fa\u5230\u8fd9\u4e9b\u6587\u4ef6<\/strong><\/p>\n\n\n\n

    \u7528\u4e8e\u4ece dBase \u6587\u4ef6\u683c\u5f0f\u5bfc\u5165\u6216\u5bfc\u51fa\u5230\u8be5\u683c\u5f0f\u7684\u9009\u9879\u5df2\u4ece\u56fe\u5f62\u7528\u6237\u754c\u9762\u4e2d\u9664\u53bb\u3002 \u5e95\u5c42 GET TRANSLATE \u548c SAVE TRANSLATE \u547d\u4ee4\u8bed\u6cd5\u4ecd\u652f\u6301\u6b64\u529f\u80fd<\/strong>\u3002<\/p>\n\n\n\n

    IBM SPSS Statistics 29<\/a><\/h2>\n\n\n\n

    \u5206\u6790\u8fc7\u7a0b\u7ebf\u6027 OLS \u66ff\u4ee3\u65b9\u6cd5\u5f39\u6027\u7f51\u7edc\u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027 OLS \u66ff\u4ee3\u65b9\u6cd5<\/strong> > \u5f39\u6027\u7f51\u7edc<\/strong> \u4ee5\u83b7\u53d6\u7ebf\u6027\u5f39\u6027\u7f51\u7edc\u56de\u5f52\u5206\u6790\u3002 \u65b0\u7684 “\u7ebf\u6027\u5f39\u6027\u7f51\u7edc” \u6269\u5c55\u8fc7\u7a0b\u4f7f\u7528 Python sklearn.linear_model.ElasticNet<\/code> \u7c7b\u6765\u4f30\u7b97\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u4e0a\u7684\u56e0\u53d8\u91cf\u7684\u89c4\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002 \u89c4\u5219\u5316\u7ec4\u5408\u4e86 L1 (\u5957\u7d22) \u548c L2 (\u5cad) \u60e9\u7f5a\u3002 \u8be5\u6269\u5c55\u5305\u542b\u53ef\u9009\u65b9\u5f0f\uff0c\u7528\u4e8e\u663e\u793a\u7ed9\u5b9a L1 \u6bd4\u7387\u7684\u4e0d\u540c alpha \u503c\u7684\u8ddf\u8e2a\u56fe\uff0c\u4ee5\u53ca\u6839\u636e\u4ea4\u53c9\u9a8c\u8bc1\u9009\u62e9 L1 \u6bd4\u7387\u548c alpha \u8d85\u53c2\u6570\u503c\u3002 \u5f53\u62df\u5408\u5355\u4e2a\u6a21\u578b\u6216\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u62e9\u60e9\u7f5a\u6bd4\u7387\u548c\/\u6216 alpha \u65f6\uff0c\u53ef\u4f7f\u7528\u4fdd\u7559\u6570\u636e\u5206\u533a\u6765\u4f30\u7b97\u6837\u672c\u5916\u6027\u80fd\u3002\u5957\u7d22\u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027 OLS \u66ff\u4ee3\u65b9\u6cd5<\/strong> > \u5957\u7d22<\/strong> \u4ee5\u83b7\u53d6\u7ebf\u6027\u5957\u7d22\u56de\u5f52\u5206\u6790\u3002 \u65b0\u7684\u7ebf\u6027\u5957\u7d22\u6269\u5c55\u8fc7\u7a0b\u4f7f\u7528 Python sklearn.linear_model.Lasso<\/code> \u7c7b\u6765\u4f30\u7b97\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u7684\u56e0\u53d8\u91cf\u7684 L1 \u635f\u5931\u6b63\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u5305\u542b\u53ef\u9009\u65b9\u5f0f\u4ee5\u663e\u793a\u8ddf\u8e2a\u56fe\u4ee5\u53ca\u6839\u636e\u4ea4\u53c9\u9a8c\u8bc1\u9009\u62e9 alpha \u8d85\u53c2\u6570\u503c\u3002 \u5f53\u62df\u5408\u5355\u4e2a\u6a21\u578b\u6216\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u62e9 alpha \u65f6\uff0c\u53ef\u4f7f\u7528\u4fdd\u7559\u6570\u636e\u5206\u533a\u6765\u4f30\u7b97\u6837\u672c\u5916\u6027\u80fd\u3002\u5cad\u5355\u51fb \u5206\u6790<\/strong> > \u56de\u5f52<\/strong> > \u7ebf\u6027 OLS \u66ff\u4ee3\u65b9\u6cd5<\/strong> > \u5cad<\/strong> \u4ee5\u83b7\u53d6\u7ebf\u6027\u5cad\u56de\u5f52\u5206\u6790\u3002 \u65b0\u7684 “\u7ebf\u6027\u5cad” \u6269\u5c55\u8fc7\u7a0b\u4f7f\u7528 Python sklearn.linear_model.Ridge<\/code> \u7c7b\u6765\u4f30\u8ba1\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u4e0a\u7684\u56e0\u53d8\u91cf\u7684 L2 \u6216\u5e73\u65b9\u635f\u5931\u6b63\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u5305\u542b\u53ef\u9009\u65b9\u5f0f\u4ee5\u663e\u793a\u8ddf\u8e2a\u56fe\u4ee5\u53ca\u6839\u636e\u4ea4\u53c9\u9a8c\u8bc1\u9009\u62e9 alpha \u8d85\u53c2\u6570\u503c\u3002 \u5f53\u62df\u5408\u5355\u4e2a\u6a21\u578b\u6216\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u62e9 alpha \u65f6\uff0c\u53ef\u4f7f\u7528\u4fdd\u7559\u6570\u636e\u5206\u533a\u6765\u4f30\u7b97\u6837\u672c\u5916\u6027\u80fd\u3002\u53c2\u6570\u52a0\u901f\u6545\u969c\u65f6\u95f4 (AFT) \u6a21\u578b\u5355\u51fb \u5206\u6790<\/strong> > \u751f\u5b58<\/strong> > \u53c2\u6570\u5316\u52a0\u901f\u5931\u8d25\u65f6\u95f4 (AFT) \u6a21\u578b<\/strong> \u4ee5\u83b7\u53d6\u53c2\u6570\u5316\u52a0\u901f\u5931\u8d25\u65f6\u95f4 (AFT) \u6a21\u578b\u5206\u6790\uff0c\u8be5\u5206\u6790\u8c03\u7528\u5177\u6709\u975e\u5faa\u73af\u751f\u547d\u65f6\u95f4\u6570\u636e\u7684\u53c2\u6570\u5316\u751f\u5b58\u6a21\u578b\u8fc7\u7a0b\u3002 \u53c2\u6570\u751f\u5b58\u6a21\u578b\u5047\u8bbe\u751f\u5b58\u65f6\u95f4\u9075\u5faa\u5df2\u77e5\u5206\u5e03\uff0c\u6b64\u5206\u6790\u62df\u5408\u52a0\u901f\u5931\u8d25\u65f6\u95f4\u6a21\u578b\uff0c\u5176\u6a21\u578b\u6548\u5e94\u4e0e\u751f\u5b58\u65f6\u95f4\u6210\u6b63\u6bd4\u3002\u7ebf\u6027\u6df7\u5408\u6a21\u578b\u548c\u5e7f\u4e49\u7ebf\u6027\u6df7\u5408\u6a21\u578b\u4e2d\u7684\u4f2a -R2<\/sup> \u6d4b\u91cf\u4f2a -R2<\/sup> \u5ea6\u91cf\u548c\u7c7b\u5185\u76f8\u5173\u7cfb\u6570\u73b0\u5728\u5305\u542b\u5728\u7ebf\u6027\u6df7\u5408\u6a21\u578b\u548c\u5e7f\u4e49\u7ebf\u6027\u6df7\u5408\u6a21\u578b\u8f93\u51fa\u4e2d (\u9002\u5f53\u65f6)\u3002 \u786e\u5b9a\u7cfb\u6570 R2<\/sup> \u662f\u4e00\u4e2a\u901a\u5e38\u62a5\u544a\u7684\u7edf\u8ba1\u91cf\uff0c\u56e0\u4e3a\u5b83\u8868\u793a\u7531\u7ebf\u6027\u6a21\u578b\u89e3\u91ca\u7684\u65b9\u5dee\u6bd4\u4f8b\u3002 \u7c7b\u5185\u76f8\u5173\u7cfb\u6570 (ICC) \u662f\u4e00\u4e2a\u76f8\u5173\u7edf\u8ba1\uff0c\u7528\u4e8e\u91cf\u5316\u7531\u591a\u7ea7\/\u5206\u5c42\u6570\u636e\u4e2d\u7684\u5206\u7ec4 (\u968f\u673a) \u56e0\u5b50\u89e3\u91ca\u7684\u65b9\u5dee\u6bd4\u4f8b\u3002\u547d\u4ee4\u8bed\u6cd5GENLINMIXED\u73b0\u5728\uff0c\u8f93\u51fa\u5305\u542b\u4f2a -R2<\/sup> \u5ea6\u91cf\u548c\u7c7b\u5185\u76f8\u5173\u7cfb\u6570 (\u5982\u679c\u9002\u7528)\u3002LINEAR_ELASTIC_NET\u65b0\u7684\u6269\u5c55\u547d\u4ee4\u4f7f\u7528 Python sklearn.linear_model.ElasticNet<\/code> \u7c7b\u6765\u4f30\u7b97\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u4e0a\u7684\u56e0\u53d8\u91cf\u7684\u89c4\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002LINEAR_LASSO\u65b0\u7684\u6269\u5c55\u547d\u4ee4\u4f7f\u7528 Python sklearn.linear_model.Lasso<\/code> \u7c7b\u6765\u4f30\u7b97\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u7684\u56e0\u53d8\u91cf\u7684 L1 \u635f\u5931\u89c4\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002 \u8be5\u547d\u4ee4\u5305\u542b\u7528\u4e8e\u663e\u793a\u8ddf\u8e2a\u56fe\u548c\u9009\u62e9\u57fa\u4e8e\u4ea4\u53c9\u9a8c\u8bc1\u7684 alpha \u8d85\u53c2\u6570\u503c\u7684\u53ef\u9009\u65b9\u5f0f\u3002LINEAR_RIDGE\u65b0\u7684\u6269\u5c55\u547d\u4ee4\u4f7f\u7528 Python sklearn.linear_model.Ridge<\/code> \u7c7b\u6765\u4f30\u7b97\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u7684\u56e0\u53d8\u91cf\u7684 L2 \u6216\u5e73\u65b9\u635f\u5931\u6b63\u5219\u5316\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002 \u8be5\u547d\u4ee4\u5305\u542b\u7528\u4e8e\u663e\u793a\u8ddf\u8e2a\u56fe\u548c\u9009\u62e9\u57fa\u4e8e\u4ea4\u53c9\u9a8c\u8bc1\u7684 alpha \u8d85\u53c2\u6570\u503c\u7684\u53ef\u9009\u65b9\u5f0f\u3002MIXED\u73b0\u5728\uff0c\u8f93\u51fa\u5305\u542b\u4f2a -R2<\/sup> \u5ea6\u91cf\u548c\u7c7b\u5185\u76f8\u5173\u7cfb\u6570 (\u5982\u679c\u9002\u7528)\u3002SURVREG AFT<\/p>\n\n\n\n

    \u65b0\u547d\u4ee4\u4f7f\u7528\u975e\u5faa\u73af\u751f\u547d\u5468\u671f\u6570\u636e\u8c03\u7528\u53c2\u6570\u751f\u5b58\u6a21\u578b\u8fc7\u7a0b\u3002Python \u548c R \u5347\u7ea7Python 3.10.4 \u548c R 4.2.0 \u662f IBM\u00ae SPSS\u00ae Statistics 29\u7684\u4e00\u90e8\u5206\u3002\u9009\u62e9\u4e2a\u6848 – \u9690\u85cf\u7684\u4e2a\u6848\u5f53\u9009\u62e9\u4e86\u90e8\u5206\u4e2a\u6848\u65f6\uff0c\u672a\u9009\u62e9\u7684\u4e2a\u6848\u5c06\u4e0d\u518d\u9690\u85cf\u5728\u6570\u636e\u7f16\u8f91\u5668\u4e2d\uff0c\u5e76\u4e14\u4e0d\u4f1a\u5e9f\u5f03\u672a\u9009\u62e9\u7684\u4e2a\u6848\u3002 \u8fd9\u8868\u793a\u8fd4\u56de\u5230 Statistics 27.0.1 \u548c\u66f4\u4f4e\u7248\u672c\u7684\u884c\u4e3a\u3002\u5c0f\u63d0\u7434\u56fe\u56fe\u677f\u6a21\u677f\u9009\u62e9\u5668\u5305\u542b\u4e00\u4e2a\u65b0\u7684\u5c0f\u63d0\u7434\u56fe\uff0c\u5b83\u662f\u76d2\u5b50\u548c\u5185\u6838\u5bc6\u5ea6\u56fe\u7684\u6df7\u5408\u3002 \u5c0f\u63d0\u7434\u56fe\u663e\u793a\u6570\u636e\u4e2d\u7684\u5cf0\u503c\uff0c\u5e76\u7528\u4e8e\u53ef\u89c6\u5316\u6570\u5b57\u6570\u636e\u7684\u5206\u5e03\u3002 \u4e0e\u53ea\u80fd\u663e\u793a\u6c47\u603b\u7edf\u8ba1\u7684\u7bb1\u56fe\u4e0d\u540c\uff0c\u5c0f\u63d0\u7434\u56fe\u63cf\u8ff0\u6c47\u603b\u7edf\u8ba1\u548c\u6bcf\u4e2a\u53d8\u91cf\u7684\u5bc6\u5ea6\u3002\u5de5\u4f5c\u7c3f\u65b9\u5f0f\u589e\u5f3a\u529f\u80fd<\/p>\n\n\n\n

      \n
    • \u4e24\u4e2a\u65b0\u7684\u5de5\u4f5c\u7c3f\u5de5\u5177\u680f\u9879: \u663e\u793a\/\u9690\u85cf\u6240\u6709\u8bed\u6cd5\u7a97\u53e3<\/strong> \u548c \u6e05\u9664\u6240\u6709\u8f93\u51fa<\/strong>\u3002<\/li>\n\n\n\n
    • “\u72b6\u6001\u680f” \u4e0a\u7684\u65b0\u6309\u94ae\uff0c\u7528\u4e8e\u5728\u7ecf\u5178 (\u8f93\u51fa\u548c\u8bed\u6cd5) \u65b9\u5f0f\u4e0e\u5de5\u4f5c\u7c3f\u65b9\u5f0f\u4e4b\u95f4\u8fdb\u884c\u5207\u6362\u3002<\/li>\n<\/ul>\n\n\n\n

      \u641c\u7d22\u589e\u5f3a\u529f\u80fd\u73b0\u5728\uff0c \u201c\u641c\u7d22\u201d\u529f\u80fd\u63d0\u4f9b\u4e86\u76f4\u63a5\u5728\u5de5\u5177\u680f\u5b57\u6bb5\u4e2d\u8f93\u5165\u8bcd\u6c47\u4ee5\u53ca\u5728\u4e0b\u62c9\u7a97\u683c\u4e2d\u67e5\u770b\u7ed3\u679c\u7684\u9009\u9879\u3002<\/p>\n\n\n\n

      <\/p>\n","protected":false},"excerpt":{"rendered":"

      IBM SPSS Statist...<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-1299","post","type-post","status-publish","format-standard","hentry","category-spss"],"_links":{"self":[{"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/posts\/1299","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/cabit.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1299"}],"version-history":[{"count":1,"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/posts\/1299\/revisions"}],"predecessor-version":[{"id":1300,"href":"https:\/\/cabit.top\/index.php?rest_route=\/wp\/v2\/posts\/1299\/revisions\/1300"}],"wp:attachment":[{"href":"https:\/\/cabit.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cabit.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cabit.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}