{"id":3077,"date":"2023-07-19T08:12:24","date_gmt":"2023-07-19T08:12:24","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%9c%aa%e6%89%be%e5%88%b0-evalpredvars-%e6%95%b0%e6%8d%ae%e7%8e%af%e5%a2%83%e5%af%b9%e8%b1%a1%e4%b8%ad%e7%9a%84%e9%94%99%e8%af%af\/"},"modified":"2023-07-19T08:12:24","modified_gmt":"2023-07-19T08:12:24","slug":"%e6%9c%aa%e6%89%be%e5%88%b0-evalpredvars-%e6%95%b0%e6%8d%ae%e7%8e%af%e5%a2%83%e5%af%b9%e8%b1%a1%e4%b8%ad%e7%9a%84%e9%94%99%e8%af%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%9c%aa%e6%89%be%e5%88%b0-evalpredvars-%e6%95%b0%e6%8d%ae%e7%8e%af%e5%a2%83%e5%af%b9%e8%b1%a1%e4%b8%ad%e7%9a%84%e9%94%99%e8%af%af\/","title":{"rendered":"\u5982\u4f55\u4fee\u590d\uff1aeval(predvars, data, env) \u4e2d\u7684\u9519\u8bef\uff1a\u672a\u627e\u5230\u5bf9\u8c61\u201cx\u201d"},"content":{"rendered":"

<\/p>\n

\n

\u5728 R \u4e2d\u60a8\u53ef\u80fd\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a<\/span><\/p>\n

 Error in eval(predvars, data, env): object 'x' not found \n<\/strong><\/pre>\n
\u5f53\u60a8\u5c1d\u8bd5\u4f7f\u7528 R \u4e2d\u7684\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u65b0\u6570\u636e\u5e27\u7684\u54cd\u5e94\u503c\uff0c\u4f46\u65b0\u6570\u636e\u5e27\u4e2d\u7684\u5217\u540d\u79f0\u4e0e\u60a8\u4ee5\u524d\u4f7f\u7528\u7684\u6570\u636e\u5e27\u4e2d\u7684\u5217\u540d\u79f0\u4e0d\u5339\u914d\u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u6b64\u9519\u8bef\u9002\u5408\u6a21\u578b\u3002 \u3002<\/span><\/p>\n
\u672c\u6559\u7a0b\u51c6\u786e\u89e3\u91ca\u4e86\u5982\u4f55\u4fee\u590d\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u9519\u8bef<\/strong><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u62df\u5408\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 #create data frame\ndata <- data. frame<\/span> (x=c(1, 2, 2, 3, 5, 6, 8, 9),\n                   y=c(7, 8, 8, 6, 9, 8, 12, 14))\n\n#fit linear regression model to data\n<\/span>model <- lm(y ~ x, data=data)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ x, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-2.1613 -0.7500 0.5000 0.9355 1.5161 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 5.5161 0.9830 5.611 0.00137 **\nx 0.7742 0.1858 4.167 0.00590 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.463 on 6 degrees of freedom\nMultiple R-squared: 0.7432, Adjusted R-squared: 0.7004 \nF-statistic: 17.37 on 1 and 6 DF, p-value: 0.005896\n<\/span><\/span><\/strong><\/span><\/pre>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u5c1d\u8bd5\u4f7f\u7528predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u65b0\u6570\u636e\u5e27\u7684\u54cd\u5e94\u503c\uff1a<\/span><\/p>\n
 #define new data frame\nnew_data <- data. frame<\/span> (x1=c(4, 5, 7, 8, 9))\n<\/span>\n#attempt to predict y values for new data frame\npredict(model, newdata=new_data)\n\nError in eval(predvars, data, env): object 'x' not found\n<\/span><\/span><\/strong><\/span><\/pre>\n
\u6211\u4eec\u6536\u5230\u9519\u8bef\uff0c\u56e0\u4e3a\u62df\u5408\u6a21\u578b\u65f6\u4f7f\u7528\u7684\u6570\u636e\u6846\u6709\u4e00\u4e2a\u540d\u4e3ax<\/strong>\u7684\u9884\u6d4b\u53d8\u91cf\uff0c\u4f46\u5728\u65b0\u6570\u636e\u6846\u4e2d\u6211\u4eec\u5c06\u9884\u6d4b\u53d8\u91cf\u547d\u540d\u4e3ax1<\/strong> \u3002<\/span><\/span><\/p>\n
\u7531\u4e8e\u8fd9\u4e9b\u540d\u79f0\u4e0d\u5339\u914d\uff0c\u6211\u4eec\u6536\u5230\u9519\u8bef\u3002<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d\u9519\u8bef<\/strong><\/span><\/h3>\n
\u4fee\u590d\u6b64\u9519\u8bef\u7684\u65b9\u6cd5\u662f\u7b80\u5355\u5730\u786e\u4fdd\u65b0\u6570\u636e\u6846\u4e2d\u7684\u9884\u6d4b\u53d8\u91cf\u5177\u6709\u76f8\u540c\u7684\u540d\u79f0\u3002<\/span><\/p>\n
\u56e0\u6b64\uff0c\u6211\u4eec\u5c06\u786e\u4fdd\u5728\u65b0\u6570\u636e\u5757\u4e2d\u547d\u540d\u9884\u6d4b\u53d8\u91cfx<\/strong> \uff1a<\/span><\/p>\n
 #define new data frame\nnew_data <- data. frame<\/span> (x=c(4, 5, 7, 8, 9)) \n<\/span><\/span><\/strong><\/span><\/pre>\n
\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u4f7f\u7528predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u65b0\u6570\u636e\u5e27\u7684\u54cd\u5e94\u503c\uff1a<\/span><\/p>\n
 #predict y values for new data frame\n<\/span>predict(model, newdata=new_data)\n\n        1 2 3 4 5 \n 8.612903 9.387097 10.935484 11.709677 12.483871<\/span>\n<\/span><\/strong><\/span><\/pre>\n
\u7531\u4e8e\u5217\u540d\u5339\u914d\uff0c\u6211\u4eec\u80fd\u591f\u6210\u529f\u9884\u6d4b\u65b0\u6570\u636e\u6846\u7684 y \u503c\uff0c\u4e0d\u4f1a\u51fa\u73b0\u4efb\u4f55\u9519\u8bef\u3002<\/span><\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u89e3\u51b3 R \u4e2d\u7684\u5176\u4ed6\u5e38\u89c1\u9519\u8bef\uff1a<\/span><\/p>\n
 \u5982\u4f55\u5728 R \u4e2d\u4fee\u590d\uff1a\u540d\u79f0\u4e0e\u4ee5\u524d\u7684\u540d\u79f0\u4e0d\u5339\u914d<\/a>
\u5982\u4f55\u5728R\u4e2d\u4fee\u590d\uff1a\u8f83\u957f\u7269\u4f53\u7684\u957f\u5ea6\u4e0d\u662f\u8f83\u77ed\u7269\u4f53\u957f\u5ea6\u7684\u500d\u6570<\/a>
\u5982\u4f55\u5728 R \u4e2d\u4fee\u590d\uff1a\u5bf9\u6bd4\u53ea\u80fd\u5e94\u7528\u4e8e\u5177\u6709 2 \u4e2a\u6216\u66f4\u591a\u7ea7\u522b\u7684\u56e0\u5b50<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728 R \u4e2d\u60a8\u53ef\u80fd\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a Error in eval(predvars, data, env): obj […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3077","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4fee\u590d\uff1aeval \u4e2d\u7684\u9519\u8bef\uff08predvars\u3001data\u3001env\uff09\uff1a\u672a\u627e\u5230\u5bf9\u8c61\u201cx\u201d - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4fee\u590d R \u4e2d\u7684\u4ee5\u4e0b\u9519\u8bef\uff1aeval(predvars, data, env) \u4e2d\u7684\u9519\u8bef\uff1a\u672a\u627e\u5230\u5bf9\u8c61\u201cx\u201d\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u672a\u627e\u5230-evalpredvars-\u6570\u636e\u73af\u5883\u5bf9\u8c61\u4e2d\u7684\u9519\u8bef\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4fee\u590d\uff1aeval \u4e2d\u7684\u9519\u8bef\uff08predvars\u3001data\u3001env\uff09\uff1a\u672a\u627e\u5230\u5bf9\u8c61\u201cx\u201d - 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